Imaging of a body part based on simulated blinking of contrast agent particles present in the body part

Abstract

A solution for imaging a body part of a patient is proposed. A corresponding ultrasound-type imaging method (700) comprises generating (742-751) a plurality of mask images from respective input images of the body part by applying corresponding blinking masks (alternating blocking and passing regions). In each mask image, a corresponding position of any contrast particles of a distinguishable contrast agent (from other contrast particles) is determined (754-769). Analysis information of the body part based on the positions of the contrast particles is then output (772-781). A computer program (600) and a corresponding computer program product for implementing the imaging method (700) are also proposed. Furthermore, a corresponding computing system (112) and an imaging system (100) including the same are proposed. A medical method based on the same imaging method (700) is further proposed.

Description

[Technical Field]

[0001] The present disclosure relates to the field of medical imaging, and more particularly to contrast agent-based ultrasound-type medical imaging. [Background technology]

[0002] The background of the present disclosure is presented below with a discussion of technology relevant to its context. However, even if this discussion refers to documents, acts, artifacts, or the like, no attempt is made to imply or represent that the techniques discussed are part of the prior art or common general knowledge in the field relevant to the present disclosure.

[0003] Medical imaging is a well-established technology (in the field of equipment for medical applications) used by physicians to examine a patient's body parts through images that provide a visual representation of the body parts (typically in a substantially non-invasive manner, even when the body parts are not directly visible). In particular, contrast-enhanced (medical) imaging is based on the administration of a contrast agent to each patient undergoing a (medical) imaging procedure. The contrast agent contains contrast particles that enhance the contrast of biological targets of interest (e.g., lesions) within the body part under examination, making them more visible in the image. This facilitates the physician's work in some medical applications (e.g., diagnostic applications).

[0004] However, medical imaging is diffraction limited due to interference of the signals used to acquire images of body parts. This causes a fundamental limitation on the (spatial) resolution of the image, making it impossible to distinguish between representations of points closer than the corresponding resolution limit (and in particular contrast particles, which appear as corresponding blurred points). The resolution depends mainly on the wavelength of the signal; the shorter the wavelength, the higher the resolution.

[0005] In particular, ultrasound (medical) imaging involves the application of ultrasound to a body part, where an ultrasound contrast agent (USCA) that acts as an efficient ultrasound reflector is typically administered to the patient. Corresponding echo signals recorded in response to the ultrasound are then used to create an image of the body part. In particular, the flow of contrast agent particles in the blood vessels of the body part provides a representation of its angiogenesis.

[0006] Ultrasound imaging allows for the observation of deep body parts (regardless of their opacity) using a signal whose penetration increases with wavelength. However, as the wavelength increases, the resolution decreases. Therefore, if contrast particles are closer than the resolution limit, they appear overlapping in the image. This makes it impossible to distinguish between blood vessels (through which contrast particles flow) that are very close to each other. As a result, observing the microvasculature of a body part becomes increasingly difficult as its depth within the body part increases. This hinders the analysis of the microvasculature, especially its abnormal changes associated with a wide range of diseases (such as neovascularization typical of tumors).

[0007] Ultrasound localization microscopy (ULM) has been proposed to provide super-resolution beyond the resolution limit. Briefly, ULM is based on the localization of isolated contrast particles. For example, for this purpose, images of a body part are acquired at a very high acquisition rate. The contrast particles to be localized are represented by corresponding (smaller) markers at their locations. The positions of the contrast particles are then tracked along the image to reconstruct their trajectories within the body part.

[0008] In particular, various examples of this technique are described in C. Huang et al., "Short Acquisition Time Super-Resolution Ultrasound Microvessel Imaging via Microbubble Separation," Scientific Reports 10:6007 (2020); R. van Sloun et al., "Super-resolution Ultrasound Localization Microscopy through Deep Learning," IEEE Transactions on Medical Imaging, 2021 March, 40(3):829-839; Christensen-Jeffries Kirsten et al., "Super-resolution Ultrasound Imaging," Ultrasound in Medicine and Biology, New York, NY, US, vol. 46, no. 4, 21 January 2020; and Park Jun Hong et al., "Deep Learning-Based Super-resolution Ultrasound Speckle Tracking Velocimetry," Ultrasound in Medicine and Biology, New York, NY, US, vol. 46, no. 3, 6 January 2020. 2020," and U.S. Patent Application Publication No. 2022 / 240899.

[0009] However, ULM requires a relatively low concentration of contrast agent. In fact, a high concentration of contrast agent would result in a large number of overlapping contrast particles in the image, which cannot be individually localized and are then discarded. As a result, relatively long image acquisition times (on the order of hundreds of seconds) are required. This is particularly due to the need to detect several contrast particles to reconstruct a representation of each blood microvessel (which takes a long time to fill with contrast agent due to its low flow rate). The administration of contrast agent may then require non-standard procedures (such as multiple injections). Furthermore, the (long) acquisition time introduces artifacts into the image due to unavoidable movements (of the patient and, in some cases, of the corresponding probe in the case of freehand scanning), especially due to patient breathing (where the acquisition time exceeds typical breath-holding capabilities).

