Modular radiation therapy apparatus and methods of selectively configuring the same

Abstract

The present disclosure provides a modular radiation therapy apparatus comprising a plurality of modules configured to sequentially interlock within one another in an annular arrangement, wherein the plurality of modules are individually extractable from the annular arrangement and at least a subset of the plurality of modules comprise computing elements configured to communicate with one another, the plurality of modules comprising a collimator module comprising a multi-leaf collimator (MLC) and at least one sensor, a linear accelerator (LINAC) module comprising at least one of a magnetron or a klystron, at least one imaging module comprising at least one of an image detector, an imaging source, a magnetic imaging system, a single photon emission computed tomography (SPECT) system, or a positron emission tomography (PET) system, a patient tracking module, a cooling module, and a vacuum module, and performing at least one of: monitoring a respective status of the plurality of modules via onboard sensors and computing elements to determine whether any module is in an error state and provisioning status information and module identifiers to a remote computing environment; or distributing computational workloads among the plurality of modules and communicating with a remote computing environment to offload processing tasks using federated processing operations.

Description

MODULAR RADIATION THERAPY APPARATUS AND METHODS OFSELECTIVELY CONFIGURING THE SAMECROSS REFERENCE TO RELATED APPLICATIONS

[0001] This claims the benefit of and priority to U.S. Application No. 63 / 700,961, filed September 30, 2024, entitled "MODULAR RADIATION THERAPY APPARATUS AND METHODS OF SELECTIVELY CONFIGURING THE SAME," the disclosure of which is incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] This application generally relates to apparatuses for applying radiation therapy to subjects.BACKGROUND

[0003] Generally, radiation therapy apparatuses are highly complex and costly systems that are difficult to transport and deploy in low resource environments. For example, a historical radiation therapy apparatus may comprise hundreds of unique elements that are integrated into a small number of major system units, each of which comprise a substantial spatial footprint. As a result, typical radiation therapy apparatuses require specialized transportation infrastructure and facilities. Further, repairing and servicing such apparatuses typically requires highly skilled onsite technicians to disassemble the major system units, thereby increasing operation complexity, resource costs, and time costs. Additionally, in such approaches, updates and improvements to the radiation therapy apparatuses may also require sophisticated on-site labor and major disassembly operations.

[0004] For these reasons, among others, historical radiation therapy apparatuses demonstrate drawbacks in maintenance complexity and transportability. Thus, previous approaches have not yet solved the challenges of providing resilient radiation therapy devices that are efficiently serviceable.BRIEF SUMMARY

[0005] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] In general, embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, and / or the like that are configured to provide modular radiation therapy systems with individually extractable modules and federated processing capabilities. For example, certain embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, and / or the like that comprise a plurality of modules configured to sequentially interlock within one another in an annular arrangement, where the plurality of modules are individually extractable from the annular arrangement and include computing elements configured to communicate with one another and with remote computing environments. Further, the methods, apparatuses, systems, computing devices, and / or the like may be configured to include various specialized modules such as collimator modules with multi-leaf collimators, linear accelerator modules with magnetrons or klystrons, imaging modules with various detection systems, patient tracking modules, cooling modules, and vacuum modules that collectively provide comprehensive radiation therapy capabilities. By doing so, the methods, apparatuses, systems, computing devices, and / or the like enable healthcare facilities to deploy transportable radiation therapy systems that demonstrate improved maintenance efficiency, reduced downtime, and enhanced operational flexibility through modular architecture and distributed processing capabilities. In this manner, the methods, apparatuses, systems, computing devices, and / or the like may enhance treatment accessibility, system reliability, and maintenance efficiency across various deployment environments including low resource settings.

[0007] Radiation therapy deployment has become increasingly important as healthcare systems seek to expand treatment accessibility while managing complex maintenance requirements and operational costs, presenting challenges in efficiently deploying and maintaining sophisticated medical equipment across diverse geographic and resource environments. Conventional radiation therapy systems often rely on monolithic architectures requiring specialized transportation infrastructure, dedicated facilities, and highly skilled on-site technicians for maintenance operations, leading to significant deployment barriers, extendeddowntime during servicing, and substantial operational costs. Implementing effective modular radiation therapy platforms presents technical challenges, including maintaining treatment accuracy across distributed components, ensuring reliable inter-module communication, and providing fault-tolerant operation during component maintenance. As healthcare delivery requirements evolve, there is a growing demand for advanced radiation therapy systems that provide modular deployment capabilities, enable remote monitoring and maintenance coordination, and offer flexible architectures for distributed processing and collaborative operation. Such systems have the potential to enhance treatment accessibility, improve operational efficiency, and contribute to overall healthcare delivery optimization, representing an area where ongoing technological development addresses current limitations and provides more sophisticated tools for expanding radiation therapy availability.

[0008] In accordance with one aspect of the present disclosure, a radiation therapy apparatus is provided. In some embodiments, the apparatus comprises a plurality of modules configured to sequentially interlock within one another in an annular arrangement, wherein the plurality of modules are individually extractable from the annular arrangement and at least a subset of the plurality of modules comprise computing elements configured to communicate with one another; and the plurality of modules comprising a collimator module comprising a multi-leaf collimator and at least one sensor, the at least one sensor comprising at least one of a temperature sensor, voltage sensor, or network identification sensor; a linear accelerator module comprising at least one of a magnetron or a klystron; at least one imaging module, a respective imaging module comprising at least one of an image detector, an imaging source, a magnetic imaging system, a single photon emission computed tomography system, or a positron emission tomography system; a patient tracking module; a cooling module; and a vacuum module.

[0009] In some embodiments, a connection between respective modules comprises a oneway interlock configured to secure the module against removal in a first direction and enable removal of the module in a second direction that is opposite the first direction. In some embodiments, a respective module comprises a first surface and a second surface located opposite the second surface, wherein the first surface comprises a plurality of protrusions configured to mate with a plurality of voids of a first adjacent module and the second surface comprises a plurality of voids configured to receive a plurality of protrusions of a second adjacent module. In some embodiments, the apparatus further comprises at least one platecomprising a ring shape, wherein the plurality of modules are connected to the plate and the plate is configured to balance a weight of the annular arrangement. In some embodiments, the at least one plate comprises a first side and a second side opposite the first side, wherein a first grouping of the plurality of modules are connected to the first side and a second grouping of the plurality of modules are connected to the second side. In some embodiments, the first grouping comprises cooling module and the vacuum module and the second grouping comprises the collimator module, the linear accelerator module, the at least one imaging module, and the at least one patient tracking module.

[0010] In some embodiments, the at least one plate comprises a first plate and a second plate, wherein the plurality of modules are connected to the first plate on a first side of the annular arrangement and the second plate on a second side of the annular arrangement. In some embodiments, the plate comprises carbon fiber. In some embodiments, a respective module comprises at least one magnetic shielding augment. In some embodiments, a respective module comprise a near-field communication circuit configured for uniquely identifying and authenticating the module. In some embodiments, at least two or more modules of the plurality of modules comprise redundant computing elements. In some embodiments, at least one of the plurality of modules comprises at least one computing device configured to monitor a respective status of one or more of the plurality of modules, determine that a module is in an error state based at least in part on the monitored status, and provision to a remote computing environment the status of and a unique identifier for the module.

[0011] In accordance with another aspect of the present disclosure, provided is a method for maintaining a modular radiation therapy apparatus comprising a plurality of individually extractable modules in an annular arrangement. In some embodiments, the method comprises monitoring a respective status of the plurality of modules via a remote computing environment; and performing, via the remote computing environment, at least one of the following based at least in part on the statuses: determining to replace at least one of the plurality of modules based at least in part on the respective status; and initiating a software update or firmware update pursuant to at least one of the plurality of modules based at least in part on the respective status.

[0012] In some embodiments, the method further comprises replacing at least one of the plurality of modules without removing any of a remaining subset of the plurality of modules from the annular arrangement. In some embodiments, the method further comprises receiving, atthe remote computing environment, a plurality of identifiers; and identifying the plurality of modules based at least in part on the plurality of identifiers.

[0013] In accordance with yet another aspect of the present disclosure, a modular radiation therapy apparatus is provided. In some embodiments, the apparatus comprises a plurality of modules configured to sequentially interlock within one another in an annular arrangement, wherein the plurality of modules are individually extractable from the annular arrangement and at least a subset of the plurality of modules comprise computing elements configured to communicate with one another to distribute computational workloads among the plurality of modules and communicate with a remote computing environment to offload processing tasks.

[0014] In some embodiments, the computing elements are configure to perform at least one federated processing operation, wherein the at least one federated processing operation comprises dynamic load balancing of computational tasks among the computing elements of different modules based on real-time system performance metrics and resource availability. In some embodiments, at least a subset of the computing elements comprise at least one machine learning model configured to analyze operational data from the plurality of modules to predict maintenance requirements and detect anomalies. In some embodiments, the at least one machine learning model is configured to generate maintenance predictions for at least a subset of the plurality of modules based at least in part on sensor data. In some embodiments, the computing elements are configured to participate in federated learning operations with other radiation therapy apparatuses.

[0015] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.BRIEF DESCRIPTION OF THE FIGURES

[0016] Having thus described the embodiments of the disclosure in general terms, reference now will be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

[0017] FIG. 1 shows a diagram of an example radiation therapy apparatus in accordance with some embodiments of the present disclosure;

[0018] FIG. 2 shows a diagram of an example radiation therapy apparatus in accordance with some embodiments of the present disclosure;

[0019] FIG. 3 illustrates a block diagram of an example apparatus that may be specially configured in accordance with at least some example embodiments of the present disclosure;

[0020] FIG. 4 depicts a flowchart diagram of an example process for maintenance, upgrades, and replacements of modules in accordance with at least some embodiments of the present disclosure;

[0021] FIG. 5 depicts a flowchart diagram of an example process for federated processing operations in accordance with at least some embodiments of the present disclosure;

[0022] FIG. 6 depicts a flowchart diagram of an example process for remote monitoring and maintenance coordination in accordance with at least some embodiments of the present disclosure; and

[0023] FIG. 7 depicts a flowchart diagram of an example process for module authentication and tracking in accordance with at least some embodiments of the present disclosure.DESCRIPTION

[0024] In general, various embodiments of the present disclosure provide modular radiation therapy apparatuses. Historical approaches to providing radiation therapy typically require bulky, mechanically complex systems that are difficult transport and deploy in low resource environments. For example, shipment of a typical radiation therapy apparatus may require specialized vehicles, remodeling of infrastructure, and labor of high skill technicians. As a result, such apparatuses are limited to installation in dedicated facilities, large hospitals, and other high resource cost environments. Further, typical radiation therapy apparatuses include highly interconnected and centralized systems such that installation, maintenance, and upgrading operations incur significant time and resource costs due to complexities of disassembling and reassembling the apparatuses. Additionally, such apparatuses may experience substantial downtime during maintenance and upgrading operations due to the extensive amalgamation of the various systems comprising the apparatus.

[0025] To overcome these challenges, and others, the present disclosure provides an improved radiation therapy apparatus comprising a modular physical architecture and federated processing means. In various embodiments, the radiation therapy apparatus includes a pluralityof modules configured to carry out various functionality associated with providing radiation therapy to a subject and monitoring the health of the apparatus. For example, the physical architecture may include a plurality of modules that are sequentially connected to each other in an annular arrangement. In some embodiments, respective modules are individually and independently removable from the radiation therapy apparatus. In this manner, the radiation therapy apparatus may be disassembled into discrete units for efficient shipping. Further, individual modules may be replaced, serviced, evaluated, upgraded, and / or the like without impacting a remaining subset of installed modules. For example, in contrast to the onsite maintenance requirements of typical approaches, a malfunctioning module may be extracted from the radiation therapy apparatus and shipped for servicing or replacement. In some embodiments, the modules include redundant systems, components, processing means, and / or the like such that one or more modules may be removed or disabled without causing downtime to the overall apparatus. Additionally, the modules may include data communication means that enable federation of computing workloads or pooling of processing resources. These advantages, and others, shall be made further apparent in the proceeding description of example embodiments of the holding system and the illustrations thereof provided in the accompanying figures.Example Radiation Therapy Apparatus

[0026] FIG 1. shows a diagram of an example radiation therapy apparatus 100 (“apparatus 100”). In various embodiments, the apparatus 100 comprises a plurality of modules 103 configured to carry out functionality for providing radiation therapy to a subject, monitoring one or more statuses of the apparatus 100, and enabling maintenance and updating of the apparatus 100. In some embodiments, the apparatus 100 includes 4, 6, 12, 24, or any other suitable number of modules 103. In various embodiments, the plurality of modules 103 are interconnected to one another in an annular arrangement 102 (see also FIG. 2). For example, a first module 103’ may be connected to a second module 103” via a one-way interlock 107. In some embodiments, the one-way interlock 107 comprises an engagement between one or more protrusions of a first module and one or more voids of an adjacent module. In some embodiments, a respective module 103 includes a first side 105 and a second side 106 opposite the first side 105. In some embodiments, the module 103 comprises a plurality of protrusions on the first side 105, theplurality of protrusions being configured to mate with a plurality of voids of a first adjacent module. In some embodiments, the module 103 comprises a plurality of voids of the second side 106, the plurality of voids being configured to mate with a plurality of protrusions of a second adjacent module.

