A method and system for quality assurance testing of medical ultrasound scanners and probes

Abstract

The invention provides a method of remote quality assurance for ultrasound systems, the method comprising: receiving from the user at a remote location an ultrasound image of a test object; processing the received image to determine quality assurance parameters of the ultrasound system under test; providing a notification to the user at the remote location the results of the quality assurance determination. The invention provides a method by which QA fault detection of medical ultrasound can be performed in a simple and scalable manner. A system and method is provided, in which QA encompassing fault detection can be performed remotely, easily, reliably and at low cost.

Description

[0001] A Method and System for Quality Assurance Testing of Medical Ultrasound Scanners and Probes

[0002] The present invention relates to a method and system for ultrasound quality assurance testing. In embodiments the invention relates to a cloud-based method and system for automated remote quality assurance testing. In embodiments, the invention also relates to a low-cost phantom accessible to both technical and non-technical users for use in such quality assurance testing. As used herein the expression Quality Assurance encompasses fault detection in the equipment under test.

[0003] Quality Assurance (QA) testing of all medical ultrasound devices is required by professional bodies such as British Medical Ultrasound Society BMUS, Institute of Physics and Engineering in Medicine IPEM, The American Institute of Ultrasound in Medicine AIUM, The European Federation of Societies for Ultrasound in Medicine and Biology EFSUMB and many more across the world. Historically, ultrasound QA has been performed by a medical physicist using an expensive and complex test object. Testing typically takes 3-4 hours on the hospital site and requires a Physicist or Engineer to visit the scanner location, test the scanner and write a report. A shortage of qualified staff and confusion on the difference between service and QA leaves some organisations non-compliant.

[0004] Handheld ultrasounds are becoming increasingly more popular. It is expected that in years to come such devices will become common for medical professionals in the same way as, stethoscopes are currently. These devices may be referred to as "pocket ultrasound" devices and the use of them has increased significantly in recent years.

[0005] It is estimated that 11,000 units were shipped worldwide in 2018 and 37,000 in 2021 , with a predicted total of 59,000 sales in 2025 (OMDIA, 2021). This popularity Is not surprising given that they have a wide range of applications, are simple to use and are inexpensive.

[0006] The availability of ultrasound imaging is a positive outcome for patients and for medical professionals alike. However, it presents a problem regarding compliance with standards and access to QA. On the one hand, traditional cart-based ultrasound is the fastest growing imaging modality, and handheld ultrasound devices are being distributed rapidly worldwide. On the other hand, the number of skilled Ultrasound Physicists and Engineers is dwindling. The result is an ever-growing gap in quality assurance testing and ultimately patient safety.

[0007] Generally, Diagnostic Ultrasound QA is a series of routine user and physics tests and checks that can be performed to monitor aspects of performance that are considered likely to change or deteriorate and importantly, may affect clinical efficacy. Typically, such tests can be performed daily, monthly, and annually. Testing can be performed to assess uniformity, crystal dropout, sensitivity and other image quality characteristics of the ultrasound device.

[0008] Uniformity in ultrasound can be generally understood as the response or image produced being homogenous when the tissue being imaged is the same. A lack of uniformity can manifest in for example a line through an image due to a faulty component or non-functioning element which can result in image artefacts and / or affect Doppler measurements.

[0009] In this context, sensitivity can be defined as the ability of the transducer elements to detect and identify weak returning echoes from background noise in an imagescanner system. A reduction or loss of sensitivity will result in reduced penetration and resolution.

[0010] The present applicant specializes in ultrasound QA and has been active in this technical area for ten years. However, it has recognized that there is no technically reliable (and cost-effective) method of testing that can be scaled or used with the large increase in numbers of active pocket ultrasound devices. Current Ultrasound QA methods are expensive and are not economically viable for handheld ultrasound users where the devices are relatively inexpensive. However, without the testing and the QA provided, users cannot be assured their device is safe and ultimately patients being scanned cannot rely on the images and data derived from scans that are undertaken using the machines or systems. There is a shortage of ultrasound physicists means they are only found in larger hospitals (in the UK, over 26% of current physicists are reaching retirement IPEM 2016). Traditional cart-based ultrasound continues to grow faster than any other imaging modality and there is a need to increase physicists to meet this demand. This is further compounded and complicated by the introduction of handheld devices, which are sometimes owned by the users and used both for private practice and within hospitals and the community. The management of these devices is crucial for patient safety (RCR 23)

[0011] Despite the already significant popularity of pocket ultrasound devices, the currently available methods and systems for QA do not provide an economically viable way to test handhelds and inexpensive ultrasound systems.

[0012] EP4191267 discloses a non-transitory computer readable medium storing instructions executable by at least one electronic processor to perform a method of monitoring a quality assurance (QA) procedure performed using a medical device. The method includes receiving a signal indicating a start of the QA procedure; analyzing video of the medical device acquired after receiving the signal to detect one or more errors during the QA procedure; and providing a remedial action addressing the detected one or more errors. The system is arranged such that a user performs the QA test. It is a system that focusses on MRI and CT QA typically in hospitals and clinical settings.

[0013] The method is focussed on getting the user to do the test correctly so that the inputs to the machine under test can be investigated.

[0014] US2021192720 discloses methods and systems for improving the image quality of ultrasound images by automatically determining one or more image quality parameters via a plurality of separate image quality models. A method is provided for an ultrasound system including determining a plurality of image quality parameters of an ultrasound image acquired with the ultrasound system. Each image quality parameter is determined based on the output from a separate image quality model. Feedback is provided to a user of the ultrasound system based on the plurality of image quality parameters.

[0015] The system uses changes in contrast, speckle and contrast in clinical regions of interest (ROI), to try and assist a user in ensuring they have captured an acceptable clinical image.

[0016] WO2023228149 discloses a bidirectional feedback system for remote spatial positioning correction of a robotic arm for ultrasound scanning. The system comprises a first robotic arm for ultrasound scanning; a second robotic arm for mirroring the first robotic arm; a first and second display; an electronic data processor configured for: receiving ultrasound scan images; sending the received ultrasound scan images to the two displays; mirroring the relative spatial positions of the first and second robotic arm. Sensing a first relative spatial position from the first robotic arm, and moving the second robotic arm to the first relative spatial position, has higher priority than sensing a second relative spatial position from the second robotic arm, and moving the first robotic arm to the second relative spatial position. A method and use of the system for remote hands- on training is also disclosed. This is said to be preferably for medical training, and more preferably for ultrasound training.

