System and method for remote PPG MR triggering

Abstract

A gating system (GT) and associated method for data acquisition of a target biophysical system (TS) by an imaging device (IA). The system has an input interface (IF) through which it can receive a time-domain phase representation of a time-domain signal of a surrogate biophysical system (SGS) measurement, which can be acquired by a sensor (S) device that operates on non-ionizing radiation. A biomarker extractor (BX) extracts instances of biomarkers that can be associated with the state of the target biophysical system from the time-domain phase representation. An output interface (OUT) provides indication of the instances of biomarkers thus extracted for data gating operation.

Description

[Technical Field]

[0001] The present invention relates to a gating system for acquiring data on a target biophysical system using an imaging device, related methods, an imaging device having the gating system, a computer program, and a computer-readable medium. [Background technology]

[0002] Cardiac magnetic resonance imaging (MRI) typically uses triggers based on electrocardiogram (ECG) measurements for scans that require synchronization to the cardiac pulse and / or cardiac state. Such synchronized triggers are sometimes referred to as some form of gating.

[0003] ECG measurements may include the so-called R peak, a signal feature representing the electrical activity at the onset of myocardial contraction, i.e., the signal feature indicating the start of the systolic state. Based on the R peak, an MR trigger sequence can be initiated. The trigger sequence may have one or more acquisition pulses and optionally one or more preparation pulses. The total duration of this sequence is checked against the duration of the cardiac cycle and the intended image formation. If the sequence does not fit the cycle, it may be split over two cycles to ensure that the acquisition pulses occur at the desired phase of the cardiac cycle. [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] For various reasons, it may be desirable to replace ECG-based measurements with triggers for other measurement modalities. For example, non-contact camera-based photoplethysmography ("PPG") triggering can be used instead of ECG to improve workflow, reduce the possibility of cross-contamination, reduce waste (disposables), and operate at higher MR magnetic field strengths, etc.

[0005] Deriving high-quality triggers from such PPG signals requires high standards. Therefore, today, efforts are being made to improve PPG signal quality and the acquisition of lead markers that can be associated with MRI trigger events of interest (e.g., the aforementioned R peak or others).

[0006] Summary of the Invention

[0007] Improved operation of imaging devices, particularly for medical imaging, is especially needed. [Means for solving the problem]

[0008] The object of the present invention is achieved by the subject matter of the independent claims. Other embodiments are incorporated into the dependent claims. It should be noted that the embodiments described below of the present invention are equally applicable to related methods, imaging apparatuses, computer programs, and computer-readable media.

[0009] According to a first aspect of the present invention, a gating system is provided for data acquisition of a target biophysical system by an imaging device, comprising: an input interface capable of receiving a time-domain phase representation of a time-domain signal of a measurement of a surrogate biophysical system, which can be acquired by a radiation-based sensor device, wherein the radiation is non-ionizing; a biomarker extractor capable of extracting at least one instance of a biomarker that can be associated with the state of the target biophysical system from the time-domain phase representation in at least one extraction event; and an output interface that provides an indication of at least one extraction event for data gating operation.

[0010] Accordingly, when such a biomarker is extracted (i.e., when such an instance is found or identified in a time-domain phase signal using the characteristics of the signal), the timing of the extraction constitutes such an extraction event. The fact that such an extraction has occurred is provided in an output in the form of a marking to control the drive or otherwise affect the gating operation. The provision of such a marking is done quickly, preferably in real time, i.e., (almost) simultaneously, when the event occurs. Two types of such gating operations are contemplated herein, and therefore, in embodiments, the data gating operation may be prospective or retrospective, the prospective gating operation includes triggering a data acquisition operation by the imaging device based on a provided marking of at least one extraction event, and the retrospective gating operation includes tagging data acquired by the imaging device based on a provided marking of at least one extraction event. In embodiments, the sensor device can be configured for non-contact operation. This enables an improved medical workflow.

[0011] The time-domain phase representation is based on the analytical signal representation of the (time-domain) signal of the measurement. For brevity, the measurement signal is sometimes referred to herein as the “measurement signal.” Thus, in embodiments, the analytical signal is a representation of the measurement signal in the complex plane. The representation is not limited to solid lines. Thus, the representation relates to complex numbers in a time series, at least some of which have non-zero imaginary numbers as components. In such embodiments, the time-domain phase representation of the phase based on the analytical signal may be expressible or realizeable by a transformation, such as one performed by a converter that maps the time-domain measurement to the complex plane. Thus, the time-domain phase representation is based on complex numbers, in particular, such numbers that do not have a zero imaginary component of the imaginary unit i = (-1). In embodiments, the analytical signal based on the time-domain phase representation of the phase is computable by a converter. The converter can perform or represent the time-domain signal of the measurement by a Hilbert transform. In some embodiments, the calculation of the time-domain phase representation of the phase by the converter corresponds to such a Hilbert transform. The Fourier method may be used. The explicit calculation of the Hilbert transform itself may not be necessary in some embodiments. The analytic signal can be obtained by a transform (such as the Hilbert transform). The analytic version of a time-domain measurement is, as mentioned above, a time-domain representation of a complex number that includes non-zero imaginary numbers as components. The spectrum of an analytic signal does not have negative frequency components, whereas a time-domain measurement (signal) in the frequency domain generally has positive and negative frequency components.

[0012] In some embodiments, what is proposed herein is the phase derived from the analysis signal associated with a segment of the measurement signal, rather than the full-length measurement signal. Accordingly, what is proposed herein may be called the short-time analysis signal phase (representation) or the sliding window analysis signal phase (representation).

[0013] In a preferred embodiment, the operation of a biomarker extractor capable of such biomarker extraction is not complex. Therefore, as specified herein, a complete signal is not required for the biomarker to operate. The biomarker can operate in real time when a measurement signal is input. The system enables real-time prospective triggering.

[0014] The phase may be a phase vector in the complex plane with respect to a reference such as the real axis or the imaginary axis. In some embodiments, the time-domain phase representation is one of the instantaneous phases.

[0015] In embodiments, the time-domain signal of a measurement that the transducer can process includes multiple frequency components. The signal can be assumed herein to be at least a quasi-periodic signal. Therefore, filtering that substantially removes all frequency components other than the fundamental frequency is undesirable herein, as it has been shown to degrade system performance.

[0016] In the embodiment, the timing of the extracted biomarkers corresponds to the timing before a maximum or minimum occurs in the time-domain signal of the measurement. This has been found to be particularly useful in cardiac imaging.

[0017] In embodiments, the system includes a filter capable of low-pass filtering the time-domain signal being measured and / or low-pass filtering the receivable time-domain phase representation. Noise filtering can be used in either case or both.

[0018] In the embodiment, the extractor can perform such extraction operations based on thresholding, such as zero crossing of time-domain phase signals.

[0019] In an embodiment, the extractor can perform such extraction operations based on processing data of the time-domain phase representation over a time-domain window (such as combining data by averaging, filtering, or other methods).

[0020] In an embodiment, the endpoints of such a window are located in the time domain with a margin away from the current time of the time-domain phase representation. This enables more robust processing as the interval of signal degradation immediately following the current time can be avoided.

[0021] In an embodiment, the system interfaces with or is interfaceable with a control interface, and the control enables triggering of the acquisition operation by the imaging device based on the indication of at least one provided extraction event.

[0022] In an embodiment, the imaging device is of a tomography type, particularly a magnetic resonance type, or a projection area type. However, other modalities such as CT (Computed Tomography) and nuclear medicine are also contemplated as alternatives or in combinations such as for certain tandem systems.

[0023] In an embodiment, the target biophysical system is capable of transitioning states, and the state is one of such states.

[0024] In an embodiment, the target or surrogate biophysical system includes any one or more of i) the cardiac system, ii) the respiratory system, and iii) the gastrointestinal system. The cardiac system can be of interest in cardiac imaging synchronized with the charged state of the heart, and the state can be represented by the characteristics of the QRS complex. In particular, this state can correspond to, for example, the R peak (systolic state) or the diastolic state. However, it should be noted that an electrocardiogram (ECG) is not required in the proposed configuration. In an embodiment, the surrogate biophysical system includes an imaged patient skin patch.

[0025] Specifically, in embodiments, the skin patch is one or more of i) a human finger, or ii) an imageable patient's head. For example, in PPG, the patch can be used on a part of the forehead, such as the temple. In particular, in contact systems, a fingertip clip-type PPG sensor can be used.

[0026] In the embodiment, the extracted biomarkers correspond to a time-domain moment indicating the presence of a low blood volume (such as a minimum value during the cardiac cycle) in a surrogate biophysical system (such as the skin patch).

[0027] In embodiments, the sensor device is configured to sense non-ionizing radiation such as visible light, IR, NIR, laser (e.g., LIDAR), or any one of other radars, the latter of which are less preferred herein. The sensor may be a PPG sensor, or other capable of acquiring video to measure the changing skin reflex caused by the changing blood volume therein. However, non-radiation-based sensors, such as ultrasonic sensors, or sensors capable of sensing any one of acoustic signals, mechanical pressure, or force, are also contemplated herein. The latter can be used, in particular, when measuring signals relating to the respiratory system or the gastrointestinal system. However, an elastic electrocardiogram sensor or similar device may also be used to measure external manifestations of visceral movement, such as for recoil due to cardiac activity.

[0028] In embodiments, the sensor device is positioned for remote (non-contact) operation away from any one or more of the target biophysical system and / or surrogate biophysical systems. Whether remote / non-contact or not, the sensor device is preferably capable of non-ionizing operation as described above. In particular, such operation is based on radiation that is non-ionizing. In addition, or instead, the operation of the sensor device is such that it does not interfere with (or interferes to an extent that is negligible for the present purpose) static or dynamic magnetic fields, such as those that may be encountered in MRI imaging applications. The sensor device is different from the detector device used for imaging by the imaging device. The sensor device is preferably not an ECG sensor / measurement device. This is because any other sensor other than an ECG can obtain measurements that can correlate with the state of the target biophysical system.

[0029] In another embodiment, an imaging apparatus is provided which has the system described in any one of the above descriptions and further comprises one or more of i) an imaging device and ii) a sensor device.

[0030] In some embodiments, the system includes a delay estimator module configured to estimate the delay between the timing of extracted biomarkers and the timing of their states. In some embodiments, the delay can be used to adapt instructions for data acquisition to be gated based on the extracted events. For example, such instructions may include an MRI pulse sequence specification. The MRI pulse sequence specification may define a trigger delay. This trigger delay may be adapted based on the estimated delay so that it can be better used for grating based on biomarkers extracted from time-domain phase representations.

[0031] Accordingly, in some embodiments, the system may include a data interface for retrieving existing instructions for data acquisition from memory and adapting them based on estimated delays. These adapted instructions for data acquisition can then be used (in place of existing instructions) for data acquisition in the gating operation. In particular, in MRI, an MRI pulse sequence standard with a trigger delay adapted based on estimated delays can be used in gating.