[0010] In either case, the localization of the contrast particles is discontinuous over time, which reduces the accuracy of contrast particle tracking (e.g., adversely affecting analysis of the microvasculature).

[0011] All of the above hinder the widespread adoption of ULM in clinical practice. Summary of the Invention

[0012] The invention is set out in the accompanying claims.

[0013] A simplified summary of the present disclosure is presented herein in order to provide a basic understanding of the present disclosure. However, its sole purpose is to introduce some concepts of the present disclosure in a simplified form as a prelude to the more detailed description that follows, and it should not be construed as identifying its key elements or delineating its scope.

[0014] Generally, the invention is based on the idea of ​​simulating the blinking of contrast particles.

[0015] In particular, one aspect provides an ultrasound-type imaging method for imaging a body part of a patient. The imaging method includes generating a plurality of mask images from each input image of the body part by applying a corresponding blinking mask (alternating blocking and passing regions). In each mask image, the corresponding positions of any contrast particles of a distinguishable contrast agent (from other contrast particles) are determined. Then, analysis information of the subject based on the positions of the contrast particles is output.

[0016] A further aspect provides a computer program for implementing the imaging method.

[0017] A further aspect provides a corresponding computer program product.

[0018] A further aspect provides a computing system for implementing the imaging method.

[0019] A further aspect provides an imaging system comprising a computing system.

[0020] A further aspect provides a corresponding medical method.

[0021] More specifically, one or more aspects of the present disclosure are set forth in independent claims, and advantageous features thereof are set forth in dependent claims, all claim language being incorporated herein verbatim by reference, with any advantageous feature being provided with reference to each particular aspect, which applies mutatis mutandis to all other aspects. [Brief explanation of the drawings]

[0022] The solution of the present disclosure, as well as further features and advantages thereof, will be better understood with reference to the following detailed description thereof, provided purely as a non-limiting indication to be read in conjunction with the accompanying drawings (for the sake of brevity, corresponding elements are indicated with equal or similar references, their description will not be repeated, and the name of each entity will generally be used to indicate both its type and its attributes, such as value, content, and representation), in particular: [Figure 1] FIG. 1 shows a diagrammatic representation of an imaging system that can be used to implement a solution according to an embodiment of the present disclosure. [Figure 2] FIG. 2 shows an exemplary image obtained with this imaging system. [Figure 3] FIG. 3 illustrates an exemplary imaging procedure based on ULM technology known in the art. [Figure 4A] FIG. 4A illustrates the general principle of the solution according to an embodiment of the present disclosure. [Figure 4B] FIG. 4B illustrates the general principle of the solution according to an embodiment of the present disclosure. [Figure 4C] FIG. 4C illustrates the general principle of the solution according to an embodiment of the present disclosure. [Figure 4D] FIG. 4D illustrates the general principle of the solution according to an embodiment of the present disclosure. [Figure 5A] FIG. 5A shows different examples of blinking masks that can be used to implement the solution according to an embodiment of the present disclosure. [Figure 5B] FIG. 5B shows different examples of blinking masks that can be used to implement the solution according to an embodiment of the present disclosure. [Figure 5C] FIG. 5C shows different examples of blinking masks that can be used to implement the solution according to an embodiment of the present disclosure. [Figure 5D] FIG. 5D shows different examples of blinking masks that can be used to implement the solution according to an embodiment of the present disclosure. [Figure 5E] FIG. 5E shows different examples of blinking masks that can be used to implement the solution according to an embodiment of the present disclosure. [Figure 6] FIG. 6 illustrates the main software components that may be used to implement a solution according to an embodiment of the present disclosure. [Figure 7A] FIG. 7A shows an activity diagram illustrating the flow of activities involved in implementing the solution according to one embodiment of the present disclosure. [Figure 7B]FIG. 7B shows an activity diagram illustrating the flow of activities involved in implementing the solution according to one embodiment of the present disclosure. [Figure 8] FIG. 8 shows a comparative example of an in vitro imaging procedure known in the art and in accordance with one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0023] Referring to FIG. 1, there is shown a diagram of an imaging system 100 that can be used to implement a solution according to an embodiment of the present disclosure.

[0024] The imaging system 100 allows imaging a scene included in its field of view (defined by the part of the world within a solid angle for which the imaging system 100 has sensitivity). In particular, the imaging system 100 is an ultrasound scanner used in (medical) imaging procedures to assist physicians (e.g., for diagnostic applications). In this case, the scene concerns a patient 103 to which an (ultrasound) contrast agent containing contrast particles that act as efficient ultrasound reflectors has been administered. In particular, the scene includes a body part 106 of the patient 103 that is to be examined (e.g., to detect or monitor a lesion such as a tumor).

[0025] The ultrasound scanner 100 comprises an imaging probe (or transducer) 109 for acquiring images of its field of view, and a central unit 112 for controlling its operation. For this purpose, the imaging probe 109 is coupled to the central unit 112, for example, via a corresponding flexible cable 115.