[0017] The document refers to automatic evaluation of the quality of ultrasound images, which does not relate to QA but rather relates to system performance, i.e. the quality of the taken image with the arm.

[0018] A paper by Fathi et al, entitled “Quality Control of real-time B-mode ultrasound scanners in Sudan / Khartoum” published in the International Conference on Scientific Research and Innovation 2023 discloses methods and systems for eventuating the performance of real-time B-mode ultrasound scanners (in Khartoum). Image quality evaluation was carried out for 22 ultrasound scanners in government institutions, private hospitals, and clinics in Sudan / Khartoum using 403 GS RMI ll / S phantom.

[0019] According to a first aspect of the present invention, there is provided a method of remote quality assurance for ultrasound systems, the method comprising: receiving from the user at a remote location an ultrasound image of a test object, an ultrasound image of the device scanning “in-air” and some camera photos of the physical condition of the ultrasound device; processing the received ultrasound images automatically to determine quality assurance parameters of the ultrasound system under test; providing a notification to the user at the remote location the results of the quality assurance determination.

[0020] In an embodiment, the method comprises providing the user with a test object for use with the method of remote QA. The user is preferably provided with a low-cost and easy-to-use test object for generating the ultrasound image of the test object.

[0021] The present method and system a thus a user-led distributed QA system, arranged which uses a simple and low-cost test object. This enables the low-cost and broadly available ultrasound QA and testing. This contrasts markedly with known and conventional ultrasound QA that is performed by a medical physicist using an expensive and complex test object

[0022] The invention provides a method by which QA fault detection of medical ultrasound can be performed in a simple and scalable manner. As explained above the growth of the use of medical ultrasound is very much underway. In addition there is a shortage of skilled medical physicist able to travel to perform fault detection QA on medical ultrasound equipment. Even if there were enough medical physicists for the growing use of medical ultrasound as an imaging mode, the present inventor shave recognized the environmental pressure to limit travel. The present invention, in which fault detection QA can be performed remotely, easily, reliably and at low cost addresses all of these technical problems.

[0023] In an embodiment, the method enables removal of the requirement for skilled interpretation of in-air reverberation, or uniform test object images by using automated image analysis and machine learning to identify faults and recommend a course of action (as explained in greater detail below).

[0024] In an embodiment, the method comprises communicating captured images to the remote location from the user using a personal transmission device. In an embodiment, the personal transmission device is intrinsic to the ultrasound system or is provided by a mobile telephone separate from the ultrasound system.

[0025] In an embodiment, the method comprises performing defined QA tests on images received at the remote location and providing results to the user.

[0026] In an embodiment, the method comprises storing results for the user and the tested ultrasound system at the remote location.

[0027] In an embodiment, the tests are selected from the group consisting of uniformity, drop-out, reverberation threshold, reverberation depth, B-mode noise levels, grey-level pulsed wave Doppler noise threshold, colour Doppler noise threshold, cable noise, signal to noise ratio, calliper accuracy / distance measurement, resolution measurement and penetration depth (sensitivity).

[0028] In an embodiment, the test object is silicon-based.

[0029] In an embodiment, the test object has interface portions shaped to engage an ultrasound transducer for testing.

[0030] In an embodiment, the test object has at least three interface portions, each shaped differently to engage with a respective different type of ultrasound transducer for testing.

[0031] In an embodiment, the test object has a cylindrical cavity which houses removable cylindrical inserts of varying attenuation. An anechoic insert will allow measurement of B-Mode noise levels and signal to noise ratio (see the example of Figure 3B).

[0032] In an embodiment, the processing is done in real time to determine the one or more quality assurance parameters of the ultrasound system under test. In an embodiment, a report is generated indicating whether or not the system under test satisfies QA requirements.

[0033] In an embodiment, the system under test includes an ultrasound probe.

[0034] In an embodiment, the system under test includes an ultrasound machine in combination with the probe.

[0035] In an embodiment, the method comprises generating baseline QA parameters for the pairing of the probe and the ultrasound machine using only 3 image / video files

[0036] In an embodiment, the method comprises baselining of the probe in combination with plural ultrasound machines.

[0037] In an embodiment, the system under test comprises an imaging chain including at least a probe and an ultrasound machine and in which the QA is performed for the entire imaging chain.

[0038] In an embodiment, the QA testing time is less than 10 minutes.

[0039] In an embodiment, the QA testing time is less than 5 minutes.

[0040] In an embodiment, the method comprises use of machine learning and artificial intelligence to detect faults of an ultrasound scanner or probe via the in-air reverberation pattern. In a preferred embodiment the use of machine learning and Al enables fault detection in an ultrasound probe or piece of equipment under test. Machine learning and Ai enables the detection of fault based on an in-air reverberation which would otherwise not be easily detectable.

[0041] In an embodiment, the method comprises use of machine learning and artificial intelligence to detect faults of an ultrasound scanner or probe via imaging a uniform test object. In a further preferred embodiment the use of machine learning and Al enables fault detection in an ultrasound probe via the imaging of a uniform test object. If uniform test object is imaged the image generation should be correspondingly uniform or at least correspond to some detected and expected outcome. Use of machine learning and Al enables the detection of fault would otherwise not be easily detectable. A test object such as that shown in and described herein with reference to Figures 3A to 3C can be used.

[0042] In an embodiment, the method comprises comparison between in-air reverberation images and images of a uniform test object to detect, verify and assess the clinical impact of a fault with an ultrasound scanner or probe.

[0043] According to a second aspect of the present invention, there is provided a testing system for remote quality assurance for an ultrasound system under test, the testing system comprising: a data store for receiving from a user at a remote location an ultrasound image of a test object, an ultrasound image scanning “in-air” and camera photos of the physical condition of the device; a processor to process the received image to determine quality assurance parameters of the ultrasound system under test and to provide a notification to the user at the remote location of the results of the quality assurance determination.

[0044] In an embodiment, the system comprises a test object for imaging with the ultrasound system under test.

[0045] In an embodiment, the system comprises an App on a computing device to prompt a user to capture an image of the test object and provide the image to the data store.

[0046] In an embodiment, the computing device is a smart phone, computer or a PDA.

[0047] According to a further aspect of the present invention, there is provided a testing system for quality assurance for an ultrasound system under test, the testing system comprising: a data store for receiving from a user an ultrasound image of a test object; a processor to process the received image to determine quality assurance parameters of the ultrasound system under test and, in real time, automatically to generate a QA report in respect of the system under test.