[0027] In some embodiments, at least a subset of the modules 103 are further connected to one another electronically via wired means, such as one or more canbus connectors.Additionally, or alternatively, in some embodiments, at least a subset of the modules 103 are configured to communicate with one another via wireless means, such as Bluetooth, Zigbee, Wireless Fidelity (WiFi), radio frequency identification (RFID), and / or the like. For example, modules 103 may include embedded RFID units configured to collect and transmit data, perform module recognition and authentication procedures, and / or the like.

[0028] In some embodiments, one or more modules 103 are Internet of Things (loT) capable such that the module may communicate with one another and other computing systems external to the apparatus 100 (e.g., patient monitoring systems, displays, cameras, and / or the like). In various embodiments, the loT capabilities enable the modules 103 to participate in a networked ecosystem of medical devices and systems, thereby facilitating comprehensive patient care coordination and apparatus monitoring. For example, an loT-enabled imaging module may automatically transmit imaging data to a hospital's picture archiving and communication system (PACS) or electronic health record (EHR) system for integration with a patient's medical history. Similarly, loT-capable patient tracking modules may communicate with external patient monitoring devices to correlate physiological data (e.g., heart rate, respiratory rate, blood pressure, and / or the like) with treatment positioning and delivery parameters.

[0029] In some embodiments, the loT capabilities include support for standard medical device communication protocols, such as Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR), Digital Imaging and Communications in Medicine (DICOM), and / or Integrating the Healthcare Enterprise (IHE) profiles. In various embodiments, the loT-enabled modules 103 are configured to automatically discover and establish secure connections with compatible medical devices within a treatment facility's network infrastructure. For example, the modules 103 may utilize service discovery protocols such as multicast Domain Name System (mDNS), Universal Plug and Play (UPnP), and / or Zero Configuration Networking (Zeroconf) to identify and connect with external systems. In someembodiments, the loT capabilities enable real-time data streaming and event-driven communications between the apparatus 100 and external systems. For example, a cooling module may transmit temperature alerts to a facility's building management system to coordinate HVAC adjustments, or a vacuum module may communicate pressure readings to external monitoring dashboards for predictive maintenance scheduling. In various embodiments, the loT- enabled modules 103 support edge-to-cloud data pipelines, enabling local processing of timesensitive operations while offloading computationally intensive analytics to remote cloud services.

[0030] In some embodiments, a module 103 comprises one or more non-ferrous materials. For example, a module 103 may comprise carbon fiber, one or more non-ferrous metals or alloys, and / or the like. In some embodiments, as shown in FIG. 2, modules 103 are attached to one or more plates. The plate may comprise one or more non-ferrous materials, such as carbon fiber, non-ferrous metals, and / or the like. In this manner, the structure may enable implementation of magnetic imaging systems within the apparatus 100. For example, the nonferrous construction may enable implementation of a magnetic resonance imaging (MRI) system within the apparatus 100. In some embodiments, the module 103, plate, and / or the like include magnetic shielding. For example, the module 103, plate, and / or the like may be housed within and / or include internal elements housed within magnetic shielding. In some embodiments, the magnetic shielding includes one or more mu-metallic alloys (e.g., alloys comprising nickel, iron, copper, chromium, molybdenum, silicon, and / or the like), copper or copper-based alloys, brass, aluminum, and / or the like. In some embodiments, the magnetic shielding comprises one or more Faraday cages, and / or the like.

[0031] In some embodiments, a respective module 103 embodies a “petaf’-shaped quanta such that a plurality of modules may be oriented in the annular arrangement 102. In this manner, a respective module may encompass a predetermined sweep angle of the annular arrangement 102. For example, the apparatus 100 may include four modules 103 in an annular arrangement 102, where each module encompasses 90 degrees of the annular arrangement 102. As another example, the apparatus 100 comprises twelve modules 103, where a respective module encompasses 30 degrees of the annular arrangement 102. In still another example, the apparatus comprises twenty-four modules 103, where a respective module encompasses 15 degrees of the annular arrangement 102. In some embodiments, a total sweep angle of the annular arrangement102 is less than 360 degrees. For example, the apparatus 100 may include four modules 103, where a respective module encompasses 15 degrees of an annular arrangement comprising a total sweep angle of 60 degrees. In some embodiments, modules may encompass different values of sweep angle of an annular arrangement 102. For example, an annular arrangement 102 with a total sweep angle of 60 degrees may include first and second modules 103 encompassing respective sweep angles of 15 degrees and a third module 103 encompassing a sweep angle of 30 degrees.

[0032] In some embodiments, the annular arrangement 102 includes a plurality of sections. A respective section may include a sequence of modules 103 connected to one another. The sections may include the same or a different sweep angle of the annular arrangement 102. For example, the annular arrangement 102 may include four independent sections such that an individual section is extractable without removing a remaining subset of the sections. In such contexts, the four independent sections may each cover a sweep angle of 60 degrees and include four modules 103 that each cover 15 degrees. In another example, an apparatus 100 includes a first section and a second section, each section covering a sweep angle of 180 degrees and comprising a sequence of interlocking modules 103.

[0033] In various embodiments, the modular structure of the apparatus 100 enables efficient disassembly. In doing so, the apparatus 100 may demonstrate greater transportability as compared to other radiation therapy systems. For example, the apparatus 100 may be disassembled into a plurality of discrete units that may be shipped individually by conventional transportation infrastructure (e.g., rail, cargo ship, delivery truck, and / or the like). In this manner, the radiation therapy apparatus may be deployed in a greater variety of environments, including low resource environments lacking infrastructure to transport and support conventional radiation therapy systems.

[0034] In some embodiments, the modularity increases the efficiency and reduces the complexity of replacing or servicing elements of the apparatus 100. For example, a module 103 may be disconnected from other modules and removed from the annular arrangement 102 (e.g., for servicing, retrofitting, replacement purposes, and / or the like) without requiring removal of any of the other modules. In this manner, the time and resource efficiency of assembling, disassembling, and servicing the apparatus 100 may be increased as compared to other approaches. Further, the connections between the modules (and potentially other elements, suchas one or more plates) may comprise conventional fasteners, which may reduce the requisite skill level required for an operator to extract modules. For example, an operator may swap out modules via the use of a jig and standard tool sets, which may be readily available in low resource environments as compared to specialized, bespoke tools utilized to service other radiation therapy systems.

[0035] In some embodiments, the apparatus 100 comprises an outer diameter 109 of 1.5-2.5 meters (m). For example, the outer diameter 109 may be 2.1 m. In some embodiments, the apparatus 100 comprises a bore 111 configured to receive a portion of a subject. In some embodiments, the bore 111 comprises a diameter 113 of 0.5-1.5 m. For example, the diameter 113 may be 1.1 m. As another example, the diameter 113 may be at least 1.0 m (e.g., a radius of the bore 111 may be at least 0.5 m).

[0036] In various embodiments, the modules 103 include onboard edge computing capabilities to decrease the computational workload of performing imaging in computing environments external to the apparatus 100. In some embodiments, the modules 103 share processing workloads with one another and / or perform computing tasks redundantly or in parallel to improve computing efficiency and stability. In some embodiments, respective sections (“slices”) of modules 103 in the annular arrangement 102 include computing devices configured to perform redundant computing processes relative to other sections. For example, a first section of modules 103 may include a first image processing unit and a second section of modules may include a second image processing unit. In this manner, the respective processing units may be upgraded, serviced, or replaced in an alternating sequence without causing downtime to the apparatus 100. As a result, the apparatus 100 may demonstrate greater uptime, efficiency, and resiliency in comparison to other approaches.

[0037] In some embodiments, the modules 103 include a linear accelerator (LINAC) module comprising a magnetron, klystron, and / or the like. In various embodiments, the LINAC module serves as the primary radiation source for the apparatus 100, generating high-energy photon beams for therapeutic applications. In some embodiments, the LINAC module is configured to produce photon beam according to a predefined energy range or value. The selection of beam energy may be optimized based on the depth and characteristics of the target treatment area within a subject. In some embodiments, the magnetron comprises a high-power microwave generator configured to produce radiofrequency (RF) energy. The magnetron may be capable ofgenerating peak power outputs ranging with micro-, nano-, or pico- pulse durations. In various embodiments, the magnetron includes integrated cooling systems to manage the substantial heat generation during operation, and may incorporate temperature monitoring and control systems to maintain optimal operating conditions and prevent thermal damage. In some embodiments, the klystron comprises an alternative RF power source that may provide enhanced stability and control compared to magnetron-based systems. The klystron may be configured to generate RF power at similar frequencies to the magnetron but with improved phase stability and amplitude control. In various embodiments, the klystron-based LINAC module enables more precise dose rate control and may support advanced treatment techniques such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). The klystron may include multiple cavities for RF amplification and may incorporate feedback control systems for maintaining consistent output characteristics.

[0038] In some embodiments, the LINAC module includes an electron gun configured to generate and accelerate electrons that are subsequently converted to photons via a tungsten target. The electron gun may comprise a heated cathode, focusing electrodes, and acceleration structures designed to produce electron beams with energies corresponding to the desired photon output energies. In various embodiments, the electron gun includes beam steering and focusing systems to optimize electron beam characteristics and ensure consistent photon production. In some embodiments, the LINAC module comprises accelerating structures, such as standing wave or traveling wave linear accelerators, configured to accelerate electrons to the desired energies. The accelerating structures may include multiple accelerating cavities or sections, each designed to incrementally increase electron energy while maintaining beam quality and stability. In various embodiments, the accelerating structures are constructed from copper or other conductive materials and may include cooling channels to manage RF heating during operation. In some embodiments, the LINAC module includes a photon target assembly comprising a tungsten or tungsten alloy target configured to convert high-energy electrons to therapeutic photon beams via bremsstrahlung radiation. The target assembly may include cooling systems to manage the substantial heat load generated during electron-to-photon conversion, and may incorporate target positioning systems to optimize photon beam characteristics. In various embodiments, the target assembly includes flattening filters configured to produce uniformphoton fluence across the treatment field, though some embodiments may support flattening filter-free (FFF) operation for enhanced dose rate capabilities.

[0039] In some embodiments, the LINAC module comprises integrated beam monitoring and control systems configured to measure and regulate photon beam output in real-time. These systems may include ion chambers, photodiodes, or other radiation detectors positioned within the beam path to monitor dose rate, beam symmetry, and beam flatness. In various embodiments, the monitoring systems are configured to provide feedback to the RF generation and electron gun systems to maintain consistent beam characteristics throughout treatment delivery. In some embodiments, the LINAC module includes safety interlocks and emergency shutdown systems configured to immediately terminate radiation production in response to detected anomalies or safety violations. The safety systems may monitor parameters such as RF power levels, electron beam current, target temperature, cooling system status, and radiation leakage to ensure safe operation. In various embodiments, the safety systems are configured to communicate with other modules 103 and external safety systems to coordinate emergency responses and maintain comprehensive safety oversight.

[0040] In some embodiments, the LINAC module comprises modular sub-components that may be individually serviced, replaced, or upgraded without requiring complete module replacement. For example, the magnetron or klystron may be housed in a removable subassembly that may be extracted for maintenance or replacement while leaving other LINAC components in place. Similarly, the electron gun, accelerating structures, and target assembly may comprise separate serviceable units to facilitate efficient maintenance and reduce downtime.

[0041] In some embodiments, the modules 103 include a collimator module including a multi-leaf collimator (MLC). The collimator module may be configured to shape and direct the radiation beam produced by the LINAC module to conform to the specific geometry of a target treatment area within a subject. In various embodiments, the MLC comprises a plurality of individually controllable leaves, each leaf being movable independently to create precise apertures that match the cross-sectional shape of a tumor or other target tissue. For example, the MLC may include 40, 60, 80, 120, or more individual leaves arranged in opposing banks, where a respective leaf may be positioned with sub-millimeter precision to achieve highly conformal dose distributions. In some embodiments, the collimator module includes dynamic MLC capabilities, enabling real-time adjustment of leaf positions during radiation delivery toaccommodate patient motion, breathing patterns, and / or changes in target geometry. The dynamic MLC functionality may be coordinated with the patient tracking module to provide motion-compensated radiation therapy, thereby improving treatment accuracy and reducing radiation exposure to healthy tissues surrounding the target area. In various embodiments, the collimator module comprises tungsten, lead, or other high-density materials for the MLC leaves to provide effective radiation attenuation. The leaves may have a thickness of 5-10 centimeters or more to block high-energy photon beams. In some embodiments, the MLC leaves comprise rounded or focused edges to minimize radiation leakage between adjacent leaves and reduce penumbra effects at field edges. In some embodiments, the collimator module includes backup collimation systems, such as primary and secondary collimators, to provide additional beam shaping capabilities and safety redundancy. The primary collimator may define the maximum field size, while the secondary collimator works in conjunction with the MLC to provide fine beam shaping control. In various embodiments, the collimator module includes integrated dosimetry systems to monitor and verify the radiation field shape and intensity in real-time during treatment delivery.