[0032] In another embodiment, a computer-implemented gating method is provided for data acquisition by an imaging device relating to a target biophysical system, comprising the steps of: receiving a time-domain phase representation based on an analysis signal of a time-domain signal of a measurement of a surrogate biophysical system, which is obtainable by a radiation-based operating sensor device, wherein the radiation is non-ionizing; extracting at least one instance of a biomarker that can be associated with the state of the target biophysical system from the time-domain phase representation in at least one extraction event; and providing an indication of at least one extraction event for data gating operation.

[0033] In another embodiment, a computer program is provided which, when executed by at least one processing unit, is adapted to cause a processing unit to perform the method.

[0034] In another embodiment, at least one computer-readable medium storing a computer program is provided.

[0035] In another embodiment, the use of a time-domain phase representation of a signal (such as a PPG) is provided for gating the data acquisition operation in an imaging device.

[0036] The underlying signal is preferably something other than the ECG signal.

[0037] Preferably, the use includes using an estimated delay in a gating operation, particularly in a data acquisition operation.

[0038] One proposed method, for example, is to use a transformed version of a PPG signal (waveform) to extract biomarkers ("markers") based on time-domain phase. While the PPG signal is an example of the original signal of a measurement, other types of measurements that do not necessarily need to be based on PPG are also conceived. The instantaneous phase is preferably based on an analytical signal and is an example of the transformed version of the measurement signal. Thus, such a transformed version can, for example, be based on the analytical signal representation of a time-domain measurement. Thus, the phase is one of the complex numbers in the complex plane (having a non-zero component along the imaginary axis i). That is, in the proposed system, instead of extracting peaks or features from the time-domain measurement signal (e.g., the PPG signal) itself, a second signal (the transformed version) is derived, which is the time-domain phase (e.g., the instantaneous phase), obtained as the phase of the analytical signal representation of the original signal of the measurement. This transformed version of the signal has been shown to exhibit sufficiently stable periodicity and fidelity with respect to cardiac pulses and can therefore be used as a surrogate of the PPG waveform in trigger or other gate applications. The analysis signal-based representation for the conversion proved to yield good and robust results.

[0039] In instantaneous phase signals, or other such time-domain phase signals (representing (non-stationary) phase as a function of time), zero crossing (or other definition / feature) is known to constitute one or more stable markers with respect to, for example, the R peak. Zero crossing is known to occur immediately before the minimum blood volume state / R peak and is independent of heart rate. This invariant property is desirable for MRI triggering, or more generally for gating. Any other thresholding can be used instead of zero crossing.

[0040] In some embodiments, ECG-blind (independent) synchronization with R-peak features is proposed herein. A low-order model can be used based on PPG signal features, such as those represented by markers in the time-domain phase signal. In other words, in the proposed system, an ECG signal is not required, and therefore, an ECG measuring instrument is also not required. Thus, at the start of the scan procedure, the expected patient-specific delay between the R-peak and the marker can be determined. Thus, in the proposed system, gating is enabled based on the marker (as in the case of MRI), using a marker extracted from the time-domain phase of the original measurement signal;

[0041] The marker occurs earlier (in time) than the feature of interest, such as the maximum value in the original signal of the measurement;

[0042] The markers respond to details related to the systolic / diastolic state, and are functionally coupled to R-peak features, thus allowing them to be used for "blind" prediction (without ECG).

[0043] The above primarily pertains to applications in cardiac imaging, but such markers can be used for other cardiac imaging applications, or even for purposes other than cardiac imaging.

[0044] The proposed method is particularly well-suited for real-time processing and can therefore be used for prospective gating, i.e., to trigger data acquisition operations within / by the imaging device. However, retrospective gating is not excluded herein.

[0045] The proposed method or system is suitable for real-time processing for at least the following reasons:

[0046] The physiological delay between the R-peak and the marker is small;

[0047] The algorithmic delay (latency) incurred for marker extraction is low;

[0048] The proposed setup is repeatable and robust, and exhibits low jitter in marker extraction.

[0049] The aforementioned delays (physiological or algorithmic) and jitter consume less time than the time consumed by changes occurring in the target and / or surrogate biophysical systems, and the time required for the image (data acquisition operation) to complete (wait time) is low. Specifically, this method and system are well suited as gates (e.g., triggers) in MRI, as a whole sequence including prepulse, preparation time, and imaging pulse, which can be adapted to IBI (heartbeat interval). Thus, for example, the delay between the R peak state and the trigger can be kept short thanks to the proposed setup which uses time-domain phase to find the trigger point (biomarker).

[0050] The proposed system will be described primarily with reference to the medical field, but this is not necessarily required in all embodiments herein, and the principle of surrogate observation by sensor devices can also be applied to non-medical settings where the target system is inaccessible for some reason, instead of (direct) measurement in the target system. Nevertheless, whether in the medical field or not, the target system is different from and can be separated from the surrogate system used for measurement to facilitate gating, but this is not necessarily required herein. Thus, in some application scenarios, the target biophysical system and the surrogate biophysical system may be identical, or at least one may comprise the other.

[0051] "User" refers to a medical professional or other person who operates the imaging device or monitors the imaging procedure. In other words, the user is generally not the patient.

[0052] "Time-domain phase" refers to a form of the time-domain signal of a measurement. As mentioned above, the signal of a measurement may be referred to as the "measurement signal" in this specification for brevity. The time-domain phase captures the overall global phase of the signal at a given time instant, or up to that time instant. Therefore, this phase is not tied to a specific frequency of the measurement signal. The time-domain phase can relate to the phase transition of the angular description of the phase vector on the complex plane, which can be associated with the measurement signal. Like the measurement signal, the time-domain phase is also a phase representation in the time domain and can therefore be used for gating purposes. It has been found that unfolding the information encoded in the original measurement signal onto the complex plane can more clearly reveal appropriate biomarkers, particularly in cardiac imaging. The state of a surrogate system correlated with the state of a target system (e.g., cardiac state) (such as a time-dependent PPG signal or other surrogate measurement signal) has been shown to yield appropriate biomarkers when the measurement signal is converted into a time-domain phase representation, and such converted signals facilitate biomarker extraction for gating. For example, the original measurement signal is thus encapsulated in a complex signal (e.g., an analytical version of the initial time-domain signal based on the Hilbert transform), or in a version of the measurement signal with respect to time, where its phase with respect to time is considered here instead of the original signal for gating purposes. As disclosed herein, the phase (angle argument) of the Hilbert transform-based analytical version of the original measurement signal (e.g., a time-series optical or non-optical image) may be considered herein for gating purposes in some examples.

[0053] In the "surrogate (biophysical system)" versus "target (biophysical system)" analogy, the surrogate biophysical system and the target biophysical system can represent different parts (organs, anatomical structures, or groups or parts thereof), functions (metabolic or physiological), or any other aspects of the human body or within the human body that relate to each other. Therefore, measurements obtained from a surrogate biophysical system can be used to predict a state of interest in the target biophysical system, although the target biophysical system may not be accessible on its own, or direct gating may not be feasible or cumbersome. In cardiac applications, the cardiac state as a target may include the R peak or other features of the QRS complex, while the surrogate state may include the amount of blood in a part of the skin, such as the patient's forehead or finger.

[0054] A "biomarker" (or simply "marker") includes any properties or features present in or appearing in a signal s or its transformed T(s), which are correlateable with the state of interest and state changes of the target system. The appearance and quality of biomarkers may change as the surrogate system changes along with the target system. The state of interest may be any state to be imaged, but such state may be purely for gating purposes, or one state may be used to image another state, or the imaging may not be limited to at least one state of interest. In the latter case, where the state of interest is solely for gating purposes, this approach may be useful in considering data acquisition latency.

[0055] The "extraction" of biomarkers involves identifying the moment when a biomarker appears and / or when one or more conditions are met (extraction policy).

[0056] "Gating" can be defined as data acquired by an imager of a target biophysical system, or as a synchronization process for acquired data. The target is subject to or susceptible to change, and therefore transitions between states. States can be described as the physiological settings of the target system. The process is synchronized with the timing, i.e., the occurrence of such states. Gating can be prospective or retrospective. Predictive gating, as primarily intended herein, corresponds to the process of initiating data acquisition at an appropriate time based on the temporal moment of an extracted biomarker. Thus, for each time-domain phase representation of the measurement signal acquired from the surrogate biophysical system, a frame is acquired only when the target biophysical system is in a predetermined (same) state. Retrospective gating means acquiring frames over time as if there were no motion or other state changes that would impair image quality, and assigning tags representing states based on the time-domain phase of the surrogate signal to each or some of such frames. Because this is done consistently over time, frames representing the same state of the target biophysical system are assigned the same tags. Next, when reconstruction is required, frames in the desired state are retrieved from the entire set of frames based on tags and reconstructed within one or more cross-sectional image planes (volumes) representing the biophysical target system in the state of interest. In prospective gating, by definition, all frames are acquired from the outset only for the same state, so such tag-based searching is unnecessary. Therefore, reconstruction can be performed based on the acquired frames. For this reason, prospective gating reduces radiation dose (e.g., CT) and wear on imaging device components (e.g., load on coils in MRI) because frames are acquired only when the biophysical target system is in the appropriate state (i.e., the state of interest in the imaging task).

[0057] Herein, exemplary embodiments of the present invention will be described with reference to the following drawings, which are not to a fixed scale unless otherwise specified. [Brief explanation of the drawing]

[0058] [Figure 1] A diagram showing a medical imaging device for imaging biophysical target systems. [Figure 2] Figure 1 shows the MRI imaging modalities that can be used with the apparatus shown in Figure 1. [Figure 3] Block diagram of a system that controls MRI operation based on gating. [Figure 4] A diagram showing a block diagram of a gating system as intended herein, which can be used in the configuration of Figure 1 in an embodiment. [Figure 5] Figure 4 shows the time curve of the measurement and the transformation of such measurement to illustrate the operation of the gating system. [Figure 6] Figure 4 shows the time curve of the measurement and the transformation of such measurement to illustrate the operation of the gating system. [Figure 7] A diagram illustrating window processing that can be used in the embodiment of the gating system shown in Figure 4. [Figure 8] A diagram showing signal characteristics that can be used in the proposed gating system. [Figure 9] A flowchart of a computer-implemented gating method, which can be used particularly in medical imaging of biophysical target systems. [Figure 10] A diagram illustrating the proposed method and the effectiveness of the gating system in the context of MRI. [Modes for carrying out the invention]

[0059] Herein, we refer to the schematic block diagram of Figure 1 showing the components of the medical imaging apparatus MIA. As intended herein, the medical imaging apparatus MIA uses a new type of gating managed by a gating component or gating system GT. In embodiments, the gating system GT is operable to synchronize the operation of the medical imaging apparatus IA ("imager") with measurements collected by the sensor device S. The measurements form measurement signals s, which are specifically transformed as intended herein. The gating system GT operates on the thus transformed measurement signals for better robustness and accuracy of synchronization, as will be described in more detail below.