[0048] A system is provided that includes remote testing of ultrasound equipment. The remote and simple nature of the testing enables users without technical expertise to automatically identify faults and assess performance of Medical Ultrasound Scanners and transducers remotely. The quality assurance testing referred to herein relates to quality assurance in the sense of fault detection in relation to probes and equipment. It is noted that quality assurance is used by some to refer to assurance of quality in an image as regards its content due, say, to actions of an inexperienced sonographer. As will be explained below, the remote and simple nature of the testing enables users without technical expertise to automatically identify faults and assess performance of Medical Ultrasound Scanners and transducers remotely in less than 10 minutes. It is preferred that the system and associated application is operated as a cloud-based system. This enables operation of the process and automated method in effect anywhere to be done on the same quick and simple time scale.

[0049] The method in embodiments supports a decentralised model of ultrasound equipment quality assurance and testing by enabling a non-technical user to perform the test regardless of location or environmental conditions.

[0050] In an embodiment, the testing system is arranged to process in real time to determine the one or more quality assurance parameters of the ultrasound system under test.

[0051] In an embodiment, the testing system is arranged to generate a report indicating whether or not the system under test satisfies QA requirements.

[0052] In an embodiment, the system under test includes an ultrasound probe. In an embodiment, the system under test includes an ultrasound machine in combination with the probe.

[0053] In an embodiment, the method comprises generating baseline QA parameters for the pairing of the probe and the ultrasound machine.

[0054] In an embodiment, the method comprises baselining of the probe in combination with plural ultrasound machines.

[0055] In an embodiment, the system under test comprises an imaging chain including at least a probe and an ultrasound machine and in which the QA is performed for the entire imaging chain.

[0056] In an embodiment, the system is arranged so as to perform the QA testing in less than 10 minutes. The QA preferably includes fault detection.

[0057] In an embodiment, the system is arranged so as to perform the QA testing in less than 5 minutes. The QA preferably includes fault detection.

[0058] According to a further aspect of the present invention, there is provided a testing phantom for use in Ultrasound system QA, the testing phantom being formed of a low cost material and being arranged to enable sensitivity and noise measurements to be made of a probe under test.

[0059] Preferably the phantom is formed of silicone.

[0060] In an example the phantom includes an additive material.

[0061] In an example the additive material is micro glass beads or other suitable additives.

[0062] In an example the speed of sound through the material of the phantom is between 900 and 1100ms-1. In an example the phantom has one or more recesses shaped so as to receive an ultrasound probe.

[0063] In an example the phantom has one or more recesses shaped so as to receive a 3D / 4D ultrasound probe.

[0064] In an example the phantom comprises a plurality of (two or more) cylindrical cutouts for receiving inserts, the cylindrical holes provided at different distances from the surface of the one or more recesses. The holes are provided at differing distances from the surfaces of the phantom into which or upon which a probe will be placed. Thus, due to the different distances different parameters associated with the probe can be measured. Providing multiple cylindrical holes in the same phantom enables multiple different parameters to be measured by the same phantom. The holes could be formed as part of a moulded article or physically cutout or ablated from the block of material defining the phantom.

[0065] Embodiments of the present invention will now be described in detail with reference to the accompanying drawings, in which:

[0066] Figure 1 is schematic representation of an ultrasound QA system;

[0067] Figure 2A is schematic flow diagram showing the steps in an Ultrasound QA method;

[0068] Figure 2B is schematic flow diagram showing the steps in an Ultrasound QA method;

[0069] Figure 3A is a schematic representations of a test object for ultrasound QA;

[0070] Figure 3B is a schematic representation of another embodiment of a test object for ultrasound QA; Figure 3C is a schematic representation of another embodiment of a test object for ultrasound QA;

[0071] Figure 4 is schematic representation of the steps of obtaining and using an ultrasound QA App;

[0072] Figures 5 to 7 show exemplary automatically generated QA reports; and

[0073] Figures 8A and 8B show a dashboard as part of an ultrasound QA testing system.

[0074] As will be explained below, the present system enables the QA testing of a number of ultrasound systems, whether they are large cart-based systems or pocket ultrasound systems. In simple terms the present system enables distributed QA and remote service provision in a way that has not previously been thought possible. The combination of a particular simple test object and remote connectivity enables the ultrasound QA process to be performed and importantly scaled across any number of users or types of systems.

[0075] Figure 1 is a simplified schematic view of an ultrasound QA system 1 according to the present disclosure. The system 1 comprises a user’s pocket ultrasound 3 together with a simple low-cost test object 5, to be described in greater detail below. A user’s mobile telephone or PDA 7 is included, having running in or on it an App 9, again to be described in greater detail below. The mobile telephone 7 is preferably connected via a network such as the internet to a data store, e.g. a database 13, in the cloud.

[0076] The data store 13 contains a record or account for the user and the specific ultrasound device 3. As will be explained below the database is arranged to store images and data files provided over the network from the device 3, preferably via the mobile telephone 7. The device 3 and the mobile telephone 7 are connected by known means, e.g., Bluetooth or a wired connection. In some embodiments it is possible that the ultrasound device 3 itself has communication capability so it is able to transfer data or images directly to the cloud and database 13 without the need for the mobile telephone 7. The problems associated with the shortage of technically qualified people and the inevitable inefficiency of having them travel from site to site is addressed by the system shown in and described above with reference to Figure 1. A user is able to capture images or files such as video of the test object 5 using the pocket ultrasound 3. The captured images are transferred to the user’s account in the data store 13 in the cloud. The stored data can then be operated on and processed by skilled personnel or appropriate software using one or more of image analysis, machine learning and artificial intelligence to return results for storage on the mobile telephone 7, e.g. by the App 9.

[0077] The method and system will now be described in greater detail.

[0078] Figure 2A is a schematic view of a flow diagram showing an exemplary method 2 of remote ultrasound QA. The system will involve the actions of a user 4 shown schematically as remote from a QA service provider 6. It is, for the purposes of this example, assumed that the user is a medical practitioner in possession of a pocket ultrasound system, such as any commercially available system including the GE Vscan Air, Philips Lumify or Butterfly iQ.

[0079] Initially at step 8 the user will have registered 8 their device with the QA service provider 6. Examples of how this can be done will be described in greater detail below. The user receives 10, or is provided with by the QA service provider, a specialised test object which is provided as a simple and low-cost silicone imaging test object..