[0026] The imaging probe 109 is handheld and sized to be held in one hand by a physician. For example, the imaging probe 109 operates in pulse-echo mode, transmitting pulses of ultrasound and receiving echo signals resulting from their reflection. For this purpose, the imaging probe 109 has an array of sensors (not shown) that function to generate ultrasound waves and receive the corresponding echo signals, for example, in a phased array arrangement. The imaging probe 109 operates at a frequency that determines the penetration of the imaging procedure within the patient 103 (the higher the frequency, the lower the corresponding wavelength and the lower the penetration). For example, the ultrasound waves may have a center frequency ranging from 1.5 MHz to 30 MHz, which allows for viewing the patient to a depth of approximately 300 mm and 15 mm, respectively.

[0027] The central unit 112 comprises a monitor 115 (for displaying images associated with each ongoing imaging procedure) and one or more input units, such as a keyboard 118 with a trackball 121 (for controlling the operation of the ultrasound scanner 100). The central unit 112 comprises several components connected to each other via a bus structure 124. In particular, a microprocessor (μP) or higher 127 provides the logic capabilities of the central unit 112. A non-volatile memory (ROM) 130 stores basic code for bootstrapping the central unit 112, and a volatile memory (RAM) 133 is used by the microprocessor 127 as working memory. The central unit 112 has a mass memory 136, for example a solid-state disk (SSD), for storing programs and data. Furthermore, the central unit 112 includes several controllers 139 for peripherals or input / output (I / O) units including the imaging probe 109, monitor 115, keyboard 118, and trackball 121, as well as a network adapter (not shown) for connecting the ultrasound scanner 100 to a network, a drive for reading and writing to removable storage units (such as a USB key), etc.

[0028] Referring to FIG. 2, an exemplary image 200 that may be acquired with this imaging system is shown.

[0029] In particular, during each imaging procedure, a sequence of (input) images is provided. For example, images are acquired from a patient's body part after administering a contrast agent, and then they are filtered to remove tissue contributions. Thus, the images essentially represent only the contrast particles present in the body part under examination. Each image is always a blurred representation of the contrast particles due to the resolution limit of the ultrasound scanner (the higher the frequency, the lower the corresponding wavelength and the higher the resolution), ranging from, for example, 500 μm at 1.5 MHz to 25 μm at 30 MHz. More specifically, each contrast particle appears as a blurred dot given by convolution with the ultrasound scanner's point spread function (PSF), which defines its response to a point source. Thus, contrast particles overlap when they are closer than the resolution limit.

[0030] For example, image 200 relates to a body part having two blood microvessels 205a and 205b (having diameters on the order of 5-20 μm). Blood microvessels 205a and 205b are not visible in image 200 (which represents only contrast agent); therefore, they have been added to image 200 as dashed lines to indicate their location in the body part, where they are closer than the resolution limit. Blood microvessels 205a and 205b contain contrast particles (having smaller diameters, such as 1-5 μm); in particular, blood microvessel 205a contains contrast particle 210a, and blood microvessel 205b contains contrast particle 210b, which are indistinguishable in image 200, hence the dashed lines. Contrast particles 210a and 210b are instead represented in image 200 as corresponding fuzzy dots 215a and 215b, respectively. The contrast particles 210a, 210b are also closer than the resolution limit, so their blur spots 215a, 215b overlap. As a result, it is impossible to distinguish the blood microvessels 205a, 205b in the image 200 using conventional ultrasound imaging techniques.

[0031] Referring now to FIG. 3, an exemplary imaging procedure based on ULM technology known in the art is shown.

[0032] In this case, any contrast particles represented by corresponding blurred dots in the image are localized to determine their position when possible, i.e., when the contrast particles are isolated, and the isolated contrast particles are then smaller (i.e., smaller than the resolution limit) than their representation in the image represented by markers corresponding to their positions.

[0033] For example, three (input) images 300(1), 300(2), and 300(3) are shown acquired over time of contrast agent particles present in the same body part of Figure 2 above. Image 300(1) includes (representations of) four contrast particles 315a, 315b, 315c, and 315d, image 300(2) includes (representations of) three contrast particles 315e, 315f, and 315g, and image 300(3) includes (representations of) three contrast particles 315h, 315i, and 315j. In image 300(1), separated contrast particles 315c and 315d are localized to determine their positions, represented by corresponding markers 320c and 320d, respectively, in image 325(1); overlapping contrast particles 315a and 315b are instead discarded. In image 300(2), separated contrast particle 315g is located to determine its position, represented by corresponding marker 320g, in image 325(2), and overlapping contrast particles 315e and 315f are instead discarded. In image 300(3), separated contrast particle 315h is located to determine its position, represented by corresponding marker 320h in image 325(3), and overlapping contrast particles 315i and 315j are instead discarded.

[0034] The positions of contrast particles 320c, 320d, 320g, and 320h located over time are accumulated in a map 330 (tracking them to reconstruct the trajectories they follow within the body along their blood microvasculature). However, in this case, many (overlapping) contrast particles are discarded. Therefore, a relatively low concentration of contrast agent is needed to increase the chance of having isolated contrast particles, e.g., 10 per mL. 3 ~10 4 contrast particles), with a corresponding increase in image acquisition time.

[0035] 4A-4D, the general principle of the solution according to an embodiment of the present disclosure is illustrated.