[0060] The medical objective supported by the gating component GT is to obtain good quality images ε of the target biophysical system TS of the patient PAT. The target biophysical system TS may be an organ, anatomical structure, tissue, or any of the aforementioned groups on or within the patient PAT. For example, the target biophysical system TS may include the heart of a human or animal patient PAT in cardiac imaging. The following examples are, in fact, primarily drawn from cardiac imaging, but it should be understood that the principles described herein are also applicable to other medical imaging tasks that are not related to the heart. As will become clear below, non-medical applications may not be excluded herein. Nevertheless, the gating principles disclosed herein have been shown to work particularly well in cardiac imaging systems. This is because the principles appear to correspond well to the structural and / or dynamic characteristics of target biophysical system TS, such as the human heart. The gating component GT helps to ensure image quality ("IQ") because the target biophysical system TS may be subject to changes (such as motion), which, if not considered, can degrade the IQ. For example, such movements can cause image blur or other image artifacts. The gating system GT is configured to facilitate the avoidance of such IQ degradation.

[0061] Sometimes, when attempting to acquire images using an imaging device (IA) of such a biophysical system (TS), it may be necessary to monitor a surrogate biophysical system (SGS) that is functionally related but different from the target system (TS).

[0062] The imaging device IA contemplated herein is preferably configured for non-invasive imaging. A surrogate biophysical system SGS can be monitored by a appropriately positioned sensor device S, which preferably remotely and non-invasively acquires the measurement signal as a time-series measurement signal s=s(t). Thus, the time-series measurement s (time-series signal) is time-domain data. The time-domain measurement signal s thus acquired is used by a gating component GT to synchronize a specific imaging task, for example, to trigger data acquisition by the imaging device IA, thereby acquiring data (reconstructed, etc.) of the target system TS from which an image of the target system TS can be acquired. The above-described use of the time-domain measurement signal s for data acquisition synchronization by the imager IA is an example of prospective gating, which is primarily contemplated herein. However, other types of gating, particularly retrospective gating, are not excluded herein and are equally contemplated. Such retrospective gating may include tagging streams of data ("frames") acquired by the imager IA based on the measured signals s. However, the primary focus of this specification is prospective gating.

[0063] As briefly stated above, the situation contemplated herein is that the biophysical target system TS undergoes changes as schematically shown in Figure 1 by the dotted circle and double arrow. That is, it is assumed that the biophysical target system TS transitions through multiple states, such as in a periodic or at least quasi-periodic manner, but aperiodic methods of such transitions are not excluded herein. Changes may occur during or between data acquisitions, which, if not due to the operation of the gating system GT, can cause data inconsistencies and therefore image artifacts. The motion of the target physical system TS is an example of such a change and is primarily contemplated herein. For example, in the context of motion, such a state change may be a change in the position and / or shape of the target system TS when it transitions from one state to another. For example, one state change may relate to deformation. Thus, the target system TS may expand or contract. In addition, or instead, the target system TS may be translated and / or rotated, or in fact, undergo changes corresponding to any two or all of the aforementioned combinations. Changes may occur repeatedly or only once. However, while the proposed principle primarily concerns states and changes of motion type, such physical motion is not necessarily intended in all embodiments herein. Thus, metabolic or physiological changes, in addition to or instead of motor functions, are also intended herein. Such changes may be measurable by functional imaging such as SPECT / PET, MRI, and other imaging modalities IA. Such images may be possible down to the cellular level, subcellular level, or even the molecular level (which may be assisted by the use of contrast agents), and such are also intended herein in additional or alternative embodiments.

[0064] As an example, continuing with cardiac imaging, the cardiac TS undergoes multiple states during the cardiac cycle, including, for example, systolic or diastolic states. In this regard, we will use the term "state" rather than "phase," as we wish to reserve the term "phase" for specific aspects of signal processing, as will be discussed later. Generally, gating systems (GT) allow for the consideration of transitions between states in the human heart. Otherwise, image quality may be degraded by blurring and other artifacts, and in some cases, the image ε may even become undiagnostic. Data acquisition in prospective gating is triggered based on measurement data collected by the surrogate system (SGS) at predefined moments when a specific state of interest is expected to appear in the target system (TS). On the other hand, if states / state changes are not considered, it may be necessary to re-execute the imaging, which leads to increased burden on the equipment, patient, and staff, as well as additional costs. All of this can be avoided with a gating system (GT). In an embodiment of prospective gating, the proposed gating system GT enables the synchronization of imaging operations, particularly the triggering of data acquisition, based on measurement signals s acquired by a sensor device S of a surrogate biophysical system SGS. Thus, measurements in the surrogate biophysical system SGS can be associated with a specific state of interest in the target TS. The target state itself may be the object of interest, for example, when the goal is to image a region of interest (ROI) in that state, or the target state becomes the object of interest at the point in time when data acquisition / tagging is initiated to image a specific aspect of the target (not necessarily limited to the state of interest). This makes it possible to consider data acquisition latency in particular. Thus, in cardiac imaging, the R peak may be the same state of interest for imaging the systolic state of the heart, but the R peak may also be used as the state of interest for gating, for example, when it is desired to image other states of the heart using a specific pulse protocol in MRI, which may result in some latency.

[0065] The sensor device S may be configured as a PPG camera and may be used to acquire video images of a specific patch of the skin SGS of a patient PAT. The skin patch is subject to fluctuations (pulsations) of blood accumulation. Such pulsations cause color changes in the image between frames, which can be confirmed by using a appropriately sensitive optical sensor within the PPG camera. The PPG signal can be understood as representing the vibration (time-dependent) of skin reflections as a response to changes in blood circulation / volume in the skin patch. As shown in Figure 1, a skin patch on the forehead of a patient PAT has been found to yield good results. The pulsations are caused by blood circulation driven by the heart. Specifically, a pulsation is a state in which the filling and depletion of blood caused by the pumping action of the heart alternate, which produces different optical colors (spectrums) in the image recorded over time. Such PPG video images have been found to adequately capture such changes in state (coloration) of the skin patch. Thus, such a skin patch has been found to be a good surrogate biophysical system SGS for the cardiac system as a biophysical target system TS. The changes in state observed in skin patch SGS correspond well to the various states that cardiac TS transitions through.

[0066] For example, cardiac charging states, which can be recorded by ECG tracing, have been shown to correlate with blood volume states in skin patches, transitioning between and between depletion (low blood volume) and accumulation (high blood volume). Low blood volume states in skin patch SGS can be measured as a trough in the appropriately coded PPG signal s(t). The onset of cardiac systole, generally identified by the R peak in the electrocardiogram trace, has been shown to correlate well with low blood volume states in the PPG measurement signal.

[0067] Generally, the gating system GS preferred as used herein is a known or predictable, preferably stable, functional correlation between the target system TS and the surrogate system SGS. In other words, just as the target system TS is assumed to undergo state transitions, the surrogate system also undergoes state transitions as it experiences associated state changes. However, the state of the surrogate system SGS can be entirely different in nature from the state of the target system TS or its changes. For example, in cardiac imaging, the cardiac state may relate to a specific spatial configuration or charge of the myocardium. For example, the state may be complete or partial contraction, or complete or partial compression, while the associated state of a skin area measured by a sensor S device may relate to a change in color. The sensor S device may, for example, use a suitable CCD (charge-coupled device) sensor component.

[0068] This specification broadly proposes high-precision extraction of biomarkers b based on time-series measurements s acquired by a sensor device S, using components of a gating device GT (described later), and utilizing this for gate processing purposes such as triggering image acquisition and tagging acquired data. More specifically, good results have been obtained by first converting the time-domain measurement signal s into a time-domain phase representation of the signal, and then extracting biomarkers b from there.

[0069] While biomarker b itself may sometimes directly represent a desired state of the target system, this is not always the case. However, it is sufficient if there is a known, modelable, predefined, and sufficiently stable relationship between the occurrence of the biomarker and the state of interest of the target system to be imaged. For example, the occurrence of biomarker b t *However, the occurrence of the state of interest in the target system may occur with a delay or before it occurs. However, it is desirable that this delay or precedence between the two be modelable, known in advance, and temporally stable (low or no jitter), so that the gating operation can be reliably performed. Also, since the state of interest may persist for a certain period of time, gating to that state is possible if the delay is sufficiently small. The proposed processing configuration is not complex, so real-time processing is facilitated. For example, as found herein in some embodiments for cardiac imaging, the state of interest is the charged state of the heart, specifically the occurrence of the R peak in the ECG, i.e., the onset of the systolic state. For example, the low blood volume state, such as the corresponding biomarker b, a low blood volume state as imaged by the sensor device S on a skin patch, and s represented in the transformed time-domain phase signal T(s), is generally known to occur after the moment of the R peak. The representation of the low blood volume biomarker in the time-domain phase presentation is remarkably prominent, which enables reliable extraction. In addition, the instantaneous delay between R peaks of biomarkers is sufficiently low, which is useful for MRI (see Figure 10 below for more details). Furthermore, the aforementioned delay Δ has been found to be remarkably stable in time-domain phase signals. The delay can be accounted for by modeling an appropriate time offset as needed, which may be added to determine a gating signal (such as a data acquisition trigger for imager IA). Thus, the image is obtained at time t'=t * At +Δ, i.e., a time offset can be added to the time when the biomarker is detected. The delay Δ between the R peak and the biomarker can be modeled separately herein. The moment t of biomarker b * However, if it occurs after the state of interest, as in the case of the R peak for cardiac imaging using PGG, the instantaneous t * Since it is known to be low enough to trigger image acquisition, it can still be used to trigger it.

[0070] The delay may be assumed to be constant across the patient population, or it may be derived from previous measurements individually calibrated for a given patient. The delay can be learned from machine learning or by any other method. For example, simple low-order models using one, two, or three parameters have been found to be sufficient to model this delay Δ (see Figure 8 below for more details).

[0071] Before describing the operation of the gating system GT as intended in more detail herein, the imaging setup MIA is first described with respect to the imaging device IA and the measuring sensor device S for the surrogate system SGS, in order to aid in the subsequent description of the gating system GT.

[0072] First, looking more generally at a medical imaging device MIA, the imager IA has a detection device DD and a signal source SS. The imager IA is operable and configured to operate in the detector device DD of a target system TS using the signal source SS and data λ to acquire data λ. The data λ thus acquired can be used to acquire an image ε of the target system TS. A stream of image data λ ("frames") can be acquired, and a corresponding stream of such image ε can be acquired from such data λ. The imager is preferably of the tomographic type, and acquiring an image ε from the acquired data λ (projection domain data) may include converting from projection domains to image regions, for example, by using a reconstruction algorithm performed on a computing platform RECON. A surrogate sensor device S may include such a signal source in some embodiments, but in other embodiments, ambient signals may be used. Thus, in some embodiments, the sensor S does not include such a signal source SS because it uses ambient signals such as PPG or the like, while the imager IA includes such a signal source SS.