[0080] At step 12, having decided that a QA process is desired or required to comply with a safety standard, the user initiates the QA process at step 12 by performing an ultrasound imaging process of the provided test object. This can involve any typical interactions with the test object or can be according to some prescribed sequence of imaging steps. The pocket ultrasound system will have captured and stored images of the test object and is then arranged automatically to transmit 14 the captured image or video files to the remote QA service provider. This can be via any known communication method and typically might be via a network such as the internet. At step 16, the transmitted data files are received by the remote QA service provider. The QA service provider 6 then performs 18 known QA tests on the captured images, with knowledge of both the test object that has been imaged, and the system used to perform the imaging. The QA testing itself is thus done remote from the user and the system and this enables a centralisation of the QA processing and a distribution of the QA testing. Importantly it removes the need for specialist highly trained and expensive medical physicist to travel to the location of each of the ultrasound operators or medical practitioners who are signed up to the QA service provider. The QA tests performed can be any one or more of known QA tests such as uniformity, drop-out, reverberation threshold, reverberation depth, B-mode noise levels, grey level, pulsed wave Doppler noise threshold, colour Doppler noise threshold, cable noise test, signal to noise ratio and penetration depth (sensitivity).

[0081] The process of QA has thus been performed remotely by a technical or nontechnical user which is a distinction from known methods of QA and significantly enables a scaling of the QA process which is not possible given current restrictive availability of trained personnel.

[0082] An important feature of the system is the provision of a known but simple test object which is used for imaging. This enables a standardisation of the imaging and of the tests performed which enables the QA method to perform fault detection and detect deterioration of the ultrasound device.

[0083] As will be explained below, an important aspect of the present is the provision of an App which a user can use to store data and records of QA processes performed on their ultrasound system.

[0084] The present system is thus a user-led distributed QA system, arranged in preferred embodiments to operate with an app together with the referred to test object. The system is arranged to be accessed 24 / 7 and to enable low-cost ultrasound testing. The present method thus allows a reversal of the known systems; instead of taking the ultrasound physicist to the device, the user can send some images to the physicist. Without the cost of travel and expensive equipment, handheld ultrasound can be made viably safe for regulated medical devices on a global scale, democratizing ultrasound safety worldwide.

[0085] All records of a user’s ultrasound will be stored via the app ensuring compliance with Managing Medical Devices (MHRA, 2021), Ell, MDR (Medical Device Regulation 2017 / 345), ANSI / AAMI EQ56, EQ89 and international guidelines on ultrasound testing. The solution meets this need and gives users accessible proof of compliance ready for regulatory inspections, removing one of the principal problems often faced by healthcare professionals.

[0086] An important aspect of the present system is the use of an inexpensive, simple- to-use test object which enables healthcare professionals globally to test their own ultrasound devices. The test object is smaller and more user-friendly than those typically used in imaging calibration or QA. This enables use by non-technical users whilst maintaining functionality for technical users who wish to manage a fleet of ultrasound scanners across an organization.

[0087] The present test object preferably enables measurement of the most important parameter (sensitivity) that is traditionally only measured with a high-cost tissue mimicking test object (TMTO). The present test object uses inexpensive silicone material to validate the proposed novel test method.

[0088] By removing the more complex components which were important for legacy ultrasound testing, a test object which allows the measurement of sensitivity can be made at a fraction of the cost.

[0089] Thus far, the test has been performed across 95 ultrasound probes from four different manufacturers and shows excellent agreement with the equivalent test performed on the high cost TMTO referred to above. The capability to test for noise levels, an important secondary measure (Dudley, 2020), will be added to the Multi-Medix test object.

[0090] Crucially, this enables poorer or more remote areas to benefit from an innovative QA system. Approximately 37% of ultrasound probes in clinical use are faulty (Dudley, 2020). The present QA system would have identified 99% of these defects, reducing the risk of misdiagnosis.

[0091] It will be understood that to perform a QA measurement, the user will take prescribed camera photographs of their handheld pocket ultrasound machine. They will preferably be instructed to:

[0092] 1. capture an image of the handheld scanning in-air; and,

[0093] 2. capture an image / video (also known as cine loop) of the machine scanning in the test object.

[0094] The captured images (in any appropriate file format, but typically JPEG files) are sent to a remote, preferably cloud-based, database where they can be accessed and reviewed by a technical review team. For example, the present applicant has teams of engineers and medical physics specialists who are able to review the received images from the cloud-based database. The outcome of the test is then delivered back to the user’s app and stored so they have access to results and a testing history of the device.

[0095] Importantly, as well as keeping a record of the tests themselves, a record is generated of the QA history which can be used to demonstrate and evidence that independent QA has taken place, thus satisfying many independent compliance thresholds. Of course, in the event the ultrasound is identified as faulty, the central physics team can advise on repair / replacement options.

[0096] The process of Figure 2A works well and represents a significant improvement over existing methods and system for ultrasound system QA. Figure 2B is a schematic flow diagram showing the steps in an alternative Ultrasound QA method. Similar to the process shown in and described with reference to Figure 2A steps 8 to 16 operate in corresponding manner in the method of step 2B. However, whereas the method of Figure 2A requires the direct involvement and time of a medical physicist, the process of Figure 2B, as will be explained below, is automated. Not only does this significantly increase the speed and accuracy of the results as generated but it enables baselining in a manner that was not previously possible. Baselining is the process by which a record is made at a certain time of the performance or QA of a component or combination. This record can be treated as a baseline or benchmark. In due course when the same component or combination are tested again, the results can be compared to the established baseline. Variation of performance of a component or combination of components over time can be determined, and if identified, flagged to a user.

[0097] Referring then to Figure 2B, once the captured images are received at the automated QA service provider, a number of software implemented tasks and processes 17 are performed automatically on the received images to generate a QA report. The processes are performed automatically and thus eliminate the risk of observer variability and external environmental factors. This the automated process ensures reproducibility and accuracy. The reproducibility itself is important as this leads to the ability to baseline outputs of the process, as will be explained in more detail below.

[0098] The software arranged to receive and process the data from a user, e.g. the images or the text relating to a system under test, is provided in a suitable programming language. In one example the software is provided as a Python script but other languages can also be used.

[0099] In an example, the software is arranged to pixelate the received images and process the pixelated images so as to generate required outputs relating to the parameters of the ultrasound system under test. For example, the images could indicate the sensitivity or noise associated with the system under test. Indeed referring to the exemplary reports shown in Figures 5 to 7 (discussed below) the digitised outputs representing parameters such as intensity and uniformity can be used to determine ethe sensitivity and noise in relation to the probe or system under test.