[0036] In this case, blinking of contrast particles is simulated. In particular, multiple blinking masks are provided. Each blinking mask alternates between multiple blocking regions and multiple pass regions. When a blinking mask is applied to the analysis region of each image (e.g., equal to the entire image), the blocking regions mask the corresponding part of the image (reset to indicate the absence of echo signals), while the pass regions do not mask the corresponding part of the image (keep it unchanged). In certain embodiments, the blinking masks are defined such that the pass regions of all blinking masks do not mask the entire analysis region of the image (thereby ensuring that applying all blinking masks to each image keeps the analysis region unchanged as a whole, with all points in the analysis region unmasked by at least one blinking mask). Each image is masked by applying a blinking mask, thereby making the (representation of) contrast particles present therein more sparse. Any distinguishable contrast particles in the (masked) image are localized to determine their location. Distinguishable contrast particles include those already separated from other contrast particles in the (original) image. However, distinguishable contrast particles also include those that overlap with other contrast particles in the (original) image but have a degree of overlap that is substantially reduced by application of the blinking mask, e.g., the total extent of each of these contrast particles and the remainder of the other overlapping contrast particles is less than 1.4 to 1.6 times (e.g., 1.5 times) the extent of each contrast particle.

[0037] For example, these figures show the application of this technique to the same image as Figure 3 above.

[0038] Starting with FIG. 4A, two blinking masks 405a and 405b are provided (in this case, having the same size as the image to which they are applied). Blinking masks 405a and 405b are divided into alternating blocking and passing regions having square shapes, represented in black and white, respectively, in the figure. Blinking masks 405a and 405b are complementary (the blocking and passing regions of blinking mask 405a correspond to the passing and blocking regions of blinking mask 405b, respectively). Blinking masks 405a and 405b are applied to image 300(1) of FIG. 3 to obtain corresponding (masked) images 410(1)a and 410(1)b, respectively. Thus, image 410(1)a preserves contrast particle 315a, and image 410(1)b preserves contrast particles 315b, 315c, and 315d, which are now all distinguishable. In image 410(1)a, (distinguishable) contrast particle 315a is localized to determine its position, represented by corresponding marker 420a in (sub) image 423(1)a. In image 410(1)b, (distinguishable) contrast particles 315b, 315c, and 315d are localized to determine their positions, represented by corresponding markers 420b, 420c, and 420d, respectively, in (sub) image 423(1)b. Images 423(1)a and 423(1)b are then combined into (localized) image 425(1), in which the positions of all contrast particles 420a-420d are represented.

[0039] Turning to FIG. 4B, blinking masks 405a and 405b are applied to image 300(2) of FIG. 3 to obtain corresponding (masked) images 410(2)a and 410(2)b, respectively. Thus, image 410(2)a retains contrast particles 315e and 315f, and image 410(2)b retains contrast particle 315g, which are now all distinguishable. In image 410(2)a, (distinguishable) contrast particles 315e and 315f are located to determine their positions in (sub) image 423(2)a, represented by corresponding markers 420e and 420f, respectively. In image 410(2)b, (distinguishable) contrast particle 315g is located to determine its position in (sub) image 423(2)b, represented by corresponding marker 420g. Images 423(2)a and 423(2)b are then combined into a (localized) image 425(2) in which the (positions of) all contrast particles 420e-420g are represented.

[0040] Turning to FIG. 4C, blinking masks 405a and 405b are applied to image 410(3) of FIG. 3 to obtain corresponding (masked) images 300(3)a and 410(3)b, respectively. Thus, image 410(3)a preserves contrast particles 315h and 315j, and image 410(3)b preserves contrast particle 315i, which are now all distinguishable. In image 410(3)a, (distinguishable) contrast particles 315h and 315j are located to determine their positions, represented by corresponding markers 420h and 420j, respectively, in (sub) image 423(3)a. In image 410(3)b, (distinguishable) contrast particle 315i is located to determine its position, represented by corresponding marker 420i, in (sub) image 423(3)b. Images 423(3)a and 423(3)b are then combined into a (localized) image 425(3), in which the (positions of) all contrast agent particles 420h-420i are represented.

[0041] 4D , (analysis) information of the body-part is then output based on (the positions of) the localized contrast particles 420a-420j. For example, the positions of the contrast particles 420a-420j are tracked to reconstruct corresponding trajectories (which they follow within the body-part along its blood microvasculature) in (cumulative) map 430. In this case, trajectory 435a followed by a contrast particle represented by markers 420b, 420c, 420g, and 420i is reconstructed, and trajectory 435b followed by another contrast particle represented by markers 420h, 420a, 420e, 420f, 420j, and 420d (corresponding to blood microvasculature 205a and 205b, respectively, in FIG. 2) is reconstructed. A super-resolution image (not shown) of the body-part can then be generated showing a representation of trajectories 435a and 435b (and thus the microvasculature of the body-part).

[0042] The above solution significantly increases the number of contrast particles that are (artificially) separated in the image, so that the contrast particles can be localized even when they overlap. This solution is then added to a relatively high (medium / high) concentration of contrast agent (e.g., 10 per mL). 5 ~10 6 This allows contrast agents to be administered using standard procedures (e.g., single bolus injection). Furthermore, any movement during the acquisition time can be reduced (e.g., by asking the patient to hold their breath), thereby limiting the corresponding artifacts.