[0042] In some embodiments, the modules 103 include one or more imaging modules. In some embodiments, an imaging module includes one or more imaging detectors, one or more imaging sources, one or more sensors, and / or the like. In some embodiments, an imaging module includes a magnetic imaging system. In some embodiments, an imaging module includes a single photon emission computed tomography (SPECT) system. In various embodiments, the imaging modules are configured to provide real-time visualization and monitoring capabilities during radiation therapy procedures. The imaging detectors may comprise flat-panel detectors, amorphous silicon detectors, complementary metal-oxide-semiconductor (CMOS) detectors, charge-coupled device (CCD) detectors, and / or scintillator-based detection systems. In some embodiments, the imaging detectors are configured to capture kilovoltage (kV) or megavoltage (MV) imaging data for patient positioning verification, treatment monitoring, and anatomical visualization. The flat-panel detectors may provide high-resolution imaging, enabling precise visualization of anatomical structures and treatment targets.

[0043] In some embodiments, the imaging sources comprise X-ray tubes, radioactive sources, or other radiation-generating devices configured to produce imaging beams at energies distinct from the therapeutic radiation beam. For example, the imaging sources may generate kVX-ray beams for soft tissue contrast imaging, while the therapeutic beam operates. In various embodiments, the imaging sources include collimation systems to shape and direct the imaging beam, and may incorporate beam fdtration to optimize image quality and reduce patient dose. The imaging sources may be positioned at various angles relative to the therapeutic beam to enable orthogonal or oblique imaging perspectives for enhanced anatomical visualization and treatment verification. In some embodiments, the imaging modules comprise cone-beam computed tomography (CBCT) systems configured to generate three-dimensional volumetric images of the patient anatomy immediately prior to or during treatment delivery. The CBCT system may include a rotating gantry or stationary multi-source configuration that acquires projection images from multiple angles around the patient. In various embodiments, the CBCT system is capable of generating images with spatial resolution and contrast resolution sufficient to distinguish between different tissue types. The CBCT imaging may be performed using dose levels significantly lower than diagnostic CT scans while maintaining adequate image quality for treatment guidance and verification.

[0044] In some embodiments, the imaging modules include portal imaging systems configured to capture images of the radiation beam as it exits the patient, thereby enabling verification of treatment delivery accuracy. The portal imaging system may comprise electronic portal imaging devices (EPIDs) positioned opposite the radiation source to capture transmitted beam images. In various embodiments, the portal imaging system is configured to compare acquired images with reference images or digitally reconstructed radiographs (DRRs) generated from treatment planning CT data to verify patient positioning and treatment field accuracy. In various embodiments, the imaging modules include real-time imaging capabilities that enable continuous monitoring of patient anatomy and target position during radiation delivery. The realtime imaging may utilize fluoroscopic imaging, cine imaging, or other dynamic imaging techniques to track respiratory motion, cardiac motion, and other physiological movements that may affect treatment accuracy. In some embodiments, the real-time imaging data is integrated with the patient tracking module and collimator module to enable motion-compensated radiation therapy, where treatment delivery is automatically adjusted based on detected anatomical motion.

[0045] In some embodiments, the imaging modules comprise dual-energy imaging systems configured to enhance tissue contrast and material discrimination. The dual-energy system mayacquire images at two different X-ray energies simultaneously or sequentially, enabling improved visualization of soft tissues, bone structures, and contrast agents. In various embodiments, the dual-energy imaging capability facilitates better target delineation and critical structure identification during treatment planning and delivery verification. In some embodiments, an imaging module includes a positron emission tomography (PET) system.

[0046] In some embodiments, the modules 103 include one or more patient tracking modules configured to track the position, condition, and / or the like of a subject within the apparatus 100. For example, via the patient tracking module, the apparatus 100 may determine whether a subject demonstrated excess movement or shifting prior to or during application of radiation therapy. In various embodiments, the patient tracking modules comprise multiple complementary tracking technologies to provide comprehensive monitoring of patient position and motion throughout the treatment process. The patient tracking capabilities may include optical tracking systems, electromagnetic tracking systems, radiofrequency (RF) tracking systems, surface imaging systems, and / or implantable marker tracking systems.

[0047] In some embodiments, the patient tracking module includes optical tracking systems comprising one or more cameras, infrared sensors, and / or laser-based positioning devices configured to monitor external patient anatomy and positioning markers. The optical tracking system may utilize stereoscopic camera arrays positioned at multiple locations around the annular arrangement 102 to provide three-dimensional tracking of patient position with submillimeter accuracy. In various embodiments, the optical tracking system is configured to detect and track reflective markers, active LED markers, or anatomical landmarks positioned on the patient's skin surface. The optical tracking may operate in real-time during treatment delivery to detect respiratory motion, cardiac motion, and involuntary patient movement that could affect treatment accuracy.

[0048] In some embodiments, the patient tracking module comprises electromagnetic tracking systems that utilize electromagnetic field generators and sensors to track the position of implanted or externally placed electromagnetic transponders. The electromagnetic tracking system may provide continuous three-dimensional position monitoring with high temporal resolution, enabling detection of both periodic physiological motion (e.g., breathing, heartbeat) and aperiodic patient movement. In various embodiments, the electromagnetic transponders comprise small, biocompatible devices that may be temporarily or permanently implanted nearthe treatment target to provide direct tumor tracking capabilities. The electromagnetic tracking system may be configured to operate in conjunction with the magnetic shielding elements of the modules 103 to prevent interference with other apparatus systems.

[0049] In some embodiments, the patient tracking module includes surface imaging systems configured to capture and analyze the three-dimensional surface topology of the patient in realtime. The surface imaging system may utilize structured light projection, laser scanning, or time- of-flight imaging techniques to generate detailed surface maps of the patient anatomy. In various embodiments, the surface imaging system is configured to compare real-time surface data with reference surface models acquired during treatment planning to detect changes in patient positioning, breathing patterns, and anatomical deformation. The surface imaging may provide non-contact monitoring capabilities that do not require placement of markers or devices on the patient. In some embodiments, the patient tracking module comprises radiofrequency tracking systems that monitor the position of RF transponders or beacons placed on or within the patient. The RF tracking system may utilize multiple RF receivers positioned around the annular arrangement 102 to triangulate the position of RF transmitters with high accuracy. In various embodiments, the RF tracking system is configured to operate at frequencies that minimize interference with other electronic systems within the apparatus 100 and provide reliable tracking performance in the presence of patient motion and physiological changes.

[0050] In various embodiments, the patient tracking module includes motion prediction algorithms configured to anticipate patient movement based on historical motion patterns and physiological signals. The motion prediction capabilities may utilize machine learning models trained on patient-specific motion data to forecast respiratory motion, cardiac motion, and other predictable physiological movements. In some embodiments, the motion prediction algorithms are configured to provide motion forecasts with lead times sufficient to enable proactive adjustment of treatment delivery parameters, such as beam gating, dynamic MLC positioning, and couch positioning corrections. In some embodiments, the patient tracking module comprises integrated physiological monitoring capabilities configured to correlate patient motion with physiological parameters such as respiratory rate, heart rate, blood pressure, and oxygen saturation. The physiological monitoring may provide additional context for interpreting patient motion data and may enable detection of patient distress or medical emergencies during treatment delivery. In various embodiments, the physiological monitoring systems are configuredto communicate with external patient monitoring devices and hospital information systems to provide comprehensive patient status information.

[0051] In some embodiments, the patient tracking module includes automated patient positioning systems configured to make real-time corrections to patient position based on tracking data. The automated positioning systems may comprise robotic treatment couches, patient immobilization devices, and / or other mechanical systems capable of making precise positional adjustments during treatment delivery. In various embodiments, the automated positioning systems are configured to operate in coordination with the collimator module and LINAC module to maintain optimal treatment geometry despite patient motion. In various embodiments, the patient tracking module comprises safety monitoring systems configured to immediately halt treatment delivery in response to excessive patient motion or positioning errors that exceed predefined safety thresholds. The safety monitoring systems may include redundant motion detection capabilities and fail-safe mechanisms to ensure patient safety during treatment delivery. In some embodiments, the safety monitoring systems are configured to provide graduated responses to different levels of patient motion, ranging from temporary beam holds for minor movements to complete treatment termination for significant positioning errors. In some embodiments, the patient tracking module includes data logging and analysis capabilities configured to record and analyze patient motion patterns throughout the treatment course. The motion data logging may provide valuable information for treatment plan optimization, patientspecific motion modeling, and quality assurance purposes. In various embodiments, the motion analysis capabilities include statistical analysis tools, motion pattern recognition algorithms, and reporting functions that enable clinical staff to evaluate treatment delivery accuracy and patient compliance.

[0052] In some embodiments, the modules 103 include one or more cooling modules comprising cooling systems configured to regulate internal temperatures of the apparatus 100. In various embodiments, the cooling modules are essential for maintaining optimal operating conditions for heat-generating components such as the LINAC module, collimator module, imaging modules, and computing devices 300. The cooling systems may comprise liquid cooling circuits, air cooling systems, thermoelectric cooling devices, and / or hybrid cooling configurations designed to address the diverse thermal management requirements of different module types. In some embodiments, the cooling module includes liquid cooling systemscomprising closed-loop coolant circuits with pumps, heat exchangers, radiators, and temperature control valves. The liquid cooling system may utilize water, glycol-based coolants, or specialized dielectric fluids depending on the specific cooling requirements and electrical isolation needs of the components being cooled. In various embodiments, the liquid cooling circuits are configured to provide targeted cooling to high-heat-generation components such as magnetrons, klystrons, RF amplifiers, and high-power computing processors. The liquid cooling system may include multiple independent cooling loops to provide redundancy and enable selective cooling of different module sections.

[0053] In some embodiments, the cooling module comprises air cooling systems including fans, blowers, heat sinks, and ducted airflow management systems. The air cooling systems may be configured to provide general ambient temperature control within the annular arrangement 102 and targeted cooling for components that do not require liquid cooling. In various embodiments, the air cooling systems include variable-speed fans and intelligent airflow control to optimize cooling efficiency while minimizing acoustic noise and power consumption. The air cooling systems may incorporate filtration systems to prevent dust and particulate contamination of sensitive components. In various embodiments, the cooling module includes thermoelectric cooling devices, such as Peltier coolers, configured to provide precise temperature control for temperature-sensitive components such as imaging detectors, sensors, and precision electronics. The thermoelectric cooling devices may enable both cooling and heating capabilities to maintain components within narrow temperature ranges regardless of ambient conditions. In some embodiments, the thermoelectric cooling systems are integrated with temperature feedback control systems to provide automated temperature regulation.

[0054] In some embodiments, the cooling module comprises heat recovery systems configured to capture and redistribute waste heat generated by high-power components. The heat recovery systems may include heat exchangers that transfer waste heat from components such as the LINAC module to other areas of the apparatus 100 that may benefit from supplemental heating, such as patient comfort systems or component preheating systems. In various embodiments, the heat recovery systems improve overall energy efficiency of the apparatus 100 by reducing both cooling loads and auxiliary heating requirements. In various embodiments, the cooling module includes distributed temperature monitoring systems comprising multiple temperature sensors positioned throughout the apparatus 100 to monitor componenttemperatures, coolant temperatures, and ambient conditions. The temperature monitoring systems may include thermocouples, resistance temperature detectors (RTDs), thermistors, and / or infrared temperature sensors to provide comprehensive thermal monitoring capabilities. In some embodiments, the temperature monitoring systems are configured to provide real-time temperature data to the computing devices 300 for thermal management control and predictive maintenance purposes.