[0100] Thus, the QA is done in effect automatically, and no significant input is needed from a skilled or trained medical physicist. The QA output, depending as it does on the digitised images, has in effect been made digital rather than analogue, since conventionally a physicist would have included visual “analogue” checks of received images to make QA assessments. A level of automation has thus been introduced using bespoke created software to enable the generation of QA reports effectively in real time on the basis of a number of received images (the same images that would previously have been provided in the process of Figure 2A).

[0101] The method as described may typically be performed with the use of low-cost test object, such as a silicone-based test object. Examples are shown in Figures 3A and 3B. Referring to Figure 3A, The test object or test object 20 is formed of a material such as silicone and has plural surfaces shaped for engagement with different standard types of ultrasound transducers. The test object 20 is shaped simply in this example as a cuboid having dimensions roughly between 6 and 20 cm. Preferably the test object is 12.5cm long, 10cm high and 6 cm deep. The sizing is selected such that it is convenient and easy for an operator to handle .

[0102] The test object has at least one flat planar side 22 with a shallow rectangular recess. . At least two of the other surfaces are provided with shaping to enable interaction with common ultrasound transducers. The upper surface 25 (“upper” in the configuration shown in Figure 2) has a curved recess 24 which is shaped and sized so as to be able to receive a curvilinear array transducer.

[0103] The bottom side surface 28 has an end domed recess 26 which is shaped and sized so as to be able to receive an endocavity array transducer.

[0104] The at least one flat surface 22 has a shallow rectangular recess 29 which is able to interact and engage with a linear and sector array transducer.

[0105] Accordingly, in a preferred example, the test object is a silicone test object. The test object has interface portions shaped to engage an ultrasound transducer for sensitivity testing. Preferably, the test object has at least three interface portions, each shaped differently to engage with a respective different type of ultrasound transducer for sensitivity testing.

[0106] In the example shown in Figure 3B, the test object has at least one cavity 30, e.g., a cylindrical cavity, which houses a removable insert of varying attenuation. An correspondingly shaped anechoic insert 31 enables noise level and signal to-noise testing.

[0107] The present inventors have recognised that, surprisingly, low cost silicon-based test object such as that shown in Figure 2 can function as a test object that provides a speckle image when scanned with an ultrasound transducer. Although the speed of sound in silicone is not matched to human tissue, the image produced is similar to homogeneous human soft tissue.

[0108] The image is also similar to the more sophisticated, speed of sound matched, TMTOs traditionally used for sensitivity measurements. Therefore, with the introduction of anechoic targets and hyperechoic targets, baseline quality assurance can occur on the low-cost silicone-based test object.

[0109] Figure 3C is a schematic representation of another embodiment of a test object for ultrasound QA. In this example a recess 9 on the upper surface is shaped to receive a 4D ultrasound probe. The curvature and dimensions of the recess are provided to correspond to those of a 4D ultrasound probe. A number of exemplary dimensions can be seen in the views of Figure 3C. These are merely exemplary. Any suitable dimensions can be used for the low cost phantom.

[0110] Furthermore, looking at the images on the left of Figure 3C the circular openings for receiving the cylindrical inserts can be seen. Importantly in this example multiple openings are provided. This provision of multiple inserts together variation in distances from the external surfaces of the phantom (which will engage in use with a probe or piece of equipment under test) enables tests to be performed as described herein. The openings preferably extend all the way through the body of the phantom as can be understood with reference to the right hand image of Figure 3C showing in dashed lines the cylindrical openings or channels within the phantom. The channels are parallel to the upper and lower surfaces of the phantom.

[0111] The test object of any of Figures 3A to 3C may be considered an advanced modular test object. Preferably the test object is arranged and configured so as to include one or more interchangeable or fixed cylindrical inserts 31 (as shown in Figure 3B) for preforming specific QA tests, such as:

[0112] ■ Noise measurements (anechoic inserts).

[0113] ■ Contrast measurements (echoic inserts with varying speeds of sound).

[0114] ■ Calliper accuracy / distance measurements (hyperechoic inserts with precise markers).

[0115] ■ Resolution measurements (hyperechoic targets of specified dimensions).

[0116] Thus, as explained above, the test object using the process of Figure 2B may effectively be thought of as a “digital test object” as compared to previous QA.

[0117] The cylindrical inserts 31 are arranged to be provided within holes or cutouts provided within the phantom. The cylindrical holes are preferably provided at selected distances from the outer surfaces of the phantom, thus defining different distances through the material of the phantom that ultrasound waves will have to travel to reach the inserts. This enables the holes or cutouts to be positioned to enable selected parameters of the ultrasound equipment to be determined.

[0118] The test object of any of figures 3A to 3C is preferably made of a silicone material including an additive. The preferred additive is micro glass beads, which , as will be explained below, can affect properties of the material in a desired manner. The present inventors have recognized that a low cost silicone phantom such as any of those shown in Figures 3A to 3C including an additive such as micro glass beads can be used effectively in an ultrasound QA process and importantly can be used in measurements of sensitivity and noise. It has previously been thought that to measure the sensitivity and noise from an ultrasound probe an expensive and complex phantom is needed. Such high cost phantoms are formed of materials that typically have speed of sound of between 1400 and 1600ms-1. A typical specific value would have been 1540ms-1.

[0119] In contrast to this, low cost phantoms such as those shown in Figures 3A to 3C have speed of sound of between 900 and 1000 ms-1. A system is provided that enables users without technical expertise to automatically identify faults and assess performance of Medical Ultrasound Scanners and transducers remotely and typically in less than 10 minutes. It is preferred that the system and associated application is operated as cloud-based.

[0120] The method can this be seen to support a decentralised model of ultrasound equipment quality assurance and testing by enabling a non-technical user to perform the test regardless of location or environmental conditions.

[0121] Some important but not necessarily essential technical features that enable the system and method to operate and function as indicated above include the use of a compact phantom that preferably is sized so as to fit into the palm of the hand of typical user but is capable of having performed on it or with it, “claim 8 tests” to achieve ultrasound QA testing. As explained herein, this can be enabled by the selection of a material with a speed of sound substantially less than the average for human tissue (1540 m / s).

[0122] In addition, it is preferred that a durable test object is used which is UV resistant, skin-safe, arranged to be cleaned (or cleanable) by the user, and is calibration and maintenance free.

[0123] The test object is also preferably a low-cost test object. As a consequence of the small size and low cost of the test object it can be made accessible in remote locations and is simple to use with no technician knowledge required.