[0043] The quality of the results obtained is then significantly improved. In particular, the localization of the contrast agent particles is more continuous, thereby facilitating their tracking. This is particularly useful for analyzing the microvasculature of a body part and providing valuable information about its morphological and / or hemodynamic properties that are indicative of some pathologies (cancer, arteriolosclerosis, diabetes, etc.).

[0044] The above results are achieved in a relatively simple manner, and the computational complexity can be easily adapted according to the contingency needs (by changing the blinking mask accordingly).

[0045] All of the above will facilitate the widespread adoption of the proposed technique in clinical practice, especially the ability to exceed the resolution limit by more than an order of magnitude (e.g., allowing contrast particles that are approximately 50 μm apart at 1.5 MHz to be distinguished down to 2.5 μm at 30 MHz).

[0046] The proposed solution facilitates the physician's task in several medical applications (e.g., diagnostic applications for detecting / monitoring lesions, therapeutic applications for delineating lesions to be treated, and surgical applications for recognizing the margins of lesions to be excised).

[0047] 5A-5D, different examples of blinking masks are shown that may be used to implement solutions according to embodiments of the present disclosure.

[0048] Each blinking mask includes a matrix of cells (arranged in corresponding rows and columns) used to mask (black cells) or not mask (white cells) corresponding elementary picture elements (e.g., pixels) of the image representing the corresponding location of the body part. Generally, the pass-through area (and also its blocking area) of each blinking mask has a range defined according to the conflicting requirements of high contrast particle isolation ability (smaller pass-through areas facilitate masking other overlapping contrast particles) and low computation time (larger pass-through areas reduce the number of blinking masks required to globally unmask the entire analysis area of ​​the image). For example, the pass-through area has a range with at least one dimension equal to a multiple of the range of representation of each contrast particle in the image (as defined by the PSF of the ultrasound scanner) by a factor F, F = 0.5-1.5, preferably F = 0.8-1.2, and even more preferably F = 0.9-1.1, such as F = 1.0. Therefore, if the size of the contrast particles is smaller than the resolution limit, the degree of representation of each contrast particle in the image corresponds to the resolution limit of the ultrasound scanner (approximately half the wavelength of the signal used to acquire the image, given by dividing the speed of ultrasound within the body part by its frequency, e.g., 1,504 m / s). In this case, at least one dimension of the pass-through region can be set to the number of cells corresponding to Fλ / 2 (i.e., 0.25λ to 0.75λ, preferably λ / 2 in the example in question, where λ is the wavelength), for example, 5 to 15 cells.

[0049] The blinking mask may comprise one or more mask sets for corresponding mask patterns. Each mask set includes multiple blinking masks with blocking and passing regions characterized by the mask set's mask pattern having corresponding displacements along one or more shift directions. The blinking masks of the mask set may be generated from a seed (blinking) mask and are repeatedly (wraparound) shifted by a shift stride along each shift direction until returning to the seed mask. Again, the shift width is defined according to the conflicting requirements of high contrast particle separation ability (lower shift width) and low computation time (higher shift width). For example, if the blocking / passing regions have the same width along the shift direction, a shift width equal to one cell requires a number of shifts equal to the width of the blocking / passing region (higher separation ability but higher computation time), while a shift width equal to the width of the blocking / passing region requires a single shift (lower computation time but lower separation ability).

[0050] Starting with FIG. 5A, a very simple blinking mask 500a is shown in a checkerboard pattern. In particular, a seed mask 500a(s) is defined to include alternating (horizontally / vertically) blocking and passing regions formed by corresponding squares with a common extent. Other blinking masks 500a are generated from the seed mask 500a(s) by shifting it along one dimension (row or column) by a selected shift stride in a wraparound manner. If the shift stride is smaller than the extent of the passing region along this dimension, each (seed / shifted) blinking mask 500a is further shifted along the other dimension (column or row) by a selected shift stride (same or different as above). For example, FIG. 5A shows nine blinking masks 500a, each 12 x 12 cells in size. The seed masks 500a(s) have blocking / passing regions formed by squares of 3 x 3 cells. Other blinking masks 500a are generated from seed mask 210a(s) by shifting seed mask 500a(s) by one cell along the rows and along the columns in a wraparound manner (moving rightward and downward, respectively).

[0051] Turning to FIG. 5B, a very simple blinking mask 500b is shown with a horizontal strip pattern. In particular, the seed mask 500b(s) is defined to include alternating blocking and passing regions formed by corresponding horizontal strips having a common width. Other blinking masks 500b are generated from the seed mask 210b(s) by shifting the seed mask 500b(s) in a wraparound manner along the rows by a selected shift stride. For example, FIG. 5B shows six blinking masks 500b, each 12 x 12 cells in size. The seed mask 500b(s) has blocking / passing regions formed by horizontal strips of 3 x 12 cells. Other blinking masks 500b are generated from the seed mask 210b(s) by shifting the seed mask 500b(s) in a wraparound manner (moving downward) along the rows by one cell in a wraparound manner.