[0073] In any case, the sensor device S can use its sensor components to convert the characteristics of interest of the surrogate system SGS into time-domain measurement signals s. The surrogate sensor device S may be image-based itself, but this is not required in all embodiments. In any case, the measurement principle, transducer mechanism, and image (if any) acquired by the sensor device S are generally different from the data acquired by the IA imaging device and image ε. The sensor measurements s acquired by the sensor device S may be of a completely different nature in terms or clinical meaning / appearance, contrast, etc. For example, in some embodiments, the imager IA is configured for MRI imaging, while the sensor device S is configured for contact or preferably non-contact / remote PPG imaging.

[0074] Next, the surrogate biophysical system measurement sensor S device (also referred to herein simply as the "(surrogate) sensor S (device)") will be described in more detail, which may have a transducer sensor component (not shown) for acquiring raw data s' and optionally a preprocessor configured to preprocess the raw data s' to acquire measurement signals s used by the gating system GT. The preprocessing may be performed on the device S, or the preprocessing may be offloaded to an external computing device such as the gating system GT itself, or another computing device of the medical imaging device MIA, or beyond.

[0075] As mentioned above, the sensor device S may be configured as a PPG camera. However, other camera types configured for visible light or for other (invisible) parts of the electromagnetic emission spectrum are also contemplated herein. In fact, other camera types such as infrared (IR), near-infrared (NIR), or LIDAR, depth sensing, or any other may be used.

[0076] In yet another embodiment, the sensor device includes a nuclear imaging device or other imaging device configured for functional, metabolic, molecular, or other types of imaging. The sensor may be an imaging device such as an imager IA, but may also be of a different modality than the imager IA. For example, if the imager IA is an MRI, the sensor device S may be, for example, a CT scanner.

[0077] The surrogate sensor device S does not necessarily have to be image-based, or even radiation-based, depending on the nature of the surrogate signal s to be measured and the necessary correlation with the states that the target TS can take during the transition process.

[0078] For example, in an alternative embodiment of sensor S, this may be an acoustic sensor such as an ultrasonic sensor. In another alternative embodiment, sensor S may instead be configured for mechanical measurement, such as ballistic measurement. An example of this is ballistocardiography, in which the pumping action of the heart is associated with a mechanical rebound impulse that can be measured. For example, sensor device S may be positioned as a appropriately sensitive scale device, an electromechanical sensor (e.g., a piezoelectric element), or an optical sensor such as a radiation-based, photoelectric sensor, on which the patient is placed. Sensor device S may be embedded in a chair or bed, stretcher, etc., on which the patient is sitting or lying. Sensor device S propagates through the body and responds to the rebound action caused by the pumping action of the heart. Alternatively, a depth-sensing camera can be used to measure the rebound action by tracking the movement of the abdominal or chest wall (e.g., up and down when the patient is in a supine position). Other types of sensors contemplated herein include those configured for any of the following: echocardiography, apical impulse testing, electrocardiogram, dynamic electrocardiogram, and others. For an overview of such techniques, please refer to Laurent Giovangrandi et al. in "Ballistocardiography - A Method Worth Revisiting", published in Conf.Proc.IEEE, Eng Med Biol.Soc, pp 4279-4282 (2011).

[0079] In addition, acoustic or RF sensors S that respond to cardiac or respiratory status may be used. One example is a pilot tone (PT) setup, which is based on the principle that patient movement (i.e., due to respiratory or cardiac activity / state) can cause coil-dependent fluctuations in signal amplitude. A dedicated transceiver or transmitter / receiver setup can be used to send and receive dedicated RF signals within the bore of the imager IA to enable interaction with the patient PAT.

[0080] However, a dedicated hardware setup is not always necessary in all cases. As in the case of MRI, the existing coils can be controlled and used to send and receive dedicated motion detection signals, distinct from the RF pulses used for MRI imaging. Similar setups can be used, but based on ultrasound or sound waves at other frequencies that respond to patient movement.

[0081] However, while other sensor configurations are certainly envisioned in one embodiment of this specification, image-based configurations, such as PPG (photoplethysmography) cameras, are of primary interest here, and their ease of implementation and cost-effectiveness are also important factors. Therefore, the following description will focus primarily on such embodiments, but it should be understood that these are illustrative and do not exclude other mentioned embodiments of the sensor S.

[0082] Instead of measuring blood volume or related volume, such as skin reflectivity of the light spectrum on a skin patch in PPG, other surrogate systems SGS using different sensors S can be used when imaging, for example, the respiratory or digestive system as the target TS. In lung imaging, a spirometer setup S can be used to measure lung condition. Other such setups may include an elastic belt with associated pressure monitoring sensors that can be adapted on the chest. Alternatively, measurement markers placed on the patient's chest wall can be used, which are observed by a camera. Imaging of the chest wall without specific markers is also planned, for example, using a depth-sensing camera as the sensor S, which has the advantage of being non-contact. In imaging tasks related to the digestive system, peristaltic manometry can be used to enable gated imaging of the motility of certain parts of the digestive system (esophagus, stomach, intestines), although this may be invasive.

[0083] Referring more precisely to the imager IA, its signal source SS generates a signal that interacts with patient tissue, such as a query signal, in order to generate a response signal that is measured by the detector device DD as detector data λ, and from this response signal, the medical image ε of the image region can be obtained.

[0084] For example, in a CT set, during an imaging session, the signal source has an X-ray source SS that rotates with the patient around the examination area ER to acquire projection images from different directions. The projection images are detected by a detector device DD, in this case an X-ray sensitive detector. The detector device DD can rotate opposite the examination area using the X-ray source SS, but such co-rotation is not always necessary, as in the case of CT scanners with 4th generation or higher generation. The signal source SS, such as an X-ray source (X-ray tube), is activated so that the X-ray beam exits from the focal point within the X-ray tube during rotation. The beam traverses the examination area and the patient tissue within it, interacting with it and causing corrected radiation. The corrected radiation is detected as intensity by the detector device DD. The detector device DD is coupled to an acquisition circuit, such as a DAQ unit, to capture the projection image in the digital domain as digital data λ. Radiation imaging operating only in the projection domain is not excluded herein.

[0085] In embodiments of MRI, the signal source SS is formed by a high-frequency coil that, in receiving mode, can also function as a detector device DD configured to receive high-frequency response signals emitted by a patient present in a magnetic field. Such response signals are generated in transmitting mode in response to the preceding RF (radio frequency) signal transmitted by the coil. However, in some embodiments, instead of the same coil used in the transceiver device being used in the different modes, there may be dedicated transmitting and receiving coils.

[0086] As another example, in radiographic imaging, the signal source SS is present in the patient in the form of a previously administered radioactive tracer that emits radioactive radiation that interacts with the patient's tissue. This interaction results in a gamma signal that is detected by a detection device DD, in this case a gamma camera, which is preferably positioned in a ring around the examination area where the patient is located during imaging.

[0087] In preferred embodiments, imaging of other organs, such as cardiac imaging, lung / chest imaging, or abdominal imaging, is performed using, for example, an MRI imager IA or CT, or nuclear imaging. In some embodiments, ultrasound ("US") imaging modality IA is also contemplated herein.

[0088] The patient PAT can take any position during the operation of the imager and / or sensor S, and therefore the patient may be standing, lying down, or squatting, depending on the clinical practice, protocol, and imaging modality used.

[0089] As an example, an MRI setup IA as intended herein is schematically shown in Figure 2. In MRI (and CT), the patient PAT is slid, for example, on a patient table PT, into the bore BR of the imager IA, which is defined by the housing HS. The bore BR defines the examination area ER of the imager IA where the patient PAT or at least the region of interest RO (head, arms, heart, etc.) is located during imaging. "Imaging" is also referred to herein as "data acquisition," and both terms are used interchangeably herein.

[0090] The examination area (ER), and therefore the 3D space within it occupied by the patient (PAT), can be conceptually considered to consist of a grid of spatial points (x,y,z). The imager (IA) acquires the detector data λ during the data acquisition / imaging operation, from which image values ​​v can be obtained by reconstruction performed by the reconstruction device (RECON). The reconstruction device (RECIN) may have a computing device that executes a reconstruction algorithm. The algorithm can be applied to the acquired detector data λ. The acquired detector data λ can also be referred to herein as "projection data" λ, in contrast to the reconstructed image ε within the image area. Thus, the reconstruction algorithm assigns image values ​​v to voxels, acquires a set of spatially distributed voxel image values ​​v(x,y,z), and thus constructs a 2D, 3D, or 4D image ε of a region of interest (ROI) for a given imaging task, i.e., a target biophysical system (TGS) such as the heart or another organ of interest (or group of organs / anatomical structures).

[0091] In prospective gating, data acquisition is initiated when an appropriate control signal is received via an appropriate control interface SL, based on a trigger signal established by the gating system GT. Most conveniently for cardiac imaging in MRI, the surrogate biophysical system SGS may be a skin patch on the patient's forehead or one of the temples, as needed. Such a surrogate system SGS is easily accessible to a line-of-sight sensor device S. The sensor device S can be mounted inside or outside the bore BR, as shown in Figure 2. The sensor can be configured to be mounted at the end of the bore BR, or, for example, on an external stand, the wall or ceiling of the examination room, and its field of view ("FOV") is adjusted to capture the surrogate system SGS, such as the patient's forehead, while the patient PAT is inside the bore during the imaging session.

[0092] The basic operation of an MRI imager is based on triggering a suitable pulse sequence by causing one or more main coils GC to emit high-frequency signals at the appropriate time based on timing signals provided by the gating system GT. Furthermore, a movable RF coil SC may be positioned within the bore BR, closer to the patient's ROI. Inside the housing HS, the main magnetic field coil MG is located. The main magnetic field coil can generally be a solenoid configuration for generating a main B0 magnetic field oriented along the Z direction parallel to the central axis of the scanner bore BR. The main magnetic field coil is typically a superconducting coil located within a cryogenic shroud, but a resistive main magnet may also be used. It can be used. Superconducting magnets are particularly preferred at higher magnetic field strengths, and therefore higher frequencies. Figure 2 shows a closed cylindrical design, but this is not a requirement herein, as open-design MRI scanners with U-shaped magnets are also intended. However, the principle of what is proposed herein is particularly beneficial for closed-design MRI scanners because the patient space is limited, as will become clear below.

[0093] The housing HS may further house or support a gradient coil GC for selectively generating a magnetic field gradient along the Z direction and / or along in-plane directions transverse to the Z direction (such as along the Cartesian X and Y directions), or along other selected directions.