[0124] As explained herein, in embodiment, the method comprises the analysis of both an “in air” image (where the probe or US equipment operates in-air) and of an image / video of the probe imaging the test object.

[0125] The method preferably comprises the comparison of in-air and in-test object images to confirm / verify ultrasound equipment faults.

[0126] Machine learning and Al are preferably used to identify and predict ultrasound probe and scanner faults by analysing images either “in-air” or in a test object or both. The method is therefore arranged to enable the establishment of a baseline measurement of each of the output parameters under test (described herein above). Future tests of the US equipment (scanner and probe) can then be compared against baseline to identify degradation or change.

[0127] Anechoic targets are preferably included to enable noise measurement and signal to noise ratio to be performed.

[0128] Finally, in some embodiments, hyperechoic targets and / or spheres of specified dimensions or separations are provided arranged within a compact test object, the targets having a speed of sound substantially lower than the average for human tissue (1540m / s). A correction factor is applied to enable calliper accuracy measurement.

[0129] The system described can be accessed and used in the form of an App that a user can have on their mobile smart phone or any appropriate digital device. Figure 4 shows a schematic simplified series of steps by which a user can download the App and set it up for running on their mobile telephone or digital device (referred to hereinafter simply as “phone” for brevity).

[0130] Initially a user will download the App from the Google Playstore ® or Apple’s Appstore ® or any other provider of mobile Apps from which the app will be available. The user is required to provide information such as the model of the ultrasound device that they have, serial number, the year of manufacture and any information regarding software versions running on it.

[0131] Next, they are prompted for information relating to how often they would like to use the App, i.e. how often do they want to perform QA on their ultrasound device. They are given the option of subscribing which can offer repeat service or of paying for a one- off QA process.

[0132] An instructional video is shown, or some other form of instructional material may be provided. For example, the App could provide a download of a written or audio instruction manual. Finally, once this information has been gathered and stored the user can select when to perform a QA test by clicking on a selector “Test a Scanner”. The App will guide the user on which images to take together with the provided low cost simple test object, and how to upload them to the App and from there to the remote database in the cloud.

[0133] Once the images have been received in the cloud-based database and stored in a user’s account, tests or checks can be run on the images to determine the QA status of the ultrasound machine under test, the tests can be performed by a human operator or using computer or Al based testing methods. The results of the tests are stored in the user’s database in association with the ultrasound machine in question. Thus, a record and historical store of the timing and details of QA process is built up.

[0134] Over time and repeated tests, it will be appreciated that a historical record is built up of all QA tests that have been done. These can be used when the user is required to demonstrate, say to a regulator, that the ultrasound machine has undergone the required QA testing over its active lifetime.

[0135] The tests review can be done entirely independently of the user, even without their knowledge of the precise time that the QA testing is done. The only role of the user in the testing is to perform that image capture of the test object when prompted by the App or when they decide that QA is required. A user has also been given the option to review the automatic result provided by the application and make changes / comments as part of an approvals process.

[0136] In the example described above with reference to Figure 2B a process is described in which automated QA is performed. The processing of the received images is preferably executed using an integrated software platform including at least two of the a mobile application, a web application and local software. The mobile application can be as described above with reference to Figure 4.

[0137] Indeed, as already described, the Mobile Application facilitates remote QA with upload and image analysis capabilities. In addition, a web application is provided which operates through a browser with optional integration into patient imaging archives. Finally, local software arranged to run on a user’s local system, and to provide on-site analysis and report generation.

[0138] The present system provides an improved system for real time automated remote QA. The system relies upon use of a low cost and easily available phantom typically made of a material such as silicone. In addition, simple tests are produced based on images that will be provided by a user to the automated system. Finally, the system relies on remote processing of the images such that there is no limits based on a user’s own IT or data processing systems. The combination of these three factors enables automated low cost QA which itself leads to several related technical advantages. The simple tests can be for example based only on sensitivity and noise from the images captured of the simple low cost phantom.

[0139] In the world of Ultrasound QA the issue of baselining is problematic. This is the question of establishing a baseline or a performance value for a system and then determining over time how the system varies with respect to the established baseline. In conventional systems in which QA is complex process and relies on expert user interaction, e.g. of a medical physicist it is neither technically nor commercially feasible to base line the performance of many ultrasounds systems. This is more so because in a busy health service establishment such as a clinic or hospital a single ultrasound probe may well be used with different ultrasound machines. The interaction of a probe with a specific machine can produce different performance than from another, theoretically, “identical” machine.

[0140] The present system, relying on the almost instantaneous, or in any event real time QA can enable baselining of any pair of probe and machine. Furthermore, the baselining can be achieved using simple tests of, say, only sensitivity and noise. This baselining can be achieved across multiple probe / machine pairs significantly improving the monitoring and understanding of the performance of an entire facility’s ultrasound hardware. In addition, the possibility of baselining any probe and machine pair means that within a hospital or clinic there is the possibility of interchangeability of probes with machines without any compromise of confidence in the accuracy or reliability of generated scans. Figures 5 to 7 show an example of the output of a real time generated ultrasound QA report generated using the presently described system. The report relates to a single piece of ultrasound equipment “Philips - CX50-PH16120” and two different ultrasound probes “L12-5 PH17254” and “L12-3 PH6516”.

[0141] The report summary (Figure 5) includes the physical images of the probes and the machine (under the heading “Visual Inspection”). The generated reports include the various elements of QA data that might be useful and / or necessary for a user to establish whether or not the probe and the pair have passed specified QA tests. As can be seen both probes have in fact failed the tests in this example.

[0142] The summary includes indication 34 of the probes and machine under test. Images 36 are included which are the photographs that will have been provided by the requester of the report. As can be seen images of both the probe(s) and the system are included.

[0143] As well as uploading images of the probe and system under test, the user is of course required to provide images of scans of the test object, performed using the probe and system under test. This can be done in response to prompt from the QA system or a user may do this themselves if they are already aware of the requirements to obtain the QA results.

[0144] Once the images of the scans are received by the QA system, a number of tests are executed automatically and promptly as soon as the test images are received. The results of the tests can be seen in the two probe test results of Figures 6 and 7. As can be seen, each includes a reverberation pattern 38, a reverberation uniformity plot 40, an image 42 of the uniformity scan of scan of the phantom, a plot 44 derived from the uniformity scan and a plot of variation of intensity value (reverberation) with pixel position.