[0055] In some embodiments, the cooling module comprises intelligent thermal management systems that utilize machine learning algorithms and predictive analytics to optimize cooling performance based on operational patterns, environmental conditions, and component aging characteristics. The intelligent thermal management systems may automatically adjust cooling parameters such as pump speeds, fan speeds, valve positions, and thermoelectric cooling setpoints to maintain optimal temperatures while minimizing energy consumption. In various embodiments, the intelligent systems are configured to predict thermal events and proactively adjust cooling parameters to prevent overheating conditions. In various embodiments, the cooling module includes emergency cooling systems and thermal protection mechanisms configured to provide rapid cooling response in the event of component overheating or cooling system failures. The emergency cooling systems may include backup cooling circuits, emergency shutdown procedures, and thermal isolation systems to protect critical components from thermal damage. In some embodiments, the emergency cooling systems are configured to communicate with other modules 103 and safety systems to coordinate emergency responses and prevent cascading thermal failures.

[0056] In some embodiments, the cooling module comprises modular cooling components that may be individually serviced, replaced, or upgraded without requiring shutdown of the entire cooling system. For example, individual cooling pumps, heat exchangers, or cooling fans may be configured as hot-swappable components that may be replaced during operation using redundant cooling capacity. In various embodiments, the modular cooling design enables efficient maintenance and reduces downtime associated with cooling system servicing. In various embodiments, the cooling module includes energy-efficient cooling technologies such as variable-speed drives, high-efficiency heat exchangers, and advanced thermal interface materials to minimize power consumption while maintaining effective cooling performance. The energyefficient cooling systems may incorporate waste heat recovery, free cooling capabilities whenambient conditions permit, and intelligent load balancing to optimize overall system efficiency. In some embodiments, the cooling module is configured to communicate with facility management systems to coordinate cooling operations with building HVAC systems and optimize overall energy usage.

[0057] In some embodiments, the modules 103 include one or more vacuum modules configured to apply negative pressurization to one or more elements of the apparatus 100 (e.g., LINAC modules, collimator modules, and / or the like) to generate and maintain vacuum conditions. In various embodiments, the vacuum modules maintain high-vacuum environments for optimal operation of electron beam generation and acceleration systems within the LINAC module. For example, the vacuum conditions may prevent electron beam scattering, reduce energy losses due to collisions with gas molecules, and maintain beam stability and focus throughout the acceleration process. In some embodiments, the vacuum module comprises multiple vacuum pumps configured in series or parallel arrangements to achieve and maintain ultra-high vacuum conditions.

[0058] In some embodiments, the vacuum module includes turbo-molecular pumps configured to provide high pumping speeds for light gases and achieve ultra-high vacuum conditions required for electron beam systems. The turbo-molecular pumps may be backed by roughing pumps, such as scroll pumps or rotary vane pumps, to provide initial evacuation and maintain backing pressure within the operating range of the turbo-molecular pumps. In various embodiments, the vacuum module comprises ion pumps configured to maintain ultra-high vacuum conditions with minimal vibration and contamination, which may be particularly beneficial for sensitive imaging and beam control systems. The ion pumps may provide chemically clean vacuum environments by capturing gas molecules through ionization and burial processes. In various embodiments, the vacuum module includes distributed vacuum monitoring systems comprising multiple vacuum gauges positioned throughout the vacuum system to monitor pressure levels in different sections of the apparatus 100. The vacuum monitoring systems may include Pirani gauges for rough vacuum measurements, ionization gauges for high and ultra-high vacuum measurements, and capacitance manometers for accurate pressure measurements across wide pressure ranges. In some embodiments, the vacuum monitoring systems are configured to provide real-time pressure data to the computing devices 300 for vacuum system control, safety monitoring, and predictive maintenance purposes.

[0059] In some embodiments, the vacuum module comprises automated vacuum control systems configured to maintain optimal vacuum conditions through intelligent pump control, valve sequencing, and leak detection capabilities. The automated control systems may include programmable logic controllers (PLCs) or embedded control systems that monitor vacuum levels and automatically adjust pump speeds, valve positions, and system configurations to maintain target vacuum conditions. In various embodiments, the automated systems are configured to perform controlled venting and pump-down sequences during maintenance operations and system startup procedures. In various embodiments, the vacuum module includes vacuum safety systems and interlocks configured to protect personnel and equipment from vacuum -related hazards. The safety systems may include pressure relief valves, emergency venting systems, and automatic shutdown procedures that activate in response to vacuum system failures or safety violations. In some embodiments, the safety systems are configured to coordinate with other modules 103 and facility safety systems to ensure comprehensive safety coverage during vacuum operations and maintenance activities.

[0060] In some embodiments, the vacuum module comprises leak detection systems configured to identify and locate vacuum leaks that could compromise system performance. The leak detection systems may include helium leak detectors, residual gas analyzers, and pressure rise rate testing capabilities to detect leaks with high sensitivity and accuracy. In various embodiments, the leak detection systems are configured to perform automated leak checking procedures and provide diagnostic information to maintenance personnel for efficient leak repair and system optimization. In various embodiments, the vacuum module includes modular vacuum components that may be individually serviced, replaced, or upgraded without requiring complete system shutdown. For example, individual vacuum pumps may be configured with isolation valves that enable pump replacement or maintenance while maintaining vacuum in other sections of the system. In some embodiments, the modular design includes redundant pumping capacity to enable continued operation during maintenance of individual vacuum components.

[0061] In some embodiments, the vacuum module comprises specialized vacuum chambers and beam tubes configured to house electron beam paths and other vacuum-sensitive components. The vacuum chambers may be constructed from stainless steel, aluminum, or other low-outgassing materials to minimize contamination and maintain stable vacuum conditions. In various embodiments, the vacuum chambers include multiple ports for instrumentation, pumpingconnections, and component access, with conflat flanges or other ultra-high vacuum compatible sealing systems. In various embodiments, the vacuum module includes bakeout systems configured to remove adsorbed gases and contaminants from vacuum chamber surfaces through controlled heating processes. The bakeout systems may comprise heating elements, temperature controllers, and thermal insulation systems designed to heat vacuum chambers to achieve ultraclean vacuum conditions. In some embodiments, the bakeout systems are configured to perform automated bakeout cycles with programmable temperature profiles and safety monitoring.

[0062] In various embodiments, the apparatus 100 includes one or more redundant modules configured to provide backup functionality and enhanced system reliability. For example, the apparatus 100 may include redundant imaging modules, patient tracking modules, cooling modules, vacuum modules, and / or the like. The redundant modules enable continued operation of the apparatus 100 even when individual modules require maintenance, experience failures, or are temporarily removed for servicing. In some embodiments, the redundant modules are configured to automatically assume operational responsibilities when primary modules are unavailable, thereby minimizing treatment interruptions and maintaining patient care continuity.

[0063] In some embodiments, the redundant modules are strategically distributed throughout the annular arrangement 102 to provide optimal coverage and load balancing. The redundant configuration may include active-passive arrangements where backup modules remain in standby mode until needed, or active-active arrangements where multiple modules operate simultaneously to share workloads and provide immediate failover capabilities. Additionally, the redundant modules may be configured with cross-compatibility features that enable different module types to provide backup functionality for one another, further enhancing the overall resilience and operational flexibility of the apparatus 100.

[0064] In some embodiments, a respective module 103 comprises one or more sensors configured to generate measurements by which health of the module may be monitored. In some embodiments, the sensor includes a temperature sensor, moisture sensor, rotation sensor (e.g., inertial measurement unit (IMU), and / or the like), acoustic sensor, pressure sensor, voltage sensor, network identification (ID) sensor, and / or the like. The network ID sensor may include radiofrequency identification (RFID) circuits, Bluetooth modules, near field communication (NFC) circuits, and / or the like. In some embodiments, the module 103 includes one or more quantum sensors. For example, the module 103 may include one or more sensors that detect oneor more quantum effects (e.g., quantum states, entanglement, interference and / or the like) to measure magnetic fields and other physical quantities. In some embodiments, the module 103 includes one or more sensors that comprise onboard data processing capabilities (e.g., edge computing sensors, and / or the like). For example, the module 103 may include a gamma camera that captures emitted radiation and generates, via onboard processing elements, SPECT projections and tomographic reconstructions.

[0065] In some embodiments, a respective module 103 comprises one or more sensors configured to generate measurements by which health of the module may be monitored. In some embodiments, the sensor includes a temperature sensor, moisture sensor, rotation sensor (e.g., inertial measurement unit (IMU), and / or the like), acoustic sensor, pressure sensor, voltage sensor, network identification (ID) sensor, and / or the like. The network ID sensor may include radiofrequency identification (RFID) circuits, Bluetooth modules, near field communication (NFC) circuits, and / or the like. In some embodiments, the module 103 includes one or more quantum sensors. For example, the module 103 may include one or more sensors that detect one or more quantum effects (e.g., quantum states, entanglement, interference and / or the like) to measure magnetic fields and other physical quantities. In some embodiments, the module 103 includes one or more sensors that comprise onboard data processing capabilities (e.g., edge computing sensors, and / or the like). For example, the module 103 may include a gamma camera that captures emitted radiation and generates, via onboard processing elements, SPECT projections and tomographic reconstructions.

[0066] In various embodiments, the temperature sensors comprise thermocouples, resistance temperature detectors (RTDs), thermistors, infrared temperature sensors, and / or fiber optic temperature sensors configured to monitor thermal conditions within the module 103 and detect potential overheating conditions that could indicate component degradation or failure. The temperature sensors may be strategically positioned at critical thermal monitoring points, such as near high-power electronics, cooling system interfaces, mechanical bearings, and heat-generating components. In some embodiments, the temperature sensors are configured to provide continuous temperature monitoring with high temporal resolution to enable rapid detection of thermal transients and gradual temperature drift that may indicate developing maintenance issues.

[0067] In some embodiments, the moisture sensors comprise capacitive humidity sensors, resistive humidity sensors, and / or dew point sensors configured to detect the presence of moisture or excessive humidity levels that could compromise electrical systems, cause corrosion, or indicate cooling system leaks. The moisture sensors may be positioned near potential moisture ingress points, such as seals, gaskets, cooling system connections, and ventilation interfaces. In various embodiments, the moisture sensors are configured to provide early warning of environmental conditions that could lead to component degradation or electrical failures, enabling proactive maintenance interventions.

[0068] In various embodiments, the rotation sensors comprise multi-axis IMUs, gyroscopes, accelerometers, and / or magnetometers configured to detect mechanical vibrations, structural movements, and orientation changes that may indicate mechanical wear, bearing failures, or structural integrity issues. The rotation sensors may be particularly valuable for monitoring rotating components such as cooling fans, vacuum pumps, and mechanical positioning systems within the modules 103. In some embodiments, the rotation sensors are configured to perform vibration analysis and frequency domain monitoring to identify characteristic failure signatures associated with specific mechanical components.

[0069] In some embodiments, the acoustic sensors comprise microphones, piezoelectric sensors, and / or ultrasonic transducers configured to monitor acoustic emissions that may indicate mechanical wear, electrical arcing, cooling system irregularities, or other operational anomalies. The acoustic sensors may be configured to perform frequency analysis to identify specific acoustic signatures associated with different types of component degradation or failure modes. In various embodiments, the acoustic sensors enable non-invasive monitoring of internal component conditions and may provide early warning of developing issues before they result in component failures or safety hazards.

[0070] In various embodiments, the pressure sensors comprise piezoresistive pressure sensors, capacitive pressure sensors, and / or strain gauge pressure sensors configured to monitor pneumatic systems, hydraulic systems, vacuum conditions, and mechanical stress levels within the module 103. The pressure sensors may be integrated with cooling systems, vacuum systems, and mechanical positioning systems to provide real-time monitoring of system performance and detect potential leaks, blockages, or mechanical failures. In some embodiments, the pressuresensors are configured to monitor differential pressures across filters, heat exchangers, and other system components to assess component condition and maintenance requirements.

[0071] In some embodiments, the voltage sensors comprise voltage dividers, isolation amplifiers, and / or digital multimeter circuits configured to monitor electrical power quality, supply voltage stability, and electrical system performance within the module 103. The voltage sensors may monitor both AC and DC voltage levels, voltage ripple, transient conditions, and power factor to assess electrical system health and detect potential electrical failures. In various embodiments, the voltage sensors are configured to provide electrical safety monitoring and may trigger protective actions in response to detected electrical anomalies such as overvoltage, undervoltage, or electrical fault conditions.

[0072] In various embodiments, the quantum sensors comprise nitrogen-vacancy (NV) center sensors, superconducting quantum interference devices (SQUIDs), atomic magnetometers, and / or quantum gravimeters configured to provide ultra-sensitive measurements of magnetic fields, gravitational fields, and other physical quantities with precision exceeding classical sensor capabilities. The quantum sensors may be particularly valuable for monitoring magnetic field uniformity and stability in modules 103 that include magnetic imaging systems or other magnetic field-sensitive components. In some embodiments, the quantum sensors are configured to detect minute changes in magnetic field conditions that could affect imaging quality or system performance, enabling precise calibration and optimization of magnetic systems. In some embodiments, the quantum sensors comprise quantum-enhanced accelerometers and gyroscopes that utilize quantum interference effects to achieve enhanced sensitivity for detecting mechanical vibrations, rotational movements, and gravitational variations. The quantum-enhanced inertial sensors may provide improved accuracy for patient positioning systems, mechanical stability monitoring, and structural health assessment of the apparatus 100. In various embodiments, the quantum sensors include quantum-enhanced pressure sensors and temperature sensors that utilize quantum effects to achieve measurement precision and stability exceeding classical sensor technologies.