[0052] Turning to FIG. 5C, a very simple blinking mask 500c is shown with a vertical strip pattern. In particular, seed mask 500c(s) is defined to have alternating blocking and passing regions formed by corresponding vertical strips having a common width. Other blinking masks 500c are generated from seed mask 210c(s) by shifting seed mask 500c(s) by a selected shift stride in a wraparound manner along the columns. For example, FIG. 5C shows six blinking masks 500c, each 12 x 12 cells in size. Seed mask 500c(s) has blocking / passing regions formed by vertical strips of 12 x 3 cells. Other blinking masks 500c are generated from seed mask 210c(s) by shifting seed mask 500c(s) by one cell in a wraparound manner (moving to the right) along the columns.

[0053] Turning to FIG. 5D, a very simple blinking mask 500d is shown with a downward diagonal strip pattern. In particular, seed mask 500d(s) is defined to have alternating blocking and passing regions formed by corresponding downward diagonal strips (extending downward moving from left to right) having a common width. Other blinking masks 500d are generated from seed mask 210d(s) by shifting seed mask 500d(s) in a wraparound manner along the upward diagonal direction (extending upward moving from left to right) by a selected shift stride (expanding or contracting the lower diagonal strips accordingly). For example, FIG. 5D shows four blinking masks 500d, each 12×12 cells in size. Seed mask 500d(s) has blocking / passing regions formed by downward diagonal strips with a width of two cells. Another blinking mask 500d is generated by wrap-around shifting (moving upward) the seed mask 500d(s) diagonally upward by one cell.

[0054] Turning to FIG. 5E, a very simple blinking mask 500e is shown with an upward diagonal strip pattern. In particular, the seed mask 500e(s) is defined to have alternating blocking and passing regions formed by corresponding upward diagonal strips (extending upward moving from left to right) with a common width. Other blinking masks 500e are generated from the seed mask 210e(s) by shifting the seed mask 500e(s) along the downward diagonal direction (extending downward moving from left to right) by a selected shift stride in a wraparound manner (expanding or contracting the upper diagonal strips accordingly). For example, FIG. 5E shows four blinking masks 500e, each 12 x 12 cells in size. The seed masks 500e(s) have blocking / passing regions formed by diagonally upward stripes with a width of two cells. Another blinking mask 500e is generated from seed mask 210e(s) by shifting (moving downward) seed mask 500e(s) diagonally downward by one cell in a wraparound manner.

[0055] Referring now to FIG. 6, there is shown the main software components that may be used to implement a solution according to an embodiment of the present disclosure.

[0056] In particular, all software components (programs and data) are generally designated by the reference numeral 600. The software components are typically stored in mass memory and are loaded (at least partially) into the working memory of the ultrasound scanner's central unit when the programs are executed, in addition to the operating system and other application programs not directly related to the disclosed solution (and therefore omitted from the figure for simplicity). The programs are initially installed in the mass memory, for example, from a removable storage unit or from a network. In this regard, each program may be a module, segment, or portion of code, which includes one or more executable instructions for implementing specified logical functions.

[0057] The acquisition module 603 drives the ultrasound scanner's imaging probe to acquire a series of acquired images (or frames) of each body part during an imaging procedure. The acquisition module 603 writes an acquired image repository 606 containing the acquired images of each ongoing imaging procedure. The acquired images provide a representation of a corresponding slice of the body part (defined by the scanning plane of the imaging probe), e.g., in standard intensity mode (B-mode), due to the different contributions to the corresponding echo signals of the body part's tissues and (potentially) contrast agent particles present therein. Each acquired image is defined by a bitmap containing a matrix of cells (e.g., with 512 rows and 512 columns), each cell containing a pixel value (pixel value), i.e., an elementary pixel representing a corresponding position in the body part with a (physical) size defined by the spatial density of the imaging probe's sensor, and each pixel value defines the pixel's brightness as a function of the intensity of the corresponding echo signal (e.g., from 0 to 256). The filtering module 609 filters the acquired images, particularly to remove (or at least substantially reduce) the tissue contribution (leaving substantially only the contrast agent particle contribution). The filtering module 609 reads the acquired image repository 606 and writes the filtered image repository 612. The filtered image repository 612 contains a sequence of corresponding filtered images produced by filtering the acquired images. Each filtered image is defined by a bitmap containing a matrix of cells (having the same size as the acquired image), with each cell containing a corresponding pixel value.