[0145] The probe analysis output 48 is generated which can be used then to determine whether or not the probe has passed. As can be seen from Figure 5, both probes under test in this specific example have failed 35. It will be understood that each of the tests (the results of which are shown in the reports of Figures 6 and 7) are themselves known tests for an ultrasound probe subject to QA analysis. However, the present system is arranged to perform these tests automatically and in real time as soon as the images are received from a user. Thus, in practice a QA result can be achieved for a probe and system combination typically in a matter of a very few minutes, e.g. 2 to 5 minutes. This contrasts even with the embodiment of Figure 2 which itself is a significant improvement over known systems due to the remote nature of the QA testing. Furthermore the automation enables the QA test report generation to be overseen or managed by a non-technical person.

[0146] It will be appreciated that the results of the tests, such as, say the reverberation pattern 38 or the uniformity phantom pattern 42 can be measured against expected values based on the known parameters of the test object (See figures 3A and 3B). Thus, a simple and robust method for real time QA of an ultrasound system and probe is provided.

[0147] In more detail referring first to Figure 6, an Ultrasound QA report can be seen in respect of a piece of tested US equipment “probe 1”.

[0148] A reverberation pattern image 38 is provided in the report. This displays the ultrasound image of the probe operating in-air. This image may be flattened / converted from a curved geometry to a flat rectangular geometry for certain probes to allow easy comparison with the graphs below.

[0149] Next, the reverberation uniformity plot 40 is provided. This is presented as a bar chart and serves to quantify the reverberation pattern image 38 and provides two separate outputs. In this example, the values of the bars in the chart are the mean pixel intensity in the corresponding part of image (38) whilst the colour (hatching / shading) of the bars indicate and highlight areas of dropout or non-uniformity. The chart therefore automatically identifies areas of concern and displays them in a simple and accessible manner.

[0150] Next, what is referred to as the “Multi-Medix Uniformity Phantom image” 42 is included. This displays the ultrasound image of the probe imaging the Multi-Medix ® phantom. The image 42 may also be flattened / converted from curved geometry to flat rectangular geometry to allow easy comparison.

[0151] Next, the report is configured to include a reverberation patten vertical intensity profile 46. This is set up to display, preferably in the form of a line graph, the average intensity of each row of pixels from the top row to the bottom row of the reverberation pattern image 38. The line graphs indicate the automatically detected deepest reverberation depth in image the second deepest or “adjacent reverb” depth. These two values enable the automatic, reproducible measurement of a widely recognised test for sensitivity of ultrasound scanners and probes.

[0152] Finally a probe analysis output 48 is provided which displays values relevant to the sensitivity measurements of the scanner and probe under test.

[0153] These measurements preferably include:

[0154] • Outputs from the line graph 46 which are the deepest reverb depth and adjacent reverb depth.

[0155] • The average intensity value the row of pixels at the deepest reverb depth and the adjacent reverb depth. The average intensity values are preferably generated automatically and formalise measurements that, conventionally, would have been taken manually by ultrasound users when performing User QA.

[0156] • The penetration depth of the ultrasound probe in image 42. This is an automatically detected penetration depth past which, the ultrasound probe and scanner can produce no image or the resulting image is dominated by noise.

[0157] It will be appreciated that the generated parameters and outputs can be selected from as desired outputs or factors for inclusion in the generated report. Thus, not all the parameters exemplified in the report of, say Figure 6, must be included in all such reports.

[0158] Furthermore, it is preferred that based on the determined parameters an Ultrasound Equipment Health Score is determined. This can, for example, be based on one or more of equipment age, uniformity of the “in-air reverberation pattern”, uniformity of the ultrasound image of a uniformity phantom, percentage or number of dead elements (dropout), change / degradation of sensitivity, change / degradation of noise measurement or signal-to-noise ratio.

[0159] Tracking the penetration depth is a widely cited ultrasound QA test but it is highly vulnerable to inter-operator and intra-operator error as well changes in environmental condition such as ambient light. The present method eliminates these factors by performing automated measurements of the image data.

[0160] The process of quick and automated QA testing enables baselining of probes and probe / system pairs in a practical manner that has not previously been possible. Whilst baselining has always been theoretically possible based on a complex time consuming and expensive testing by a medical physicist, in practice these issues have made the process impractical. What this has meant is that baselining is often not done at all, and when it is it might have been done for a particular pair of probe / system and then assumed to be correct or to apply for all probes of the same model using other different systems of the same model.

[0161] Natural variation would likely mean that the baselining is not correct. The natural variation in such complex systems would mean that in reality the performance of a different pairing, i.e. either or both of the machine and probe being different, would mean that the performance would be different even if they were technically the same models of probe and system. The present automated method of QA , which can produce results in a matter of minutes thus enables baselining of all probe / system pairings in a way that has not practically been possible before. The speed at which a QA process can be performed means that QA and baselining can in effect now be done for an entire imaging chain, or any selection of components within the imaging chain.

[0162] Whereas the constraints that existed previously, (cost / time) meant that in practice decisions were necessary to limit the QA testing to, say, just a probe, now all parts of an imaging chain can be QA tested. This technical benefit is a consequence of the novel and innovative process by which QA process is automated and performed in real time. Furthermore, the ability to perform QA testing and certification in real time on any pairing of a probe or system enables the interchangeability if probes within a hospital or clinic environment. In cases in which QA has not been performed for a combination of system and probe a hospital may decide or be required not to allow use of the particular probe with the particular system. However, if QA has been performed for all possible pairing then the interchangeability of probes with systems is significantly improved.

[0163] The App as mentioned herein may be run on a portable device or may be code that when run on a computer (having associated microprocessor, memory and optional peripheral devices) generates the required interfaces and functionality.

[0164] Figures 8A and 8B are respectively the upper and lower parts of a schematic view of a dashboard from the App that preferably relates to multiple pieces of tested and QA’d ultrasound equipment. Preferably it represents all the US equipment of a particular institution. For example, this could be a specific hospital or clinic or it could even relate to multiple pieces of apparatus all belonging to a specific doctor. The dashboard incorporates the results of the reports (Figures 6 and 7) generated using the presently described method of remote automated QA for a piece of ultrasound equipment. Some or all of the data and QA parameters determined in respect of each of the pieces of ultrasound equipment are selected for inclusion in the dashboard.

[0165] Looking at Figure 8A there are pie charts showing graphically and directly statistics relating to how many of the pieces of equipment of the institution in question have been QA tested and how many more are still to be done. An indication of average age for each of the pieces of ultrasound equipment is shown simply in bar chart.