[0094] The housing HS further houses or supports a high-frequency head or body coil MC (referred to herein as the main coil) for selectively exciting and / or detecting magnetic resonances. While birdcage coils are common below 128 MHz, in addition to birdcage coils, other coils, such as transverse electromagnetic (TEM) coils, phased coil arrays, or any other type of high-frequency coil, can be used as volume transmitting coils. The housing HS typically includes a cosmetic inner liner that defines the scanner bore BR. Roughly speaking, the main magnetic field MG causes alignment of hydrogen nuclei (protons) in the tissue within the magnetic field B0. When the RF pulse is switched off, the disrupted nuclei relax to return to alignment and are therefore transmitted during their relaxation, and the resonant pulse is picked up by the coil MC in receiving mode as projected data λ (in this case, RF). The contrast in such MRI images corresponds to the spatial distribution of protons (hydrogen nuclei) in the examination area (ER), and therefore changes on or within the patient's body / tissue. Imaging of other odd-numbered Z elements (having odd-numbered protons and neutrons) can also be performed as needed. However, while imaging of hydrogen concentration (its protons) is very common, this is merely for illustrative purposes, and MRI based on response signals from other elements with unpaired nuclei, such as 13C, 14N, 19F, or 31P, is also intended herein.

[0095] The MRI pulse signal can be initiated based on the timing at which the extracted target biomarker b is established by the gating system GT, based on the measurements s acquired by the camera S.

[0096] Next, the pulse sequence can be started, taking into account possible preparation time, to acquire appropriate projection data λ over a period of time as the response RF signal picked up by the RG coil as described above. Thus, the RF coil MC may be switchable between transmit and receive modes. Alternatively, two sets of coil RF, i.e., receiver and transmitter, are used. A set of gradient coils GC can be used to spatially locate the resonant response signal in the examination area ER. From the acquired RF resonant projection data λ, a cross-sectional or volume image ε in the image area of, for example, a human heart in a desired state (e.g., systole), can be reconstructed by the reconstructor RECON. This can be repeated over multiple cardiac cycles until sufficient projection data λ is acquired. Thus, as described in relation to Figure 1 above, the cross-sectional image / image volume ε can be acquired by reconstructing from projection data λ acquired at appropriate moments synchronized with changes in cardiac state, using the measurement value from the sensor S of the surrogate system SGS as an indicator. The MRI reconstruction algorithm may include the implementation of the inverse 2D Fourier transform, or iterative reconstruction may be used. In CT, filtering back projection (FBP), or other approaches such as iterative, algebraic, or statistical approaches can be used.

[0097] Here, refer to Figure 3, which briefly summarizes the imaging operation synchronized with the operation of the MRI image gating component GT. In transmit mode, the RF coil MC operates to send high-frequency pulses into the examination area for interaction with protons in human tissue present in the image area, and the RF coil MC is switched to receiver mode to receive resonant response signals from the protons. The response signals are data acquired during the scan operation of the imager IA, from which a cross-sectional image ε of the spatial distribution of hydrogen protons can be reconstructed by the reconstructor RECON. The gradient coil GC has a different function. The gradient coil GC operates for the spatial localization of the resonant response signals provided by the RF coil MC. The gradient coil GC and RF coils operate in cooperation according to the scan timing protocol. At the appropriate timing, as specified in the imaging protocol or imaging task, one of the three sets of spatial gradient coils CZ, CX, and CY of the spatial gradient coil GC and the appropriate current are applied to the RF coil to generate excitation pulses. Switching of the main coil or surface coil is performed via a control interface CL that operates based on timing signals received from the gating system GT. The Gating System GT can be integrated into an Imager IA, for example, the Imager IA's operating console OC, particularly for prospective gating. For retrospective gating, the Gating System GT can be integrated into a workstation computer system or any other computing system.

[0098] Multiple pulse protocols are used by the main coil MC (and / or local coils placed on / on the patient's body) to construct the excitation RF pulse. In some cases, particularly in cardiac MRI, such an imaging sequence may include an imaging window that includes a prepulse, a waiting time called a preparation delay / latency, and the actual acquisition pulse. The preparation delay depends on the physical properties of the tissue to be imaged, and its length is usually a predetermined constant that cannot be changed.

[0099] Next, referring to Figure 4, which shows a block diagram of the Gating System GT, we will now explain the Gating System GT in more detail.

[0100] The surrogate sensor S device is operable to acquire measurement signals from a surrogate biophysical system SGS as time-series measurement values ​​s=s(t). The measurement values ​​s may be the state / quality of the surrogate system and may include images / videos, sound signals, scale measurements, etc. The signals are collected remotely and preferably non-contact / non-invasively. In a preferred embodiment, a video feed of the patient PAT from or relating to the surrogate biophysical system SGS is collected. In some embodiments, the time-series measurement values ​​s=s(t) are based on a video feed of images acquired by the sensor device S of a skin patch, such as in the PPG setting described above. Thus, the sensor device S may have a PPG camera. The raw measurement values ​​s'=s'(t) acquired by the sensor can be pre-processed to obtain a signal s=s(t) to be processed by a gating system GT. Pre-processing can be performed in the sensor device S, by a pre-processor module (not shown) mounted on the gating system GT, or by such a module located elsewhere. Preprocessing may include adjustments such as A / D conversion, amplification, signal shaping, or other preprocessing to derive a well-defined measurement signal s=s(t). However, in other embodiments, it is the raw signal that is further processed by the gating system GT, and therefore such preprocessing (other than A / D conversion) is not required in all embodiments herein. In embodiments involving such preprocessing, particularly in relation to PPG or other image / video-based embodiments, the pixel values ​​in a given acquired frame are averaged, weighted averaged, or otherwise combined to arrive at a single scalar value s for each instant t, s(t), thus obtaining the measurement signal s=s(t). Needless to say, vector signals that are not of scalar type are also not excluded herein.Appropriate conditioning procedures are described in W Wang et al. in "Fundamentals of camera-PPG based magnetic resonance imaging", published in IEEE Journal of Biomedical and Health Informatics, vol 26(9), pp 4378-4389 (2022).

[0101] In general, as detailed below, processing by the gating system GT can be based on the measured signal s=s(t) at each instant t. Alternatively or additionally, processing may be based on a time interval of a buffered signal over a specific time period, and the gating system GT will use the buffered value for any output generated at the output port OUT of the gating system GT. The TIFF2026514926000002.tif1779 is processed in one go. The time section of the time-series signal can define a window w, which is further defined below. In other embodiments, the entire time-series signal is received at the input port IN of the gating component GT, which is then processed in retrospective gating, etc., but this is less preferred herein since real-time processing is primarily intended. Accordingly, the proposed system GT is preferably configured for real-time processing so that the signal s(t) enters the input port IN over time t. Such (quasi) real-time processing, or processing per moment t, or processing via (short) buffering over appropriately short periods is preferably intended herein. Such real-time processing is preferred herein for prospective gating, as is primarily intended herein. As detailed below, the processing includes a transformed version Ts(t) of the signal s, and the above-described processing per moment t or buffering / windowing processing can also be applied to the transformed signal Ts(t).

[0102] For example, a suitable PPG or other image / video signal can be constructed from raw signals s using region of interest localization and / or automated skin patch identification, as described in Wang et al. literature. In some embodiments, the construction of signals s is based on a two-step approach: in the first step, candidate PPG signals are created for each patch. The suitability of each candidate is determined, and then, as in the second step, a weighted average of the candidates (based on suitability) is performed to construct the final PPG signals s. Suitability can be quantified by some metric or objective function, depending on the task at hand. Alternatively, other methods of combination may be used instead of weighted average. The two-step approach is not limited to PPG signals and can instead be used for other spatial or non-spatial images or non-image data, such as those collected by a sensor S.

[0103] Preprocessing of the signal s may further involve encoding the raw signal s', which can affect the "semantics" of the measured values ​​that make up the signal s. For example, in PPG, the measured value can vary depending on the blood volume at the measurement site SGS. Different types of encoding can be attempted. For example, in some encodings, a higher signal value s(t) at a given instant t corresponds to a higher blood volume, while in some other (inverse) encodings, a higher signal value corresponds to a lower blood volume, and so on for other types of measured values.

[0104] The converter TF operates on a time-domain signal s and converts it into a time-domain representation of the signal's phase. The time-series signal Ts(t) thus converted from the phase of signal s is then processed by a biomarker extractor BX from which biomarkers b representing or associated with a desired state of the target system TS are extracted. Such extraction is particularly relevant when the time-series changing signal satisfies defined extraction conditions t. *Monitoring may be included for the following: For example, in one embodiment, the transformed signal T(s)(t) is thresholded in a thresholding operation against a threshold such as zero in order to enforce a zero-crossing extraction policy. However, other such extraction policies can be considered equally, depending primarily on the numerical range of the transformed signal T(pl), its coding, etc. The extraction process may involve averaging values ​​across a window w (below), and the processed values ​​are one or more values ​​T thus averaged or otherwise combined, which are then thresholded.

[0105] While the useful transformations performed by the converter TF are known to be based on the Hilbert transform (s)(t), as primarily intended in the embodiments, other transformations are also intended herein that can transform the original time-domain signal measurement s(t) into another time-domain signal T(s)(t) that represents or relates to the global phase of the signal s. The global phase can be thought of as describing how the various periodic components, such as the frequency components that make up the component signal, relate to each other. The converter TR will be described in more detail below.

[0106] Optionally, filter components such as an upstream filter FLU and / or a downstream filter FLD (relative to the transducer TR) exist to smooth each signal for better performance. For example, low-pass filtering can be used to remove a signal (s or T(s)) or to at least suppress high-frequency noise components that are known in some cases in the measurement or that may be introduced as artifacts by the transducer TR itself. In embodiments, such filtering is either upstream (see upstream filter component FLU) or downstream (see downstream filter component FLD), but not both. However, in other embodiments, both upstream UFL and downstream filtering DFL are used. When both filter instances are used, each low-pass filter may be used, but other filter combinations are not excluded herein. Generally, the filter components UFL, DFL contemplated herein are configured primarily for noise filtering.

[0107] An arbitrary window W can be used to define the processing window w within the transformed signal train T(s)(t) as described above. The biomarker extractor BX processes the transformed numerical values ​​T(s)(t)t within this window, as will be described in more detail below. The extractor BX can operate instantaneously or at intervals of multiple instants within the window w. The extraction process can be based on averaging, weighted averaging, or any other method of combining the information found in each window w. The window can be moved over time. As described above, the extraction policy can be formulated with respect to thresholding the phase values ​​T(s) thus processed (averaged, etc.) found within the timing window w. The window is related to an empirical instant t and extends into the past from a given instant t. Therefore, a holding buffering circuit may be required. The proposed window settings are not complex so that real-time processing is guaranteed. Specifically, the method used to extract markers (positions) is not complex as described herein. The length of the window does not need to extend across the entire time-series signal s.