[0073] In various embodiments, the edge computing sensors comprise embedded microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and / or application-specific integrated circuits (ASICs) configured to perform real-time data processing, signal conditioning, and preliminary analysis of sensor measurements. The edgecomputing capabilities enable the sensors to perform local data filtering, noise reduction, feature extraction, and anomaly detection without requiring transmission of raw sensor data to external computing systems. In some embodiments, the edge computing sensors are configured to implement machine learning algorithms, statistical analysis routines, and predictive maintenance algorithms to identify patterns and trends in sensor data that may indicate developing maintenance issues or performance degradation. In some embodiments, the edge computing sensors comprise distributed processing networks where multiple sensors within a module 103 or across multiple modules 103 collaborate to perform coordinated sensing and analysis tasks. The distributed sensor networks may implement consensus algorithms, data fusion techniques, and collaborative filtering to improve measurement accuracy and reliability. In various embodiments, the edge computing sensors are configured to adapt their processing algorithms and measurement parameters based on operational conditions, environmental factors, and historical performance data to optimize sensing performance and reduce false alarms.

[0074] In various embodiments, the gamma camera comprises scintillation detectors, photomultiplier tubes, and / or solid-state photodetectors configured to capture gamma ray emissions from radiopharmaceuticals administered to patients during nuclear medicine procedures. The gamma camera may include collimators to define the field of view and improve spatial resolution, and may incorporate multiple detector heads to enable simultaneous imaging from different angles. In some embodiments, the gamma camera includes onboard image reconstruction processors configured to perform filtered back-projection, iterative reconstruction, and / or advanced reconstruction algorithms to generate SPECT images in real-time during data acquisition. In some embodiments, the gamma camera comprises cadmium zinc telluride (CZT) detectors, cesium iodide (CsI) scintillators, and / or other advanced detector materials configured to provide improved energy resolution, spatial resolution, and detection efficiency compared to conventional gamma cameras. The advanced detector materials may enable reduced imaging times, lower patient radiation doses, and improved image quality for SPECT imaging applications. In various embodiments, the gamma camera includes adaptive collimation systems, electronic collimation capabilities, and / or multi-pinhole collimators to optimize imaging performance for different clinical applications and patient anatomies.

[0075] In some embodiments, the module 103 includes onboard memory and processing capabilities embodied as one or more computing devices 300. In some embodiments, theonboard processing means include one or more central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), custom silicon boards, and / or the like. In various embodiments, the processing means enable onboard computing functionality including real-time image analysis, sensor analysis for apparatus health monitoring, real-time patient tracking, and / or the like.

[0076] In some embodiments, the processing means enable federated performance of functionality for carrying out radiation therapy procedures. For example, a remote computing environment (e.g., cloud system, and / or the like) may delegate radiation therapy processes to different modules 103 or sections thereof. In some embodiments, the onboard computing capabilities of the modules 103 enable multiprocessor calculations, which may improve computing performance. Additionally, in some embodiments, the onboard computing capabilities enable a respective module 103 to perform independent self-diagnostic checks, functional upgrades, and / or the like. In various embodiments, the federated processing architecture enables dynamic load balancing and resource allocation across the plurality of modules 103 based on real-time computational demands and system availability. For example, computationally intensive tasks such as real-time image reconstruction, dose calculation algorithms, and motion prediction modeling may be automatically distributed among available computing resources within the apparatus 100 and external computing environments. The federated system may utilize distributed computing protocols, such as message passing interface (MPI), Apache Spark, or custom load balancing algorithms, to coordinate task distribution and ensure optimal utilization of available processing capacity.

[0077] In some embodiments, the federated processing capabilities include fault-tolerant computing mechanisms that enable continued operation even when individual modules 103 or computing elements experience failures or are temporarily unavailable for maintenance. The fault-tolerant mechanisms may include checkpoint and restart capabilities, redundant computation verification, and automatic failover procedures that seamlessly transfer computational workloads from failed components to available backup systems. In various embodiments, the federated architecture supports heterogeneous computing environments where different modules 103 may comprise different types of processing hardware, such as CPUs, GPUs, FPGAs, and specialized medical imaging processors, with the system automatically optimizing task assignment based on the computational characteristics and hardware capabilitiesof each module. In various embodiments, the multiprocessor calculations enabled by the onboard computing capabilities include parallel processing of imaging data, simultaneous execution of multiple treatment planning algorithms, and concurrent monitoring of multiple patient safety parameters. The multiprocessor architecture may support both symmetric multiprocessing (SMP) and asymmetric multiprocessing (AMP) configurations, enabling optimization of computational resources for different types of radiation therapy tasks. For example, image processing tasks may be distributed across multiple GPU cores for parallel execution, while control system tasks may be assigned to dedicated real-time processing cores to ensure deterministic response times.

[0078] In some embodiments, the multiprocessor capabilities include support for vector processing, single instruction multiple data (SIMD) operations, and specialized mathematical coprocessors optimized for radiation therapy calculations such as dose distribution modeling, beam path optimization, and Monte Carlo radiation transport simulations. The multiprocessor systems may incorporate cache coherency protocols, shared memory architectures, and highspeed interconnects to enable efficient data sharing and communication between processing elements within and across modules 103. In various embodiments, the independent selfdiagnostic capabilities of the modules 103 include automated hardware testing routines, software integrity verification, calibration validation procedures, and performance benchmarking assessments. The self-diagnostic systems may execute built-in test (BIT) procedures, power-on self-test (POST) sequences, and continuous background monitoring to detect potential hardware failures, software corruption, or performance degradation before they impact treatment delivery. In some embodiments, the self-diagnostic capabilities include machine learning-based anomaly detection algorithms that learn normal operational patterns and identify deviations that may indicate developing maintenance issues or component failures.

[0079] In some embodiments, the functional upgrade capabilities enable over-the-air (OTA) updates to module firmware, software patches, algorithm improvements, and feature enhancements without requiring physical access to the apparatus 100 or interruption of clinical operations. The upgrade system may include secure update mechanisms, digital signature verification, rollback capabilities, and staged deployment procedures to ensure safe and reliable implementation of updates. In various embodiments, the upgrade capabilities support modular software architecture where individual functional components may be updated independently,enabling targeted improvements to specific capabilities such as imaging algorithms, dose calculation methods, or patient tracking accuracy without affecting other system functions.

[0080] In some embodiments, the onboard processing means includes one or more machine learning models, artificial intelligence (Al) engines, and / or the like configured to monitor functionality and safety of the apparatus 100. In various embodiments, the machine learning models comprise supervised learning algorithms, unsupervised learning algorithms, reinforcement learning algorithms, and / or deep learning neural networks trained to recognize patterns in operational data that may indicate normal operation, developing maintenance issues, or potential safety hazards. The machine learning models may be trained using historical operational data from the apparatus 100, similar radiation therapy systems, and / or synthetic data generated through simulation of various operational scenarios and failure modes.

[0081] In some embodiments, the machine learning models include predictive maintenance algorithms configured to analyze sensor data, operational parameters, and usage patterns to predict when components or modules 103 may require maintenance, replacement, or recalibration before actual failures occur. The predictive maintenance models may utilize time series analysis, anomaly detection algorithms, and degradation modeling to identify subtle changes in system performance that precede component failures. In various embodiments, the predictive maintenance capabilities enable proactive scheduling of maintenance activities during planned downtime periods, thereby reducing unscheduled treatment interruptions and improving overall system availability.

[0082] In various embodiments, the Al engines comprise expert systems, decision trees, fuzzy logic controllers, and / or neural network architectures configured to make real-time decisions regarding system operation, safety protocols, and treatment delivery parameters. The Al engines may incorporate medical physics knowledge, radiation safety protocols, and clinical best practices to provide intelligent automation of routine tasks while maintaining appropriate human oversight for critical decisions. In some embodiments, the Al engines are configured to adapt their decision-making algorithms based on accumulated operational experience, patient outcomes, and evolving clinical protocols.

[0083] In some embodiments, the machine learning models include computer vision algorithms configured to analyze imaging data from the imaging modules in real-time to detect anatomical changes, patient positioning errors, and treatment delivery accuracy. The computervision models may utilize convolutional neural networks (CNNs), recurrent neural networks (RNNs), and / or transformer architectures to process medical images and identify clinically relevant features with accuracy comparable to or exceeding human observers. In various embodiments, the computer vision capabilities enable automated quality assurance checks, realtime treatment verification, and adaptive treatment planning based on observed anatomical changes. In various embodiments, the Al engines include natural language processing (NLP) capabilities configured to analyze treatment plans, medical records, and clinical protocols to identify potential conflicts, optimization opportunities, and safety concerns. The NLP systems may process structured and unstructured clinical data to provide decision support for treatment planning, dose optimization, and patient safety monitoring. In some embodiments, the NLP capabilities enable automated generation of treatment summaries, safety reports, and regulatory compliance documentation.

[0084] In some embodiments, the machine learning models comprise federated learning architectures that enable multiple apparatuses 100 to collaboratively train shared models while maintaining patient privacy and data security. The federated learning approach allows individual apparatuses 100 to contribute to model improvement without sharing sensitive patient data, thereby enabling continuous enhancement of Al capabilities across a network of radiation therapy systems. In various embodiments, the federated learning models incorporate differential privacy techniques, secure aggregation protocols, and blockchain-based consensus mechanisms to ensure data privacy and model integrity. In various embodiments, the Al engines include reinforcement learning agents configured to optimize treatment delivery parameters, resource allocation, and operational efficiency through trial-and-error learning and reward-based optimization. The reinforcement learning agents may learn optimal control policies for beam delivery, patient positioning, and system coordination by interacting with simulated and real treatment environments. In some embodiments, the reinforcement learning capabilities enable adaptive optimization of treatment protocols based on individual patient characteristics, treatment response, and real-time physiological feedback.

[0085] In some embodiments, the machine learning models include ensemble methods that combine multiple Al algorithms to improve prediction accuracy, reduce false alarms, and enhance system robustness. The ensemble approaches may utilize voting mechanisms, weighted averaging, and stacking techniques to integrate predictions from different models and providemore reliable decision support. In various embodiments, the ensemble methods incorporate uncertainty quantification techniques to provide confidence intervals and reliability metrics for Al-generated recommendations and predictions.

[0086] In various embodiments, the apparatus 100 is configured to federate data amongst onboard processing means, local computing environments external to the apparatus 100, and remote computing environments. For example, the apparatus 100 may generate and store in onboard memory radiation therapy plans, motion management data, and / or the like. Further, the apparatus 100 may provision the radiation therapy plans, motion management data, and / or the like to nearby computing devices and / or a cloud-based service. In doing so, the apparatus 100 may offload or federate data processing workloads to the nearby computing devices, cloud-based service, and / or the like.

[0087] In some embodiments, the data federation architecture comprises hierarchical data distribution protocols that prioritize data sharing based on computational urgency, network bandwidth availability, and security requirements. For example, time-critical data such as realtime patient positioning information and beam control parameters may be processed locally within the apparatus 100 or shared with nearby edge computing devices to minimize latency, while computationally intensive tasks such as treatment plan optimization and long-term outcome analysis may be offloaded to remote cloud computing environments with greater processing capacity. The hierarchical approach may utilize quality of service (QoS) protocols, network traffic shaping, and adaptive bandwidth allocation to ensure optimal data flow and processing efficiency across the federated computing environment.

[0088] In various embodiments, the data federation capabilities include intelligent data caching and synchronization mechanisms that maintain consistency across distributed computing environments while minimizing network traffic and storage requirements. The caching systems may implement distributed hash tables, content delivery network (CDN) principles, and predictive prefetching algorithms to anticipate data access patterns and proactively position frequently accessed data at optimal locations within the federated network. In some embodiments, the synchronization mechanisms utilize eventual consistency models, conflict resolution algorithms, and versioning systems to manage concurrent data modifications across multiple computing environments while maintaining data integrity and preventing data corruption.

[0089] In some embodiments, the federated data architecture supports heterogeneous data formats and communication protocols to enable seamless integration with diverse computing environments and medical information systems. The apparatus 100 may include data transformation engines, protocol adapters, and format converters configured to translate between different data representations such as DICOM imaging data, HL7 clinical messages, JSON- formatted sensor data, and proprietary treatment planning formats. In various embodiments, the data federation system implements service-oriented architecture (SOA) principles, microservices patterns, and containerized deployment models to provide scalable and maintainable integration capabilities.