[0166] Figure 8B shows the tabulated results, with one row being provided for each of the pieces of equipment. A health score (mentioned aboveO is indicated giving a quick and intuitive indication of the health or relative health of each piece of QA equipment. There is an indication of when QA was last done and when the next QA is due (or recommended).

[0167] Embodiments of the present invention have been described with particular reference to the examples illustrated. However, it will be appreciated that variations and modifications may be made to the examples described within the scope of the present invention.

Claims

Claims1. A method of remote quality assurance for ultrasound systems, the method comprising:Receiving from the user at a remote location an ultrasound image of a test object; processing the received image to determine one or more quality assurance parameters of the ultrasound system under test; providing a notification to the user at the remote location the results of the quality assurance determination preferably including the identification of faults.

2. A method according to claim 1 , comprising in addition, receiving an ultrasound image scanning “in-air”, and processing the received ultrasound image scanning “in-air” and the ultrasound image of the test object to determine one or more quality assurance parameters of the ultrasound system under test.

3. A method according to claim 1 or 2, comprising providing the user with an inexpensive, easy-to-use test object for use with the method of remote QA.

4. A method according to any of claims 1 to 3, comprising communicating captured images to the remote location from the user using a personal transmission device.

5. A method according to claim 4, in which the personal transmission device is intrinsic to the ultrasound system or is provided by a mobile telephone separate from the ultrasound system.

6. A method according to any of claims 1 to 5, comprising performing defined QA tests on images received at the remote location and providing results to the user.

7. A method according to claim 6, comprising storing results for the user and the tested ultrasound system at the remote location.

8. A method according to claims 5 or 6, in which the tests are selected from the group consisting of uniformity, drop-out, reverberation threshold, reverberation depth, B-mode noise level, grey level, pulsed wave Doppler noise threshold, colour Doppler noise threshold, cable noise, signal to noise ratio and penetration depth (sensitivity).

9. A method according to any of claims 1 to 8, in which the test object is a silicon- based test object.

10. A method according to claim 9, in which the test object has interface portions shaped to engage the transducer of the ultrasound system for sensitivity testing.

11. A method according to claim 9, in which the test object houses an anechoic insert or anechoic area for B-Mode noise level and signal to noise ratio testing.

12. A method according to claim 10, in which the test object has at least three interface portions, each shaped differently to engage with a respective different type of ultrasound transducer for sensitivity testing.

13. A method according to any of claims 1 to 12, in which the processing is done in real time to determine the one or more quality assurance parameters of the ultrasound system under test.

14. A method according to claim 13, in which a report is generated indicating whether or not the system under test satisfies QA requirements.

15. A method according to claim 13 or 14, in which the system under test includes an ultrasound probe.

16. A method according to claim 15, in which the system under test includes an ultrasound machine in combination with the probe.

17. A method according to claim 16, comprising generating baseline QA parameters for the pairing of the probe and the ultrasound machine.

18. A method according to claim 16, comprising baselining of the probe in combination with plural ultrasound machines.

19. A method according to any of claims 13 to 18, in which the system under test comprises an imaging chain including at least a probe and an ultrasound machine and in which the QA is performed for the entire imaging chain.

20. A method according to any of claims 1 to 19, in which the QA testing time is less than 10 minutes.

21. A method according to claim 20, in which the QA testing time is less than 5 minutes.

22. A testing system for remote quality assurance for an ultrasound system under test, the testing system comprising: a data store for receiving from a user at a remote location an ultrasound image of a test object; a processor to process the received image to determine quality assurance parameters of the ultrasound system under test and to provide a notification to the user at the remote location of the results of the quality assurance determination.

23. A testing system according to claim 13, comprising a test object for imaging with the ultrasound system under test.

24. A testing system according to claim 13 or 14, comprising an App on a computing device to prompt a user to capture an image of the test object and provide the image to the data store.

25. A testing system according to claim 15, in which the computing device is a smart phone or a PDA.

26. A testing system for quality assurance for an ultrasound system under test, the testing system comprising: a data store for receiving from a user an ultrasound image of a test object; a processor to process the received image to determine quality assurance parameters of the ultrasound system under test and, in real time, automatically to generate a QA report in respect of the system under test.

27. A method of quality assurance for ultrasound systems, the method comprising:Receiving from the user an ultrasound image of a test object; processing the received image to determine one or more quality assurance parameters of the ultrasound system under test and, in real time, automatically generating a QA report in respect of the system under test.

28. A testing phantom for use in an ultrasound QA system, the testing phantom being formed of a low cost material and being arranged to enable sensitivity and noise measurements to be made of a probe under test.

29. A testing phantom according to claim 28, in which the phantom is formed of silicone.

30. A testing phantom according to claim 29, in which the phantom includes an additive material.

31. A testing phantom according to claim 30, in which the additive material is micro glass beads.

32. A testing phantom according to any of claims 28 to 31, in which the speed of sound through the material of the phantom is between 900 and 1000ms'1.

33. A testing phantom according to any of claims 28 to 32, in which the phantom has one or more recesses shaped so as to receive an ultrasound probe.

34. A testing phantom according to claim 33, in which the phantom has one or more recesses shaped so as to receive a 4D ultrasound probe.

35. A testing phantom according to claims 33, comprising a plurality of cylindrical holes, such as cutouts, for receiving inserts, the holes being provided at different distances from the surface of the one or more recesses.

36. A phantom according to claim 35, in which the inserts are provided as anechoic targets for measurement of noise and signal-to-noise ratio.

37. A phantom according to any of claims 28 to 36 including one or more hyperechoic targets and / or spheres of specified dimensions or separations located in a test object which has a speed of sound lower than the average for speed of sound in human tissue.

38. A cloud-based application arranged automatically to identify faults and assess performance of Medical Ultrasound Scanners and transducers remotely, wherein preferably the application is arranged to execute or support the method of any of claims 1 to 21.

39. A method according to any of claims 1 to 21, comprising using machine learning and artificial intelligence to detect faults of an ultrasound scanner or probe via an in-air reverberation pattern.

40. A method according to any of claims 1 to 21, comprising use of machine learning and artificial intelligence to detect faults of an ultrasound scanner or probe via imaging a uniform test object.

41. A method according to any of claims 1 to 21 , comprising comparison between in-air reverberation images and images of a uniform test object to detect, verify and assess the clinical impact of a fault with an ultrasound scanner or probe.

Images