[0108] Subsequently, the biomarker b instance extracted herein may be output at the output port OUT, used for various tasks including display on a display device DD, stored in memory MEM or a database DB, or used in prospective gating, as is often intended herein. The output of the biomarker b instance may, in particular, include outputting a trigger moment / timing, i.e., the time when the biomarker was extracted / discovered, or simply a signal indicating that the extraction was performed. Thus, the trigger moment t when the biomarker b is extracted can be used in the control interface CL to trigger the acquisition of projection / detector data λ. * , or its indicator signal. Therefore, for some or each occurrence of biomarker b, the imaging / acquisition operation of imager IA can be triggered. Thus, a series of biomarker moments t *It is provided by the gating system GT and can trigger each data acquisition operation by appropriately interfacing with the control circuit of the imager IA via the control interface CL. For example, an MRI pulse sequence can be sent by issuing a trigger signal appropriately synchronized based on the occurrence of the biomarker b. For example, the LG pulse sequence can be triggered based on an established instance of the biomarker b.

[0109] In some embodiments of cardiac imaging, the state of the target system TS indicated by the biomarker is the state of the R peak (the start of the systolic state). Other extraction policies may enable the extraction of different biomarkers representing different states of the target system TS, such as the start of the diastolic state required by the current clinical task.

[0110] As described above, the gating system GT may not necessarily depend on the exact timing at which the biomarker b is extracted (although such embodiments are not excluded). Instead, the gating system GT takes into account the subsequent occurrence of the biomarker regarding the state of interest of the target system at the output OUT or elsewhere, and applies a time offset Δ added to the time t at which the biomarker b was extracted. * For example, an appropriate offset can be added to the timing of the biomarker instant t. * However, in some embodiments related to cardiac imaging (such as other ones like MRI or CT), the biomarker b found in this specification consistently occurs after the start of the (next) R peak state / systolic state. However, since it has been found in this specification that the delay is sufficiently low, the biomarker based on the time-domain phase signal can be beneficially used for gating, especially in cardiac imaging (MRI, CT, or others).

[0111] To describe the biomarker b of interest in more detail, it has been found that it can be usefully interpreted as a zero (or other threshold) crossover of the instantaneous phase T(s), or another global phase representation in the time domain. The phase representation in the time domain can be obtained, for example, by applying a Hilbert transform to the original measurement signal s, as is actually intended herein in embodiments. In the context of PPG, and given the corresponding code of the signal s, it has been found that the zero crossover of the time-domain phase represents the R-peak state of the myocardium of interest in the cardiac image.

[0112] Here, we will describe the operation of the signal converter TF in more detail. From a time-domain PPG or other measured signal, the analysis signal can be calculated in the complex plane over time as follows: TIFF2026514926000003.tif15155 Here, t 2 = -1, and operator H represents the Hilbert transform. Generally, the analyzed signal does not have negative frequency components in the f-domain, whereas the measured signal, when represented in the f-domain, generally shows how such negative frequency components occur.

[0113] The time-domain phase signal (such as an instantaneous phase signal) based on the analysis signal A of the PPG or other measurement signal can be calculated by taking the phase of the analysis signal, following (1): TIFF2026514926000004.tif13152

[0114] Therefore, the phase argA(s) is one of the complex numbers (generally having a non-zero imaginary component along the imaginary axis i). s=s(t) is a signal over time, and the phase T(s)(t)=[argA(s)](t).

[0115] The PPG signal can be described as a harmonic sequence with the principal amplitude at the fundamental frequency. The phase locus is expected to resemble a monotonically increasing function, and is essentially nearly linear, with the mean derivative corresponding to the fundamental frequency and taking place in the range [-π, π]. Thus, any given value φ corresponds to the state of cardiac contraction. Therefore, by solving φ(s)(tk)=k, the MRI trigger t * The value from which =tk can be derived can correspond to the maximum amplitude in the time-domain signal, and thus proved to be a suitable threshold for detection. This has been verified in practice. The delay between the ECG R peak and the instantaneous phase zero crossing is stable for a given patient, which makes this approach suitable, for example, for cardiac MRI triggering.

[0116] The converter TR can operate dynamically at each time moment t, and therefore the converted signal is calculated at each time moment t (at each or several current time moments t0).

[0117] Therefore, as shown in equations (1) and (2) above, the transformed signal T(s) generated by the transformer TF is, in some embodiments, an argument on the complex plane in the analytic signal representation of the signal s, based on the Hilbert transform of the signal s or on any other such transform.

[0118] A time-domain phase representation T(or more) can be conceptualized as a phasor entity that traces the phase angle in a complex plane relative to a reference position such as the real or imaginary axis (3 o'clock or 12 o'clock position). The analytical signal or phase vector encapsulates all the information in the original time-domain signal into complex numbers. By encapsulating the original time-domain signal s in this way, or mapping it to a two-dimensional space such as the complex plane, the information is "distributed" across its entire dimension, and the global topological aspects of the time-domain measurement signal are sufficiently explicitly revealed. This can also be seen from the time curves shown in Figures 5 and 6. Such diffusion into at least two-dimensional space can also be attempted by using transformations other than the analytical signal based on the Hilbert transform.

[0119] Figure 5a) shows an ECG trace with an R peak. The ECG trace represents the true state of the target system, and the R peak ("R" in the figure) represents the onset of the systolic state, and therefore low blood volume at surrogate site SGS (e.g., skin patches such as the forehead and temples). The time curve b) shows time-domain measurements s, i.e., without transducer TR operation. Moments of low blood volume (blood depletion, indicated by small circles in curve b) have been found to be useful biomarkers associated with the R peak in the target system. However, due to jitter, there is usually a variable time offset Δ between the two (the R peak and the moment of low blood volume at the surrogate SGS). Therefore, the offset may not be equal between instances, which makes reliable gating difficult in the signal representation in Figures 5a) and b).

[0120] As shown by the time curves in Figures 6a) and 6b), the situation improves thanks to the transformer TR. Curve a) again shows the electrocardiogram waveform including the R wave, while curve b) shows the waveform transformed into instantaneous phase (global phase in the time domain) based on the analytical signal conceptualized by the Hilbert transform. Thus, Figure 6b) can be understood as a transformation of the PPG signal s (or other measured signal, not necessarily PPG) in Figure 5A. Here, the instantaneous phase zero crossing in the surrogate system SGS (shown as "X" in Figure 6b), and the R peak of the target system TS are at a defined constant offset Δ over time. This enables consistent and reliable gating, resulting in improved image quality, reduced wear on imaging equipment, improved patient comfort, and increased image processing throughput.

[0121] In this specification, the ECG traces shown in Figures 5A and 6A are used solely for the purpose of illustrating ground truth, and it will be understood that the proposed gating system GT can be used as an alternative to cumbersome and interfering ECG methods, whether PPG-based or surrogate measurement-based. ECG instruments and methodologies significantly disrupt the imaging workflow, particularly in closed-bore systems such as some MRI (see Figure 2) or CT imager IA designs. Instead, the proposed system GT, with its transducer TR, can replace ECG-based gating systems, achieving blind gating that does not require ECG.

[0122] In the case of PPG versus R-wave peak, as shown in Figures 5 and 6, zero crossing in the phase-time domain representation consistently occurs after the R-wave peak. The same consistent situation may apply to other types of measurements other than the R-wave peak, or to other features of the QRS complex. As previously stated, zero crossing is merely one example, and thresholding depends on how the signal s is encoded (see preprocessing above). For example, a high value in the phase domain may represent a low blood volume or otherwise. Any particular form of signal encoding / construction of the s signal may be used herein, as long as it is consistently used. Thus, in some such embodiments, instead of zero crossing, thresholding for non-zero values ​​may exist, if necessary, to find biomarkers b in the time-domain phase T(v) for gating purposes. Note that even when non-zero thresholding is used, this situation can be reformulated as zero crossing by shifting it. Therefore, the description of the principles disclosed herein using zero-crossing extraction policies does not limit the generality of this disclosure.

[0123] Figures 7a) to 7c) show the window processing W as previously described. As can be seen, Figure a) again shows the ECG trace, and the curve in Figure b) shows the low-pass filtered version of curve a). Figure 7c) shows the time-domain phase representation derived from the analysis signal related to the Hilbert transform, or from any other suitable transform TR. As can be seen, as we approach the current time t0, the signal degrades, which relates to how the time-domain phase is calculated in some embodiments and / or how the filtering FLD operation works, if applicable. Accordingly, it is proposed herein to configure the window w such that its endpoint expires before the current time t0 and is outside the gap or safety margin between the current time and the expiration of the window w. Any one, two or more, or all of i) transform TR, ii) zero crossing, or more generally feature extraction, iii) filtering FLD may be performed within the window interval. Operations such as extraction, filtering, transform, etc., or other processing may then use one or more phase values ​​within the window w as needed.

[0124] The window width defines the intensity of smoothing over instantaneous phase. Shorter windows result in higher temporal resolution, while longer windows are advantageous when the phase calculation TR is noisy. A window width of approximately 130–170 ms has been found to provide a good trade-off. Midpoints of the window or other localizers can be selected as a trade-off between the robustness the system can support and the maximum delay. Preferably, localizers for use in MRI sets are adapted to the acquisition sequence. The longer the delay, the further the localizer moves away from the so-called "edge effect" at t0 of real-time processing, as shown in Figure 7. The window can be dynamically adapted over time. The window can enable improved real-time processing, particularly in prospective gating, as primarily intended herein in certain embodiments.

[0125] Specifically, in the case of a transducer TR, phase recovery and / or filtering outside the safety margin are sufficiently stable and may be beneficial to average within this window w to make them more robust. Therefore, in the proposed setup, a system with windowing has two additional parameters: the position / center point of this analysis window w (the system delay at which a trigger is detected) and the window width for smoothing outliers.

[0126] When used by a converter TR, the numerical calculation of the Hilbert transform may be based on the Fourier transform of the original signal s into the frequency domain, with frequency shifts of + / 90° for the negative and positive spectral component frequencies, respectively, and also by the inverse Fourier transform into the time domain. The Fast Fourier Transform (FFT) algorithm can be used for computational efficiency. In other embodiments, the Hartley transform can be used to compute the Hilbert transform. See, for example, RN Bracewell, "The Fourier Transformation and Its Applications", Chapter 13, 3rd edition, McGraw Hill, (2000).

[0127] In some embodiments, as a result of the conversion TR, positive frequencies are doubled and negative frequencies are removed from the time-domain measurement signal s.

[0128] In this regard, it should be noted that the representation of a time-domain phase signal with respect to the Hilbert transform can be understood as conceptual. Therefore, the Hilbert transform itself does not need to be explicitly computed, as in embodiments based on the Fourier transform. Thus, the time-domain phase representation (such as an analytic signal) can be obtained by Fourier or other methods without using the Hilbert transform as an intermediate form. Nevertheless, computational approaches that rely on implementing them in equations (1) and (2) are also considered, and therefore, in practice, such explicit computation of the Hilbert transform may be necessary.