[0090] In various embodiments, the federated computing environment includes load balancing algorithms that dynamically distribute computational workloads based on real-time system performance metrics, resource availability, and processing requirements. The load balancing may utilize machine learning algorithms trained on historical performance data to predict optimal task allocation and resource utilization patterns. For example, the system may automatically redirect image reconstruction tasks from overloaded local computing resources to available cloud computing instances, or redistribute patient tracking calculations among multiple modules 103 based on their current computational capacity and network connectivity status. In some embodiments, the data federation architecture incorporates edge computing capabilities that enable local processing of latency-sensitive operations while maintaining connectivity to centralized computing resources for complex analytical tasks. The edge computing nodes may be positioned at various levels of the computing hierarchy, including within individual modules 103, at facility-level computing centers, and at regional data processing hubs. In various embodiments, the edge computing capabilities include local data preprocessing, real-time anomaly detection, and immediate response generation for safety-critical situations that require sub-millisecond response times.

[0091] In various embodiments, the federated data system includes comprehensive data governance and lineage tracking capabilities that maintain detailed records of data provenance, processing history, and access patterns across all computing environments. The data governance framework may implement blockchain-based audit trails, cryptographic data integrity verification, and automated compliance monitoring to ensure adherence to medical device regulations, patient privacy requirements, and data security standards. In some embodiments, thedata lineage tracking enables forensic analysis of treatment delivery accuracy, facilitates regulatory reporting, and supports quality assurance investigations by providing complete visibility into data flow and processing operations.

[0092] In various embodiments, a plurality of apparatuses 100 are configured to provision radiation therapy plans, outcomes, and / or the like with one another and a remote computing environment. In doing so, the network of apparatuses 100 may generate and distribute an evolving knowledge base by which radiation therapy processes and plans may be evaluated and augmented. In various embodiments, data provisioned by the apparatus 100 may be anonymized or pseudo-anonymized to prevent disclosure of sensitive data, such as personal identifiable information (PII), protected health information (PHI), and / or the like.

[0093] In some embodiments, the onboard computing capabilities enable a respective module 103 to communicate with one another and / or a remote computing environment to authenticate module hardware, software, firmware, and / or the like. In doing so, use of illicit or unauthorized components may be detected and prevented. For example, the apparatus 100 may provision an alert to a remote computing environment in response to a failure to authenticate a module 103 or element thereof. In some embodiments, the apparatus 100 is configured to provision, to a remote computing environment, one or more unique identifiers for the plurality of modules 103. In doing so, the apparatus 100 may enable the remote computing environment to track and authenticate the elements installed on the apparatus 100. Additionally, the authentication and tracking of modules 103 and firmware, hardware, software, and / or the like installed thereon may enable the remote computing environment to efficiently identify downstream affected entities in instances where components are determined to be faulty. For example, in response to determining a batch of module components fabricated on a particular date are faulty, the remote computing environment may index the modules 103 of one or more apparatuses 100 to determine one or more impacted modules 103 and initiate replacement or servicing of the affected components.

[0094] In various embodiments, the apparatus 100 is configured to self-monitor radiation therapy processes and determine the status of one or more modules 103. In some embodiments, the apparatus 100 is configured to output module statuses to one or more computing devices, remote computing environments, and / or the like. For example, the apparatus 100 may determine that tracked movements of a subject exceed one or more thresholds. In response to thedetermination, the apparatus 100 may output an alert to the subject, a computing device associated with an operator of the apparatus, a cloud-based monitoring service and / or the like. As another example, the apparatus 100 may determine that a vacuum pump of a vacuum module is experiencing early failure. In response to the determination, the apparatus 100 may provision an alert to a remote computing environment and, in doing so, cause the remote computing environment to initiate replacement, evaluation, and / or servicing of the vacuum pump.

[0095] In some embodiments, the modules 103 are individually upgradeable such that a module 103 may receive updates to hardware, software, firmware, and / or the like without impacting downtime or compatibility with other modules of the apparatus 100. For example, an imaging module may be upgraded for increased capacity, throughput, and / or the like without impacting compatibility with modules having earlier generations of compute or sensor functionality. In some embodiments, the apparatus 100 includes one or more computing elements configured to communicate with a remote computing environment to receive upgrades to module firmware, software, and / or the like. In some embodiments, the computing element is configured to identify the modules 103 and a current configuration thereof (e.g., hardware version, firmware version, software version, and / or the like). In various embodiments, the computing element is configured to access program code, drivers, and / or the like that are stored at one or more remote computing environments. In this manner, the apparatus 100 may be remotely updated and iterated upon without requiring high skill onsite labor (e.g., software engineers, programmers, and / or the like).

[0096] In some embodiments, the remote computing environment includes one or more application programming interfaces (APIs) and / or the like by which module software, module firmware, and / or the like are exposed to the apparatus 100. In some embodiments, the one or more APIs enable communication between multiple apparatuses 100. For example, via the API and remote computing environment, one or more modules of a first apparatus 100 may share data, configurations, updates, and / or the like with one or more modules of a second apparatus 100. In doing so, the apparatuses 100 may execute distributed computing processes, provision updates to one another, perform comparative health monitoring processes, and / or the like.

[0097] In some embodiments, the apparatus 100 determines and provisions to the remote computing environment a respective status of one or more modules 103. For example, the apparatus 100 may determine that one or more modules 103 are in an error state (e.g.,misfunctioning, depowered, unresponsive, and / or the like) or have experienced a compromising event, such as excess heat, shock, moisture, and / or the like. The apparatus 100 may provision the error state and an identifier for the module 103 to the remote computing environment. In doing so the apparatus 200 may cause the remote computing environment to determine that the module 103 should be replaced, serviced, updated, and / or the like. Further, the apparatus 100 may cause the remote computing environment to initiate a replacement process to ship a replacement module, component, and / or the like to the location of the apparatus 100. Additionally, or alternatively, the apparatus 100 may cause the remote computing environment to provision to the apparatus 100 (or a technician associated therewith) one or more instructions for servicing or updating the module 103.

[0098] In some embodiments, the apparatus 100 is configured to quarantine computing elements, sensors, modules 103, and / or the like that are determined to be in an error state. In such contexts, the apparatus 100 may utilize redundant components, modules, computing capacity, and / or the like. For example, the apparatus 100 may determine that a computing core in a first module 103 is experiencing an error state. In response to the determination, the apparatus 100 may disable the computing core from performing prior assigned tasks (e.g., image processing, beam control, patient tracking, and / or the like) and reallocate the computing workload to other computing cores in the first module 103 and / or additional modules 103. In some embodiments, the apparatus 100 is configured to offload computing workloads to a local computing environment, remote computing environment, and / or the like. For example, the apparatus 100 may provision sensor readings, imaging data, and / or the like to a remote computing environment for processing. In this manner, the computing capacity available to the apparatus 100 may be dynamically adjusted to accommodate current needs without requiring installation of additional onsite computing elements.

[0099] FIG. 2 shows a diagram of an example radiation therapy apparatus 100. As shown the apparatus 100 may comprise a plurality of modules 103 that are sequentially interlocked to one another in an annular arrangement 102. In some embodiments, the modules 103 are connected to one or more plates. In some embodiments, a plate includes a ring shape. In some embodiments, the modules 103 are connected to a first plate 104A on a first side and a second plate 104B on a second side such that the modules 103 are between the first plate 104A and second plate 104B. Alternatively, in some embodiments, a first subset of modules 103 areconnected to a first side of a plate 104C and a second subset of modules 103 are connected to a second side of the plate 104C (e.g., the first and second sides being opposite one another). In some embodiments, the modules 103 in the first and subset are arranged in an alternating pattern along the first and second sides of the plate 104C. In some embodiments, modules having mechanical systems are connected to a first side of the plate 104C and modules having electronic and / or computing systems are connected to a second side of the plate 104C. For example, a first grouping of modules 103 that are connected to a first side of the plate 104C may include a cooling module, a vacuum module, and / or the like. Continuing the example, a second grouping of modules 103 that are connected to a second side of the plate 104C may include a LINAC module, one or more imaging modules, one or more patient tracking modules, one or more onboard processing and / or storage means, and / or the like.

[0100] In various embodiments, the one or more plates improve weight balancing and stability of the apparatus 100. Further, the plates may oppose shifting of the modules 103 away from an installed position within the annular arrangement 102. In some embodiments, a respective plate comprises a single component. Alternatively, in some embodiments, a plate includes a plurality of sections connected to one another via fasteners, welding, and / or the like. In this manner, the plate may be disassembled for efficient transportation. Further, the grids, girders, and / the like that embody a respective section may reduce an overall weight of the assembled plate.Example Computing Device

[0101] FIG. 3 shows a block diagram of an example computing device 300 that may be specially configured in accordance with at least some example embodiments of the present disclosure. In some embodiments, the apparatus shown in FIG. 1 and described herein comprises one or more apparatuses configured to carry out functionality and processes described herein. For example, as shown in FIG. 3, the apparatus may further comprise or otherwise be communicably coupled with one or more computing devices 300. The computing device 300 may be configured to at least perform one or more operations described herein for providing radiation therapy to subjects and configuring and maintaining the radiation therapy apparatus 100. For example, one or more computing devices 300 may be configured to carry out operations of the radiation therapy apparatus 100, one or more modules, and / or the like shown in FIGS. 1-2.In various embodiments, one or more computing devices 300 are configured to perform functions including collimation control, beam generation control and modulation, sensor measurement processing, imaging, patient tracking, cooling control, vacuum control, and / or the like, among other functions associated with monitoring, controlling, and maintaining the radiation therapy apparatus 100. In some embodiments, a plurality of computing devices 300 of different modules 103 are configured to perform computing functions cooperatively with one another or redundantly in parallel to one another. In doing so, computational workloads and processes may be more efficiently carried out and performed by the radiation therapy apparatus 100.

[0102] In order to perform these operations, the computing device 300 may, as illustrated in FIG. 3, include a processor 302, a memory 304, input / output circuitry 306, and / or communications circuitry 308. The computing device 300 may be configured to carry out functionality described herein to generate and control application of radiation to a subject, identify and authenticate modules, monitor the status of modules, enable remote updating of module software and firmware, initiate replacement or maintenance processes for one or more modules, and / or the like. In various embodiments, the computing device 300 is configured to communicate with other computing devices 300 of the same or different module. For example, the computing device 300 may communicate with computing devices 300 of other modules to share processing workloads, monitor module statuses, initiate or control functionality on other modules, and / or the like. In some embodiments, the computing device 300 is configured to communication with one or more remote computing environments. For example, via one or more computing devices 300, the apparatus described herein may communicate with a remote computing environment to enable remote health monitoring, patient monitoring, troubleshooting, updating, maintenance, and / or the like.

[0103] Although components 302-308 are described in some cases using functional language, it should be understood that the particular implementations necessarily include use of particular hardware. It should also be understood that certain of these components 302-308 may include similar or common hardware. For example, two sets of circuitry may both use the same processor 302, memory 304, communications circuitry 308, or the like to perform their associated functions, such that duplicate hardware is not required for each set of circuitry. Theterm “circuitry” as used herein includes particular hardware configured to perform the functions associated with respective circuitry described herein.

[0104] Of course, while the term “circuitry” should be understood broadly to include hardware, in some embodiments, the term “circuitry” may also include software for configuring the hardware. For example, although “circuitry” may include processing circuitry, storage media, network interfaces, input / output devices, and the like, other elements of the computing device 300 may provide or supplement the functionality of particular circuitry.

[0105] In some embodiments, the processor 302 (and / or co-processor or any other processing circuitry assisting or otherwise associated with the processor) may be in communication with the memory 304 via a bus for passing information among components of the computing device 300. The memory 304 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In other words, for example, the memory may be an electronic storage device (e.g., a non-transitory computer readable storage medium). The memory 304 may be configured to store information, data, content, applications, instructions, or the like, for enabling the computing device 300 to carry out various functions in accordance with example embodiments of the present disclosure.

[0106] The processor 302 may be embodied in a number of different ways and may, for example, include one or more processing devices configured to perform independently. Additionally or alternatively, the processor may include one or more processors configured in tandem via a bus to enable independent execution of instructions, pipelining, and / or multithreading. The use of the term “processing circuitry” may be understood to include a single core processor, a multi-core processor, multiple processors internal to the computing device, and / or remote or “cloud” processors.