[0058] In a solution according to an embodiment of the present disclosure, the mask generator 615 can generate blinking masks at runtime. The mask generator 615 reads a mask definition repository 618. The mask definition repository 618 includes definitions of one or more mask sets (of blinking masks) that can be used to mask the filtered image. The mask definition repository 618 has an entry for each mask set. The entry stores an indication of the mask pattern of the mask set. In particular, the mask pattern is defined by the type, i.e., shape, and arrangement (e.g., squares arranged like a checkerboard, strips arranged horizontally, vertically, diagonally downward, diagonally upward, etc.) of the blocking / passing regions of the corresponding seed mask. Furthermore, the mask pattern is defined by the range (or all possible values) of the blocking / passing regions of the seed mask in terms of the number of cells (e.g., sides of a square, width of a strip, etc.). Furthermore, the entry includes information for generating other blinking masks of the mask set from the seed mask. In particular, the entry stores an indication of one or more shift directions (horizontal, vertical, diagonally downward, diagonally upward, etc.). For each shift direction, an entry stores all possible shift strides in terms of the number of cells (as defined by its possible range). The mask generator 615 may also expose a user interface (e.g., via a virtual slider) for selecting the precision level of the ultrasound scanner. In this case, the mask definition repository 618 also includes (generation) information for generating blinking masks for all possible precision levels. For example, the mask definition repository 618 includes a table listing precision levels along with their generation information. The generation information starts with a preferred mask set with the highest shift stride for the lowest precision level. The shift stride decreases as the precision level increases, and when a minimum shift stride value (e.g., 1 cell) is reached, the next mask set with the highest shift stride (in descending order of priority) is listed, and so on. The mask generator 615 writes a blinking mask repository 621 containing the blinking masks to be applied to the filtered image.Each blinking mask is defined by a matrix of cells (having the same size as the filtered image), each cell containing a (binary) masking flag that defines whether the cell belongs to a blocking region or a passing region, e.g., asserted (e.g., with a logical 1) for a blocking region and deasserted (e.g., with a logical 0) for a passing region. A masking module 624 masks the filtered image using the blinking mask. The masking module 624 reads the filtered image repository 612 and the blinking mask repository 621 and writes a masked image repository 627. The mask image repository 627 contains corresponding mask images generated by applying the blinking mask to each filtered image. Each mask image is defined by a bitmap containing a matrix of cells (having the same size as the filtered image), each cell storing a corresponding pixel value.

[0059] The localization module 630 locates (distinguishable) contrast particles present in the masked images. The localization module 630 reads the mask image repository 627 and writes to a localization map repository 633. The localization map repository 633 contains a sequence of localization maps corresponding to filtered images, where each localization map indicates the positions of localized contrast particles in all masked images generated from the corresponding filtered image. Each localization map is defined by a matrix of cells (having the same size as the masked image), where each cell contains a (binary) localization flag that is asserted (e.g., with a logical value 1) when a contrast particle is located in the corresponding cell in one or more masked images, and deasserted (e.g., with a logical value 0) otherwise. The tracking module 636 tracks the (positions of) the contrast particles along the localization maps to reconstruct their trajectories. The tracking module 636 reads the localization map repository 633 and writes to a tracking map repository 639. The tracking map repository 639 includes a sequence of tracking maps corresponding to the localization maps, where the tracking maps provide an indication of the trajectories of contrast particles within the body part. For example, each tracking map is defined by a matrix of cells (having the same size as the localization map), with each cell including a tracking value. The tracking value has a null value (e.g., 0) if the contrast particle was not located in the corresponding cell of the localization map, or a different value if the contrast particle was located in the corresponding cell of the localization map. In particular, if the same contrast particle was also located in a previous localization map, the tracking value includes an index of the position of the (paired) contrast particle in the previous localization map (defined by the coordinates, i.e., row and column, of the corresponding cell); otherwise, the tracking value includes an appearance flag (e.g., −1) indicating that the contrast particle has just appeared. The accumulation module 642 accumulates information about the contrast particles derived from the tracking maps. The accumulation module 642 reads the tracking map repository 639 and writes to the accumulation map repository 645.The accumulation map repository 645 includes an accumulation map defined by a matrix of cells (having the same size as the tracking map), each cell containing an accumulation value indicating a characteristic of the contrast particles located in the cell, e.g., their density and / or velocity, or otherwise a null value (e.g., 0). The analysis module 648 generates analysis information of the body part derived from the trajectories of the contrast particles. In particular, the analysis information may include one or more super-resolution images, each of which provides an indication of the contrast particles (and, in turn, of the corresponding blood vessels in the body part) along their trajectories. The super-resolution images are defined by a matrix of cells (having the same size as the accumulation map), each cell containing a pixel value that depends on a characteristic of the corresponding contrast particle, e.g., their density or velocity. Additionally or alternatively, the analysis information may include one or more global parameters of the body part or its region of interest (ROI), e.g., global parameters of morphological type (vascular density, vascular tortuosity, etc.) and / or hemodynamic type (perfusion index, blood volume, etc.). The analysis module 648 reads the cumulative map repository 645 and writes to an analysis information repository 651, which stores analysis information. The analysis module 648 also exposes a user interface for selecting the type of analysis information to be provided. The output module 654 outputs the analysis information (e.g., on the monitor of the ultrasound scanner), either alone or in association with the acquired images. To this end, the output module 654 reads the analysis information repository 651 and, optionally, the acquired image repository 606.

[0060] 7A-7B, activity diagrams illustrating the flow of activities involved in implementing a solution according to one embodiment of the present disclosure are shown.

[0061] In particular, this diagram represents an exemplary process that may be used to image a body part of a patient using method 700. In this regard, each block may correspond to one or more executable instructions for implementing a specified logical function on a central unit of an ultrasound scanner.