[0129] Here, we refer to Figure 8, which shows features that can be used for delay Δ prediction with respect to PPG (or other) waveforms s. As described above, the delay is between the occurrence of the extracted biomarker b and the state of the target TS of interest, which is associated with biomarker b.

[0130] As described above, some MRI acquisition pulse sequences require a preparation pulse (and therefore additional time). For example, such a preparation pulse may be required in the late diastolic state of an LGE sequence. The same applies to some systolic-centered imaging protocols. Therefore, the prediction of the (expected) delay Δ (time difference) between the ECG R peak and the zero-crossing PPG trigger is considered herein.

[0131] In Figure 8, the dashed line represents the moment of the R peak, while the dotted line represents the moment of zero crossing in the time-domain phase (e.g., instantaneous phase). To completely replace the ECG modality, the estimation of the delay Δ should preferably rely solely on data from the PPG signal ("features"). It has been found that it may be possible to derive a low-rank model to estimate this delay for a given patient PAT using two or more PPG features (e.g., two or three). Some such features are shown in Figure 8 and referred to in more detail therein. Again, the delay Δ is shown for the PPG signals s, but as mentioned above, this is not limiting and can be applied to other blood volume measurements s or other measurements that are not necessarily related to blood volume.

[0132] The proposed marker b can not only be accurately derived from the time-domain phase signal Ts = T(s), but the relationship between the phase of interest and the target state (e.g., R peak) can also be predicted using only PPG data. When the positive zero crossing Z is used as biomarker b, the delay Δ can be accurately predicted by using appropriate features of the time-domain measurement signal s (e.g., PPG signal) and the associated time-domain phase signal T(s). A non-extensive description of these features is shown in Figure 8, where the depth d is defined as the ratio between the range of the PPG waveform and its average level.

[0133] Features F and D indicate the rising zero crossing and falling zero crossing of the PPG signal, respectively. M is the maximum value of the PPG waveform, and Z indicates the rising zero crossing of the associated phase signal, such as the biomarker b which is preferably used herein, although other biomarkers may be used instead, as mentioned above.

[0134] Using only one or two of these features, an accurate prediction of Δ can be obtained. In one embodiment, the time difference between the phase marker Z and the maximum value M of the PPG signal s can be used as an estimate. In an embodiment, a second PPG feature is used. For example, this second PPG feature can be obtained as i) the time difference between the descending zero crossing and the ascending zero crossing in the PPG signals (F and D), or i) the time difference between the maximum PPG value and the ascending zero crossing in the PPG signals (M and Z), or iii) the time difference between the phase marker and the ascending zero crossing in the PPG signal.

[0135] In embodiments, Δ can be predicted using any two linear combinations of these parameter pairs. Other combinations of features using three or more features are also contemplated. Generally, a small number of features, such as one, two, or three, are used for better responsiveness. Preferably, fewer than five are used.

[0136] By determining a suitable shift and (optionally, scaled) time difference between the phase marker Z and the maximum PPG signal M, a slightly less accurate prediction can be obtained. The above features can be extracted from the PPG curve in the preparation / calibration phase, whether single or paired, and collected from pre-imaging of the patient PAT to establish a set of estimated delay Δj, which can then be averaged or otherwise combined to estimate the delay Δ.

[0137] The delay Δ may be calculated in advance and then used for a given patient or another patient in imaging for the gate. In some embodiments, the delay is a time-domain phase-based estimate t such as zero crossing. * It can be used to refine it. However, in some embodiments (for example, in relation to the R peak, t * Delays can be used to confirm delays (for example, when they occur after a state of interest). For example, the delay in cardiac MRI can be confirmed to be sufficiently low for a given patient, and as a result, the intended MRI pulse sequence fits well in the patient's IBI (see Figure 10 below for more details on this). If this is the case, a confirmation signal can be issued by activating a transducer such as a display device, lamp, or speaker. Alternatively, or in addition to this, the confirmation signal is used as an additional gating criterion. Thus, data acquisition is triggered when biomarker b is extracted / discovered and it is found that the delay is sufficiently low. Thus, delay calculations are performed dynamically and repeatedly at some or all moments when a biomarker is extracted, rather than as a one-off. Alternatively, a warning signal is issued. By grasping the delay Δ, it may also be possible to align the acquired data at the trigger with the R peak or state of energization for further improved ECG blind synchronization. Calculating the delay is optional but preferred in some embodiments.

[0138] The delay can be estimated individually for each patient during the calibration phase, which can be done quickly and does not disrupt the clinical workflow.

[0139] As seen in Figure 8 (and later in Figure 10), using a time-domain phase-based gating method, for example, the delay Δ between the zero crossing of the time-domain phase and the R-peak moment is small. Time-domain phase-based biomarkers b (such as the zero crossing) can occur before or after the R-peak. However, since they are known to be close enough to the R-peak, they can be used to our advantage herein. For example, the delay Δ is very small in the proposed setting, and for many MRI pulse sequences (even those requiring a preparation pulse), the entire sequence fits into the IBI.

[0140] Accordingly, the gating system GT may include a delay estimator module DEM that specifically estimates the delay Δ between the biomarker b moment and the moment of interest in the target system TS using one of the feature pairs as described above. Alternatively, the delay may be estimated based on historical ECG data from the patient or other patients. As previously mentioned, machine learning (ML) can be used, for example, to predict the delay based on patient characteristics. Such a delay Δ may be established from clinical experience, or, as herein it may be assumed, it may be known by either method. Alternatively, preferably, a low-order model such as a linear model or averaging can be used to predict the delay as described above.

[0141] The proposed estimation of delay Δ for time-domain phase-driven gates is useful because it enables retrofitting for current gate purposes and existing (instruction) data acquisition operations, such as MR pulse sequences. For example, in sequences such as LGE (Late Gadolinium Enhancement), the goal is to obtain images of a specific cardiac state. Therefore, there currently exists a table that defines a specific trigger delay amount (e.g., in milliseconds), and the system needs to wait for this delay after an observed feature (such as the aforementioned R-wave peak). However, the delay Δ achievable with the proposed time-domain phase (based on PPG or other, rather than the originally intended ECG) differs from the originally intended R-wave peak trigger. This may be because, for example, the proposed PPG or other measurement signal replaces the ECG setup, and the R-peak is no longer directly observed. Thus, knowing the delay Δ estimated herein for the R-peak for a specific biomarker b allows for the "as is" reuse of a good number of existing stocks of older MRI sequences by simply (preferably automatically) adapting the initially defined trigger delay. This makes it possible to integrate the proposed method more effectively into existing clinical workflows or imaging settings.

[0142] Next, refer to the flowchart in Figure 9 illustrating the steps of the gating operation described above, based on the time-domain phase representation of the measurement signal s acquired with respect to the surrogate system. This method can be used in prospective or retrospective gating operations in connection with imaging of the target system by an imaging device. A change in state in or of the target system TS is intended to be functionally related to a change in state in or of the surrogate system. The change in state in or of the surrogate system is measured by the measurement signal s. This method is based on using an appropriate transformation to convert the initial measurement time-domain signal s into the aforementioned time-domain phase representation of s. The time-domain phase representation T(s) thus obtained can then be used in place of or in addition to the original signal s. The time-domain phase signal has been found to be useful for reliable extraction based on an appropriate biomarker b as described above, which can then be used for gating, for example, when controlling the image data acquisition operation with respect to the relevant target system TS.

[0143] In step S905, a surrogate system or raw data time-domain measurement s'=s'(t) of the surrogate system is collected. The raw data is preferably collected remotely and non-invasively by using visible or invisible light, ultrasound (US), IR, or NIR, but non-radiation modalities such as scale or piezoelectric measurements in ballistic (rebound) applications are also intended.

[0144] In any step S910, the raw data may be preprocessed to obtain a preprocessed time-domain signal s=s(t) of the measured values, which is to be further processed herein. Preprocessing may include adjusting or averaging the raw data per instant (e.g., spatially) to the signal s at instant t, or combining them in other ways. Preprocessing may further include encoding the thus preprocessed measured values ​​s collected in relation to a surrogate system, which are then received in step S920 for gating purposes.

[0145] In step S930, there is an optional filtering step, such as low-pass filtering, to remove noise components from the received signal s(t).

[0146] In step S940, the optionally filtered measurement signal s is transformed to obtain a time-domain phase representation T(s(t)) = Ts(t) by calculating the instantaneous phase based on the Hilbert transform of the time-domain signal s(t). An analytical signal representation may be used. In step S940, the Fourier method may be used. In all embodiments herein, it may not be necessary to explicitly calculate the Hilbert transform.

[0147] In step S950, an optional first or second filtering step (if the first filtering step S930 is used) is performed to filter the time-domain phase representation signal T(s). Again, or here, low-pass filtering may be used. Both filtering steps, i.e., the upstream step S930 and the downstream step S950, are optional, or one or both may be used as needed.

[0148] Regardless of whether it is filtered or not, the time-domain phase representation signal T(s) is monitored, and in step S970, a biomarker is extracted from the time-domain phase signal T(s)(t) in an extraction event according to step S940. Such an extraction event occurs when the monitored signal T(s)(t) satisfies one or more conditions according to an extraction policy. A threshold, such as zero crossing, may be one such policy. If the signal satisfies the policy, it can be said that the extraction event has occurred.

[0149] Optionally, the extraction process may be based on window processing of the time-domain phase representation signal in step S960. Thus, the extraction in S970 can be based on multiple or a single value of T(s)(t) buffered in window w.

[0150] In step S980, the time t during which biomarker b is extracted in this manner * The output is generated and made available for further processing as needed. Therefore, time t * This is the time of such an extraction event. Instead of, or in addition to, the timing, another indicator signal is output, such as an agreed number ("0" or "1", or any other number), or any other data item, such as a pulse agreed to indicate an extraction event.

[0151] In particular, in step S990, the gating operation is performed at the timing t issued in step S980. *Alternatively, this may be done based on other extracted event indicator signals. Optionally, the method may include a delay estimator step S985, or the step may be part of the output step S980. The delay estimator generates an estimate Δ of the delay between the time of occurrence of the extracted biomarker and the intended state of the gated target TS. The processing may be based on the moment of occurrence of one or more features converted into measured signals s and / or time-domain phase representations Ts. A linear combination based on simply two or more such features may be used to estimate the delay. The delay Δ can be calculated at any time, not necessarily between steps S990 and S980. The delay can be used / considered in the gating operation. The delay time is pre-calculated in a preparation phase, for example. This may be calculated once and used for multiple (e.g., similar patients), or it may be calculated individually for a given patient. Once the delay is calculated, it is displayable. The delay may be compared to the data acquisition waiting time of the imaging device, and if the delay is too long, or if a data acquisition operation is not issued for the biomarker extracted at that moment, a warning signal is issued. Thus, the delay calculation S985 can be used as a watchdog for each moment or several moments whenever a biomarker is extracted in S970. Only if the delay is found to be sufficiently low, data acquisition is triggered in step S990. In addition to, or instead of, the estimated delay is used to adapt existing data acquisition commands that have their own pre-specified trigger delays, which were not originally intended for use in the proposed time-domain phase signal-based gating. For example, the estimated S985 delay can be used to adapt such existing trigger delays, for example, by addition, subtraction, or replacement of the existing trigger delay with the estimated delay Δ. Thus, an existing stock of MRI pulse sequences originally intended to be driven by an ECG signal can be reused in a gating configuration driven by the proposed time-domain phase representation Ts(t).Therefore, this method may further include the step of adapting such existing data acquisition commands and using them in the following gating operation S990.