[0107] In an example embodiment, the processor 302 may be configured to execute instructions stored in the memory 304 or otherwise accessible to the processor 302. Alternatively or additionally, the processor 302 may be configured to execute hard-coded functionality. As such, whether configured by hardware or by a combination of hardware with software, the processor 302 may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly. Alternatively, as another example, when the processor 302 is embodied as anexecutor of software instructions, the instructions may specifically configure the processor 302 to perform the algorithms and / or operations described herein when the instructions are executed.

[0108] The computing device 300 further includes input / output circuitry 306 that may, in turn, be in communication with processor 302 to provide output to a user and to receive input from a user, user device, or another source. In this regard, the input / output circuitry 306 may comprise a display that may be manipulated by a mobile application. In some embodiments, the input / output circuitry 306 may also include additional functionality including a keyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, a microphone, a speaker, or other input / output mechanisms. The processor 302 and / or user interface circuitry comprising the processor 302 may be configured to control one or more functions of a display through computer program instructions (e.g., software and / or firmware) stored on a memory accessible to the processor (e g., memory 304, and / or the like).

[0109] The communications circuitry 308 may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and / or transmit data from / to a network and / or any other device, circuitry, or module in communication with the computing device 300. In this regard, the communications circuitry 308 may include, for example, a network interface for enabling communications with a wired or wireless communication network. For example, the communications circuitry 308 may include one or more network interface cards, antennae, buses, switches, routers, modems, and supporting hardware and / or software, or any other device suitable for enabling communications via a network. Additionally, or alternatively, the communication interface may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). These signals may be transmitted by the computing device 300 using any of a number of wireless personal area network (PAN) technologies, such as Bluetooth® vl.O through v3.0, Bluetooth Low Energy (BLE), infrared wireless (e.g., IrDA), ultra-wideband (UWB), induction wireless transmission, or the like. In addition, it should be understood that these signals may be transmitted using Wi-Fi, Near Field Communications (NFC), Worldwide Interoperability for Microwave Access (WiMAX) or other proximity-based communications protocols.Example Operations

[0110] To address some of the shortcomings of various existing approaches to radiation therapy system deployment and maintenance, various embodiments of the present disclosure provide techniques for implementing modular radiation therapy apparatuses with individually extractable components and federated processing capabilities. For example, in some embodiments, a radiation therapy apparatus comprises a plurality of modules configured to sequentially interlock within one another in an annular arrangement, where individual modules may be extracted for maintenance or replacement without disrupting the operational integrity of remaining system components. Further, the radiation therapy apparatus may incorporate specialized modules including collimator modules with multi-leaf collimators, linear accelerator modules with magnetrons or klystrons, imaging modules with various detection systems, patient tracking modules, cooling modules, and vacuum modules that collectively provide comprehensive treatment capabilities while enabling efficient transportation and deployment in diverse healthcare environments.

[0111] By utilizing the noted techniques for modular architecture, remote monitoring, and distributed processing coordination, various embodiments of the present disclosure may improve the accessibility and maintainability of radiation therapy systems across various deployment scenarios. In doing so, the embodiments may reduce transportation barriers, minimize maintenance downtime, and enhance operational flexibility through intelligent component management and federated computing capabilities. By enabling individual module extraction, remote health monitoring, and automated maintenance coordination, the described embodiments may enhance treatment delivery continuity, reduce operational costs, and facilitate deployment of sophisticated radiation therapy capabilities in low resource environments and geographically distributed healthcare facilities.

[0112] FIG. 4 depicts a flowchart diagram of an example process 400 for maintenance, upgrades, and replacements of modules in accordance with at least some embodiments of the present disclosure. The process 400 may be performed by various embodiments of the radiation therapy apparatus 100 shown in FIGS. 1-2 and described herein. For example, the process 400 may be performed by one or more computing devices 300 that embody functionality of the radiation therapy apparatus 100 described herein. In some embodiments, via various operations of the process 400, the radiation therapy apparatus 100 may improve maintenance efficiency and system reliability by enabling individual module extraction, remote monitoring of module health,and automated initiation of replacement or servicing procedures, reducing downtime requirements for maintenance operations, and enabling proactive component management while maintaining treatment delivery continuity through redundant system architectures.

[0113] At operation 403, the process 400 includes monitoring a respective status of the plurality of modules via onboard sensors and computing elements. In some embodiments, the status monitoring comprises continuous collection of sensor data including temperature measurements, voltage levels, pressure readings, acoustic emissions, and vibration patterns from sensors distributed throughout the plurality of modules. The monitoring process may utilize temperature sensors, moisture sensors, rotation sensors, acoustic sensors, pressure sensors, voltage sensors, and network identification sensors to generate comprehensive health assessments for individual modules and their components. In various embodiments, the monitoring system employs machine learning algorithms and artificial intelligence engines to analyze sensor data patterns and identify early indicators of component degradation or potential failures before they impact treatment delivery operations.

[0114] At operation 406, the process 400 includes determining whether any module is in an error state based at least in part on the monitored status data. In some embodiments, the error state determination comprises comparing real-time sensor measurements against predefined operational thresholds, analyzing trend patterns in historical data, and applying predictive maintenance algorithms to identify modules requiring attention. The determination process may utilize edge computing capabilities within individual modules to perform local analysis and anomaly detection, enabling rapid identification of issues without requiring external processing resources. For example, the system may detect excessive temperature readings in a cooling module, abnormal vibration patterns in a vacuum pump, or voltage irregularities in a LINAC module that indicate developing maintenance requirements.

[0115] At operation 409, the process 400 includes provisioning status information and module identifiers to a remote computing environment in response to detecting an error state. In some embodiments, the provisioning process enables centralized monitoring and management of multiple radiation therapy apparatuses through secure communication channels and standardized data formats. The apparatus 100 may transmit unique module identifiers, error codes, sensor readings, and diagnostic information to enable remote assessment of maintenance requirements and coordination of replacement or servicing activities. In various embodiments, theprovisioning process includes data anonymization and encryption capabilities to protect sensitive operational information while enabling effective remote monitoring and support services.

[0116] At operation 412, the process 400 includes receiving maintenance instructions or replacement notifications from the remote computing environment. In some embodiments, the remote computing environment analyzes the provisioned status information to determine appropriate maintenance actions, identify replacement components, and generate specific instructions for local technicians or automated systems. The instructions may include step-by- step procedures for module extraction, component replacement, calibration requirements, and safety protocols specific to the identified maintenance needs. For example, the remote system may determine that a vacuum module requires pump replacement and provide detailed instructions for safely extracting the module, replacing the pump assembly, and verifying proper operation before reinstallation.

[0117] At operation 415, the process 400 includes extracting the identified module from the annular arrangement without removing other modules. In some embodiments, the extraction process utilizes the modular architecture and one-way interlock mechanisms to enable individual module removal while maintaining the structural integrity and operational capability of remaining modules. The extraction may be performed using standard tools and jigs without requiring specialized equipment or highly skilled technicians, enabling efficient maintenance operations in diverse deployment environments. In various embodiments, the extraction process includes automated safety interlocks and quarantine procedures that isolate the module from other system components and prevent inadvertent activation during removal.

[0118] At operation 418, the process 400 includes installing a replacement or serviced module and verifying proper integration with the apparatus. In some embodiments, the installation process includes automated authentication procedures that verify module compatibility, firmware versions, and calibration status before enabling operational integration. The verification process may include communication testing with adjacent modules, sensor calibration validation, and functional testing of module-specific capabilities to ensure proper operation within the overall system architecture. For example, the system may verify that a replacement imaging module properly communicates with patient tracking modules and collimator modules to enable coordinated treatment delivery operations.

[0119] FIG. 5 depicts a flowchart diagram of an example process 500 for federated processing operations in accordance with at least some embodiments of the present disclosure. The process 500 may be performed by various embodiments of the radiation therapy apparatus 100 shown in FIGS. 1-2 and described herein, including federated processing among modules within a single apparatus, between modules and remote computing environments, and among modules of multiple apparatuses. For example, the process 500 may be performed by computing devices 300 distributed across multiple modules 103 and external computing systems. In some embodiments, via various operations of the process 500, the federated processing system may improve computational efficiency and system resilience by dynamically distributing workloads based on available resources, enabling collaborative processing across multiple apparatuses, and providing fault-tolerant computing capabilities that maintain treatment delivery continuity during component failures or maintenance operations.

[0120] At operation 503, the process 500 includes identifying available computing resources across the plurality of modules and external computing environments. In some embodiments, the resource identification process comprises real-time assessment of processing capacity, memory availability, network connectivity, and computational load across distributed computing elements within individual modules, local computing environments, and remote cloud-based services. The identification system may utilize distributed resource discovery protocols, performance monitoring algorithms, and predictive load analysis to maintain current awareness of available computational capacity. For example, the system may identify that imaging modules have available GPU processing capacity while LINAC modules are operating at full computational load, enabling intelligent task distribution for optimal resource utilization.

[0121] At operation 506, the process 500 includes receiving computational tasks requiring processing resources. In some embodiments, the computational tasks may include real-time image reconstruction, dose calculation algorithms, patient motion prediction, treatment plan optimization, sensor data analysis, and predictive maintenance calculations that require varying levels of processing power and specialized computational capabilities. The task reception process may include priority classification, resource requirement assessment, and deadline specification to enable intelligent scheduling and resource allocation decisions. In various embodiments, the system maintains task queues with different priority levels to ensure that safety-criticaloperations receive immediate processing attention while background tasks utilize available spare capacity.

[0122] At operation 509, the process 500 includes analyzing task requirements and matching them with optimal computing resources. In some embodiments, the analysis process utilizes machine learning algorithms trained on historical performance data to predict optimal resource allocation patterns based on task characteristics, system load conditions, and performance requirements. The matching process may consider factors including computational complexity, data locality, network latency, security requirements, and real-time constraints to determine the most appropriate processing location for each task. For example, latency-sensitive patient tracking calculations may be assigned to local module computing resources, while computationally intensive treatment planning tasks may be distributed to remote cloud computing environments with greater processing capacity.

[0123] At operation 512, the process 500 includes distributing computational tasks among selected computing resources using federated processing protocols. In some embodiments, the distribution process implements load balancing algorithms, fault tolerance mechanisms, and communication protocols that enable coordinated execution across heterogeneous computing environments. The federated processing may utilize message passing interfaces, distributed computing frameworks, and containerized execution environments to ensure consistent and reliable task execution regardless of the underlying hardware architecture. In various embodiments, the distribution system includes checkpoint and restart capabilities that enable recovery from processing failures and dynamic reallocation of tasks based on changing resource availability.

[0124] At operation 515, the process 500 includes monitoring task execution progress and system performance across distributed computing resources. In some embodiments, the monitoring process provides real-time visibility into computational workload status, resource utilization patterns, and performance metrics to enable proactive management of federated processing operations. The monitoring system may track task completion rates, processing latencies, error conditions, and resource consumption to identify optimization opportunities and potential issues requiring intervention. For example, the system may detect that image reconstruction tasks are experiencing delays due to network congestion and automatically redirect processing to local computing resources to maintain treatment delivery schedules.

[0125] In various embodiments, the system performance metrics include computational throughput measurements, resource utilization statistics, and quality of service indicators that provide comprehensive visibility into federated processing operations. The throughput metrics may encompass task completion rates measured in operations per second, data processing bandwidth measured in megabytes per second, and computational cycles per task to assess the efficiency of distributed processing across different module types and external computing environments. For example, the system may track that imaging reconstruction tasks achieve processing rates of 50-100 frames per second when distributed across multiple GPU-enabled modules, while dose calculation algorithms may demonstrate throughput rates of 10-20 treatment plans per minute when utilizing cloud-based computing resources with specialized mathematical coprocessors.

[0126] In some embodiments, the resource utilization metrics include CPU usage percentages, memory consumption patterns, network bandwidth utilization, and storage input / output operations per second across the distributed computing infrastructure. The system may monitor that individual modules maintain CPU utilization levels between 60-80% during normal operations to ensure adequate processing headroom for emergency tasks, while memory usage patterns may indicate optimal cache hit rates and data locality characteristics that influence task assignment decisions. Additionally, the performance monitoring may track network latency measurements between modules and external computing environments, with typical inter-module communication latencies of 1-5 milliseconds and cloud connectivity latencies of 50-200 milliseconds depending on geographic proximity and network infrastructure quality. The quality of service metrics may encompass task completion success rates, error recovery times, and system availability percentages that demonstrate the reliability and fault tolerance capabilities of the federated processing architecture.

[0127] At operation 518, the process 500 includes aggregating results from distributed processing tasks and integrating them into the overall system operation. In some embodiments, the aggregation process includes data validation, consistency checking, and result synthesis to ensure that distributed computations produce accurate and reliable outputs for treatment delivery and system control functions. The integration process may utilize consensus algorithms, redundancy verification, and quality assurance checks to validate distributed processing results before incorporating them into safety-critical operations. In various embodiments, theaggregation system maintains audit trails and processing lineage information to support regulatory compliance and quality assurance requirements.

[0128] At operation 521, the process 500 includes sharing processing results and system knowledge with other radiation therapy apparatuses through federated learning networks. In some embodiments, the knowledge sharing process enables collaborative improvement of treatment algorithms, predictive maintenance models, and operational optimization across multiple installations while maintaining patient privacy and data security. The federated learning system may utilize differential privacy techniques, secure aggregation protocols, and anonymization procedures to enable knowledge sharing without exposing sensitive patient information. For example, multiple apparatuses may collaboratively train machine learning models for patient motion prediction by sharing model updates rather than raw patient data, enabling improved treatment accuracy across the entire network of installations.

[0129] FIG. 6 depicts a flowchart diagram of an example process 600 for remote monitoring and maintenance coordination in accordance with at least some embodiments of the present disclosure. The process 600 may be performed by various embodiments of the radiation therapy apparatus 100 shown in FIGS. 1-2 and described herein in coordination with remote computing environments for centralized monitoring and maintenance management. For example, the process 600 may be performed by computing devices 300 within modules 103 communicating with external monitoring systems and cloud-based maintenance coordination services. In some embodiments, via various operations of the process 600, the remote monitoring system may improve maintenance efficiency and system reliability by enabling centralized oversight of multiple radiation therapy apparatuses, proactive identification of maintenance requirements, and coordinated deployment of replacement components and service resources across distributed installations.

[0130] At operation 602, the process 600 includes monitoring a respective status of the plurality of modules via a remote computing environment. In some embodiments, the remote monitoring process comprises continuous collection and analysis of operational data, sensor measurements, and performance metrics transmitted from multiple radiation therapy apparatuses to centralized monitoring systems. The remote monitoring capabilities may utilize cloud-based analytics platforms, machine learning algorithms, and predictive maintenance models to assess system health and identify developing maintenance requirements across distributed installations.For example, the remote system may simultaneously monitor temperature trends, vibration patterns, and performance degradation indicators from cooling modules across multiple apparatuses to identify common failure modes and optimize maintenance scheduling.

[0131] At operation 604, the process 600 includes determining whether maintenance is needed based on the monitored status information. In some embodiments, the maintenance determination process utilizes automated decision algorithms, threshold-based alerting systems, and predictive analytics to identify modules requiring immediate attention, scheduled maintenance, or proactive replacement. The determination process may consider factors including component age, usage patterns, environmental conditions, and historical failure data to generate maintenance recommendations with appropriate urgency levels and resource requirements.

[0132] At operation 606, the process 600 includes determining to replace at least one of the plurality of modules based on the respective status. In some embodiments, the replacement determination process evaluates the cost-effectiveness of repair versus replacement, component availability, and operational impact to generate optimal maintenance strategies. The system may automatically initiate procurement processes, coordinate shipping logistics, and schedule maintenance windows to minimize treatment delivery interruptions while ensuring optimal system performance and reliability.

[0133] At operation 608, the process 600 includes replacing at least one module without removing a remaining subset of modules from the annular arrangement. In some embodiments, the replacement process utilizes the modular architecture to enable selective component replacement while maintaining operational capability of unaffected system elements. The process may include remote guidance for local technicians, automated verification procedures, and integration testing to ensure proper installation and system compatibility.

[0134] At operation 610, the process 600 includes initiating software updates or firmware updates pursuant to at least one of the plurality of modules. In some embodiments, the update process enables remote deployment of software enhancements, security patches, and firmware improvements without requiring on-site technical expertise. The update system may include staged deployment procedures, rollback capabilities, and compatibility verification to ensure safe and reliable implementation of system improvements across distributed installations.

[0135] FIG. 7 depicts a flowchart diagram of an example process 700 for module authentication and tracking in accordance with at least some embodiments of the present disclosure. The process 700 may be performed by various embodiments of the radiation therapy apparatus 100 shown in FIGS. 1-2 and described herein to ensure system security and component authenticity through remote verification and tracking capabilities. For example, the process 700 may be performed by computing devices 300 within modules 103 communicating with centralized authentication servers and component tracking databases. In some embodiments, via various operations of the process 700, the authentication system may improve system security and regulatory compliance by preventing use of unauthorized components, enabling rapid identification of affected systems during component recalls, and maintaining comprehensive audit trails for quality assurance and regulatory reporting purposes.

[0136] At operation 703, the process 700 includes provisioning unique identifiers for the plurality of modules to a remote computing environment. In some embodiments, the identifier provisioning process transmits module serial numbers, hardware versions, firmware versions, and cryptographic certificates to centralized authentication and tracking systems. The unique identifiers may include manufacturer information, production batch data, and component specifications to enable comprehensive tracking and authentication capabilities throughout the module lifecycle.

[0137] At operation 706, the process 700 includes authenticating module hardware, software, and firmware via the remote computing environment. In some embodiments, the authentication process utilizes cryptographic verification, digital signatures, and certificate validation to confirm component authenticity and detect unauthorized modifications. The authentication system may verify component integrity, validate software checksums, and confirm compatibility with approved system configurations to prevent security vulnerabilities and ensure regulatory compliance.

[0138] At operation 709, the process 700 includes determining whether authentication was successful. In some embodiments, the authentication verification process evaluates cryptographic signatures, certificate validity, and component compatibility to generate pass / fail determinations for individual modules and system components. The verification process may include multiple authentication factors and redundant verification mechanisms to ensure reliable detection of unauthorized or compromised components.

[0139] At operation 712, the process 700 includes enabling normal module operation and tracking module status following successful authentication. In some embodiments, the operational enablement process activates module functionality, establishes communication channels with other system components, and initiates continuous monitoring of module performance and security status. The tracking system may maintain real-time awareness of module operational status, performance metrics, and security posture to support ongoing system management and maintenance operations.

[0140] At operation 715, the process 700 includes provisioning alerts to the remote computing environment regarding authentication failures. In some embodiments, the alert provisioning process generates immediate notifications to security monitoring systems, maintenance personnel, and regulatory authorities when unauthorized or compromised components are detected. The alert system may include detailed diagnostic information, component identification data, and recommended response procedures to enable rapid containment and remediation of security incidents.

[0141] At operation 718, the process 700 includes updating module tracking databases and monitoring for component issues. In some embodiments, the database update process maintains comprehensive records of module deployment, authentication status, and operational history to support quality assurance, regulatory compliance, and predictive maintenance activities. The monitoring system may track component performance trends, identify emerging issues, and coordinate proactive maintenance interventions to ensure optimal system reliability and safety.

[0142] While various aspects have been described, additional aspects, features, and methodologies of the claimed apparatuses will be readily discernible from the description herein, by those of ordinary skill in the art. Many embodiments and adaptations of the disclosure and claimed inventions other than those herein described, as well as many variations, modifications, and equivalent arrangements and methodologies, will be apparent from or reasonably suggested by the disclosure and the foregoing description thereof, without departing from the substance or scope of the claims. Furthermore, any sequence(s) and / or temporal order of steps of various processes described and claimed herein are those considered to be the best mode contemplated for carrying out the claimed inventions. It should also be understood that, although steps of various processes may be shown and described as being in a preferred sequence or temporal order, the steps of any such processes are not limited to being carried out in any particularsequence or order, absent a specific indication of such to achieve a particular intended result. In most cases, the steps of such processes may be carried out in a variety of different sequences and orders, while still falling within the scope of the claimed inventions. In addition, some steps may be carried out simultaneously, contemporaneously, or in synchronization with other steps.

[0143] The embodiments were chosen and described in order to explain the principles of the claimed inventions and their practical application so as to enable others skilled in the art to utilize the inventions and various embodiments and with various modifications as are suited to the particular use contemplated. Alternative embodiments will become apparent to those skilled in the art to which the claimed inventions pertain without departing from their spirit and scope. Accordingly, the scope of the claimed inventions is defined by the appended claims rather than the foregoing description and the exemplary embodiments described therein.

Claims

CLAIMSWhat is claimed is:

1. A radiation therapy apparatus, comprising: a plurality of modules configured to sequentially interlock within one another in an annular arrangement, wherein: the plurality of modules are individually extractable from the annular arrangement; and at least a subset of the plurality of modules comprise computing elements configured to communicate with one another; and the plurality of modules comprising: a collimator module comprising a multi-leaf collimator (MLC) and at least one sensor, the at least one sensor comprising at least one of a temperature sensor, voltage sensor, or network identification (ID) sensor; a linear accelerator (LINAC) module comprising at least one of a magnetron or a klystron; at least one imaging module, a respective imaging module comprising at least one of an image detector, an imaging source, a magnetic imaging system, a single photon emission computed tomography (SPECT) system, or a positron emission tomography (PET) system; a patient tracking module; a cooling module; and a vacuum module.

2. The radiation therapy apparatus of claim 1, wherein: a connection between respective modules comprises a one-way interlock configured to secure the module against removal in a first direction and enable removal of the module in a second direction that is opposite the first direction.

3. The radiation therapy apparatus of claim 1, wherein: a respective module comprises a first surface and a second surface located opposite the second surface;the first surface comprises a plurality of protrusions configured to mate with a plurality of voids of a first adjacent module; and the second surface comprises a plurality of voids configured to receive a plurality of protrusions of a second adjacent module.

4. The radiation therapy apparatus of claim 1, further comprising: at least one plate comprising a ring shape, wherein: the plurality of modules are connected to the plate; and the plate is configured to balance a weight of the annular arrangement.

5. The radiation therapy apparatus of claim 4, wherein: the at least one plate comprises a first side and a second side opposite the first side; a first grouping of the plurality of modules are connected to the first side; and a second grouping of the plurality of modules are connected to the second side.

6. The radiation therapy apparatus of claim 5, wherein: the first grouping comprises cooling module and the vacuum module; and the second grouping comprises the collimator module, the LINAC module, the at least one imaging module, and the patient tracking module.

7. The radiation therapy apparatus of claim 4, wherein: the at least one plate comprises a first plate and a second plate; and the plurality of modules are connected to the first plate on a first side of the annular arrangement and the second plate on a second side of the annular arrangement.

8. The radiation therapy apparatus of claim 5, wherein: the at least one plate comprises carbon fiber.

9. The radiation therapy apparatus of claim 1, wherein: a respective module comprises at least one magnetic shielding augment.

10. The radiation therapy apparatus of claim 1 , wherein: a respective module comprise a near-field communication circuit configured for uniquely identifying and authenticating the module.

11. The radiation therapy apparatus of claim 1, wherein: at least two or more modules of the plurality of modules comprise redundant computing elements.

12. The radiation therapy apparatus of claim 1, wherein: at least one of the plurality of modules comprises at least one computing device configured to: monitor a respective status of one or more of the plurality of modules; determine that a module is in an error state based at least in part on the monitored status; and provision to a remote computing environment the monitored status and a unique identifier for the module.

13. A method for maintaining a modular radiation therapy apparatus comprising a plurality of individually extractable modules in an annular arrangement, the method comprising: monitoring a respective status of the plurality of modules via a remote computing environment; and performing, via the remote computing environment, at least one of the following based at least in part on the statuses: determining to replace at least one of the plurality of modules based at least in part on the respective status; and initiating a software update or firmware update pursuant to at least one of the plurality of modules based at least in part on the respective status.

14. The method of claim 13, further comprising: replacing at least one of the plurality of modules without removing any of a remaining subset of the plurality of modules from the annular arrangement.

15. The method of claim 13, further comprising: receiving, at the remote computing environment, a plurality of identifiers; and identifying the plurality of modules based at least in part on the plurality of identifiers.

16. A modular radiation therapy apparatus, comprising: a plurality of modules configured to sequentially interlock within one another in an annular arrangement, wherein: the plurality of modules are individually extractable from the annular arrangement; and at least a subset of the plurality of modules comprise computing elements configured to communicate with one another to distribute computational workloads among the plurality of modules and communicate with a remote computing environment to offload processing tasks.

17. The modular radiation therapy apparatus of claim 16, wherein: the computing elements are configure to perform at least one federated processing operation; and the at least one federated processing operation comprises dynamic load balancing of computational tasks among the computing elements of different modules based on real-time system performance metrics and resource availability.

18. The modular radiation therapy apparatus of claim 16, wherein: at least a subset of the computing elements comprise at least one machine learning model configured to analyze operational data from the plurality of modules to predict maintenance requirements and detect anomalies.

19. The modular radiation therapy apparatus of claim 18, wherein: the at least one machine learning model is configured to generate maintenance predictions for at least a subset of the plurality of modules based at least in part on sensor data.

20. The modular radiation therapy apparatus of claim 16, wherein: the computing elements are configured to participate in federated learning operations with other radiation therapy apparatuses.

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