[0152] The gating operation S990 may include, for example, controlling (e.g., triggering) the data acquisition operation of the imaging device with respect to the patient during the predicted gating. During such a gated data acquisition operation, projection data λ can be acquired (e.g., CT, MRI, nuclear imaging, ultrasound imaging), from which an image of the target state of the target TS can be acquired. In addition, or alternatively, in a retrospective gating embodiment, the processing therein may occur during extraction time t * Based on (optionally considering a delay Δ), the process may include tagging the stream λ of data acquired by the imaging device with the tag "κ". The tag can be incorporated as metadata, such as in the header file of individual frames. Other processing steps may, if necessary, extract time t for the tagged frames and / or biomarker b. * This may include storing, displaying, or otherwise processing the data. As described above, an acquisition operation (such as an MRI pulse sequence) may have its original trigger delay adapted by an estimated delay (S985), which is the thus adapted data acquisition operation used in the gating step S990.

[0153] In additional steps (not shown), the method may include reconstructing an image within an image region from acquired or tagged image data λ. The thus reconstructed image may represent a target system in a desired target state associated with an extracted biomarker.

[0154] This specification acknowledges that the phase characteristics of the fundamental wave derived from PPG measurements at the forehead or other locations are a kind of average characteristic that ignores the details of the waveform s. However, the systolic and diastolic portions of the cardiac cycle are known to respond differently to changes in IBI. This is not the case when considering only the phase response of the fundamental wave; such a phase characteristic becomes linear. Therefore, this specification prefers to leave other frequencies in the signal for processing by a gating system, as this allows for precise timing to associate several PPG markers b (from the fundamental) with the R peak position, as well as using the fundamental spectral frequency components of the original signal s. Accordingly, filtering out all such frequency domain components other than the fundamental wave is not recommended here, as it jeopardizes such timing.

[0155] Figure 10 illustrates the beneficial effects of the gating system GT and method described above in the context of MRI. The lower panel is a timing chart of the data acquisition operation AO in MRI. The data acquisition operation AO consists of an instruction including a pre-pulse / preparation pulse PP and an acquisition pulse AP, with a modality-specific latency or preparation delay L between the two. Without the proposed system GT and method, the duration of the acquisition operation AO may not fit entirely within the length of the introductory biomechanism (IBI), which is undesirable. This is because it could hinder gating to states of interest, such as the systolic state SS and diastolic state DS of the heart, i.e., the target TS, and impair the IQ. However, by triggering the acquisition operation AO based on a time-domain phase signal (e.g., its zero-crossing "X" or other thresholding), the delay Δ becomes sufficiently small, and the entire duration of the acquisition operation AO fits within the length of the IBI. Thus, the trigger point X / biomarker b is not only reliable but also close enough to the systolic state SS to ensure sufficient time for the acquisition operation AO to complete within the IBI.

[0156] Thanks to the proposed time-domain phase gate, the R peak and trigger point t *The delay Δ between the two is very low, allowing a good number of MRI pulse protocols to be adapted to IBI. Therefore, the proposed method is applicable to all MRI pulse protocols that can be adapted in this way, especially thanks to the low delay Δ.

[0157] The components of the Gating System GT can run as one or more software modules on one or more general-purpose processing unit PUs, such as workstations associated with the Imager IA, or on server computers associated with a group of imagers.

[0158] Alternatively, some or all components of the gating system GT may be arranged as hardware such as a appropriately programmed microcontroller or microprocessor, for example, an FPGA (Field-Programmable Gate Array), or a hardwired IC chip or application-specific integrated circuit (ASIC) incorporated into the imaging system IA. In other embodiments, the gating system GT can be implemented in part in software and part in hardware.

[0159] The different components of a gating system GT can be implemented on a single data processing unit (PU). Alternatively, some or more components can be implemented on different processing units (PUs), potentially located remotely within a distributed architecture and connected within an appropriate communication network, such as a cloud setup or client-server setup.

[0160] One or more features described herein may be configured or implemented as a circuit encoded in a computer-readable medium, or using a circuit and / or a combination thereof. The circuit may include discrete and / or integrated circuits, a system-on-a-chip (SOC), and combinations thereof, a machine, a computer system, a processor and memory, and a computer program.

[0161] In another exemplary embodiment of the present invention, a computer program or computer program element is provided, which is configured to perform a method step of a method according to one of the above embodiments on a suitable system.

[0162] Accordingly, the computer program elements may be stored in a computer unit which may be part of an embodiment of the present invention. This computing unit may be configured to perform or induce the execution of the steps of the method described above. Furthermore, the computing unit may be adapted to operate each component of the apparatus. The computing unit may be configured to operate automatically and / or to perform user instructions. The computer program may be loaded into the working memory of a data processor. Accordingly, the data processor may be equipped to perform the method of the present invention.

[0163] This exemplary embodiment of the present invention encompasses both computer programs that use the present invention from the outset and computer programs that modify existing programs to use the present invention through updates.

[0164] Furthermore, the computer program element may provide all the steps necessary to satisfy the procedure of the exemplary embodiment of the method described above.

[0165] According to a further exemplary embodiment of the present invention, a computer-readable medium such as a CD-ROM is presented, and the computer-readable medium has computer program elements stored thereon, which are described in the preceding section.

[0166] Computer programs may be stored and / or distributed on suitable media (in particular, non-temporary media, but not necessarily) such as optical storage media or solid-state media supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0167] However, computer programs may be presented on a network such as the World Wide Web and can be downloaded from such a network into the working memory of a data processor. According to a further exemplary embodiment of the present invention, a medium for making a computer program element available for download is provided, and this computer program element is configured to perform a method according to one of the aforementioned embodiments of the present invention.

[0168] It should be noted that embodiments of the present invention are described with reference to different subject matter. In particular, some embodiments are described with reference to method-type claims, and other embodiments are described with reference to apparatus-type claims. However, those skilled in the art will understand from the above and below descriptions that, unless otherwise noted, any combination of features belonging to one type of subject matter, as well as any combination of features relating to different subject matter, are disclosed in this application. However, all features can be combined to provide a greater synergistic effect than the simple sum of the features.

[0169] Although the present invention has been illustrated and described in detail in the drawings and the foregoing description, such illustrations and descriptions should be considered illustrative or descriptive and not limiting. The present invention is not limited to the disclosed embodiments. Other variations of the disclosed embodiments can be understood and achieved by those skilled in the art in carrying out the claimed invention from a consideration of the drawings, disclosure and dependent claims.

[0170] In the claims, the words “comprising” do not preclude other elements or steps, and the indefinite articles “a” or “an” do not preclude plurality. A single processor or other unit may perform the functions of several items mentioned in the claims. The mere fact that certain means are mentioned in different dependent claims does not imply that combinations of these means cannot be used advantageously. No reference numeral in the claims should be construed as limiting in scope. Such reference numerals may consist of numbers, letters, or any combination of alphanumeric characters.

Claims

1. A gating system for acquiring data on a target biophysical system using an imaging device, An input interface capable of receiving a time-domain phase representation based on an analysis signal of a time-domain signal of a measurement of a surrogate biophysical system, which can be obtained by a radiation-based sensor device, wherein the radiation is non-ionizing; A biomarker extractor capable of extracting at least one instance of a biomarker that can be associated with the state of the target biophysical system from the time-domain phase representation in at least one extraction event, For data gating operations, an output interface that provides an indication of the at least one extracted event, A gating system having [a specific feature].

2. The gating system according to claim 1, wherein the data gating operation is prospective or retrospective, the prospective gating operation includes triggering a data acquisition operation by the imaging device based on the provided marking of the at least one extraction event, and the retrospective gating operation includes tagging the data acquired by the imaging device based on the provided marking of the at least one extraction event.

3. The gating system according to claim 1 or 2, wherein the sensor device can be configured to operate non-contact and / or remotely.

4. The gating system according to any one of claims 1 to 3, wherein the time-domain phase representation of the phase can be calculated by a converter capable of being expressed as a Hilbert transform of the time-domain signal of the measured value.

5. The gating system according to any one of claims 1 to 4, wherein the gating system has a filter capable of low-pass filtering the time-domain signal of the measured value and / or low-pass filtering the receivable time-domain phase representation.

6. The gating system according to any one of claims 1 to 5, wherein the extractor is capable of performing such an extraction based on thresholding, and / or the extractor is capable of performing the extraction based on processing data of the time-domain phase representation across a time-domain window.

7. The gating system according to claim 6, characterized in that the endpoint of the window is located in a time domain with a margin away from the current time in the time domain phase representation.

8. The gating system according to any one of claims 1 to 7, further comprising a control interface capable of triggering an acquisition operation by the imaging device based on the provided indication of the at least one extraction event.

9. The gating system according to any one of claims 1 to 8, wherein the imaging device is a tomographic type, a magnetic resonance type, or a projection area type.

10. A) The target biophysical system or the surrogate biophysical system comprises one or more of i) the cardiac system, ii) the respiratory system, iii) and the digestive system, and / or B) the surrogate biophysical system comprises an imageable patient skin patch, according to any one of claims 1 to 9.

11. The gating system according to any one of claims 1 to 10, wherein the sensor device is configured to sense radiation such as visible light, IR, NIR, laser, LIDAR, or ultrasound, or to sense acoustic signals, mechanical pressure, or force.

12. A gating system according to any one of claims 1 to 11, i) the imaging device, and ii) one or more of the sensor devices, An imaging device having the following features.

13. A gating method for acquiring data on a target biophysical system using an imaging device, The steps include receiving a time-domain phase representation based on an analysis signal of a time-domain signal of a measurement of a surrogate biophysical system, which can be obtained by a radiation-based sensor device, wherein the radiation is non-ionizing, The steps include: extracting at least one instance of a biomarker that can be associated with the state of the target biophysical system from the time-domain phase representation in at least one extraction event; For data gating operations, the steps include providing an indication of at least one extraction event, A method of having.

14. A computer program that causes at least one processing unit to perform the method according to claim 13, when executed by the processing unit.

15. At least one computer-readable medium storing the computer program described in claim 14.

Images