[0062] Before the corresponding imaging procedure begins, a medical operator (e.g., a nurse) administers a contrast agent to the patient. For example, the contrast agent is a suspension of gas-filled bubbles in a liquid carrier. The gas-filled bubbles are generally stabilized by entrapment or encapsulation of the gas or its precursor in various systems, including phospholipids, emulsifiers, oils, thickeners, sugars, proteins, or polymers. The stabilized gas-filled bubbles are generally called microvesicles. In particular, microvesicles dispersed in an aqueous medium and bound at the gas / liquid interface by a very thin envelope containing a surfactant (i.e., an amphiphile) are also known as microbubbles, while microvesicles surrounded by a solid envelope formed by lipids or natural or synthetic polymers are also known as microballoons or microcapsules. Another type of contrast agent includes suspensions of polymeric or other solid porous microparticles, which possess gas bubbles trapped within the pores of the microparticles or adsorbed onto their surfaces. An example of a commercially available contrast agent containing microvesicles is SonoVue™ by Bracco International BV. For example, the contrast agent is administered intravenously to the patient as a bolus (i.e., a single dose delivered in a syringe over a short period of time, on the order of 2-20 seconds), so that the contrast agent circulates within the patient's circulatory system to perfuse the body part.

[0063] The physician then switches on the ultrasound scanner and places its imaging probe in contact with the patient's skin in the region of the body part. In response, the process begins by proceeding from the black start circle 703 to block 706. Here, the flow of activity branches according to the ultrasound scanner's settings (e.g., manually set, defined by default, or the only one available). In particular, if the blinking mask is dynamically generated, the mask generator obtains the precision level in block 709. For example, the precision level may be manually entered by the physician (via the mask generator's user interface) or may be read from a configuration parameter (customizable starting from a default value). In response, in block 712, the mask generator retrieves generation information corresponding to the precision level from the mask definition repository (defined by one or more mask sets, each with its mask pattern, shift direction, and corresponding shift stride). In block 715, the mask generator considers the (current) mask set indicated in the generation information (starting with the first one in descending order of priority). In block 718, the mask generator constructs a seed mask of the mask set according to the mask pattern as defined by the type, i.e., shape, and arrangement, of the blocking / passing regions, as well as by their extents (save it in the corresponding repository). In block 721, the mask generator considers the (current) shift direction of the mask set (starting from the first one in any order). In block 724, the mask generator constructs another blinking mask of the mask set (save it in the corresponding repository) by shifting a previously constructed blinking mask of the mask set (starting from the seed mask) by the shift stride along the shift direction. In block 727, the mask generator verifies whether this blinking mask is the same as the seed mask (meaning all blinking masks of the mask set in the shift direction have been generated). If not, the process returns to block 724 and repeats the same operations.Conversely (when the generation of the mask sets is complete), the activity flow proceeds to block 730, where the mask generator verifies whether all shift directions of the mask set have been applied. If not, the process returns to block 721 and repeats the same operations for the next shift direction. Conversely (after all shift directions have been applied), the activity flow proceeds to block 733, where the mask generator verifies whether all mask sets have been generated. If not, the activity flow returns to block 715 and repeats the same operations for the next mask set. Conversely (when all mask sets have been generated), the activity flow proceeds to block 736, where the same point is reached directly from block 706 if the precision level has not changed since the previous imaging procedure (so that the corresponding blinking mask has already been generated) or if the blinking mask is statically defined in the corresponding repository (without a mask definition repository and mask generator).

[0064] The acquisition module then continuously acquires acquired images of the field of view including the body part (e.g., at an acquisition rate of 500-5,000 images per second) (and stores them in a corresponding repository). Specifically, at each acquisition time, the acquisition module drives the imaging probe to apply a sequence of ultrasound waves with low acoustic energy (e.g., a low mechanical index MI=0.01-0.4, resulting in negligible destruction of the contrast agent), records the responsive received echo signals (e.g., in the form of radio frequency (RF) or in-phase quadrature (IQ) signals), and processes the echo signals (e.g., pre-amplification, digitization, beamforming, video conversion, logarithmic compression, scan conversion, etc.) to create a corresponding acquired image. The acquisition module may operate in a contrast-specific imaging mode (e.g., using harmonic imaging (HI), pulse inversion (PI), power modulation (PM), or contrast pulse sequence (CPS) techniques) to substantially eliminate or at least reduce the dominant (linear) contribution of tissue in the echo signal relative to the (nonlinear) contribution of the contrast agent. In block 739, the filtering module filters each (current) acquired image (extracted from the corresponding repository) into a filtered image (which it stores in the corresponding repository). In particular, the filtering module may preprocess the acquired image (to spatially correspond the acquired image to a reference image, such as the first acquired image), for example, by applying an image intensity-based motion correction technique. In either case, the filtering module filters the acquired image to eliminate or at least substantially reduce the tissue contribution thereto. For example, the filtering module may apply a spatiotemporal filtering technique (e.g., based on singular value decomposition (SVD)) that exploits the spatiotemporal coherence of tissue motion and contrast particle motion (flowing with blood) to distinguish between them. The filtering module may also post-process the filtered image to remove noise, for example, by applying intensity thresholding techniques.

[0065] In a solution according to an embodiment of the present disclosure, in block 742, the masking module determines a reduction region of the filtered image where contrast particles are present. For example, the reduction region is set to the largest rectangle that includes all contrast particles. The reduction region may be represented by a reduction mask defined by a matrix of cells (having the same size as the filtered image), where each cell contains a (binary) reduction flag that is deasserted (e.g., with a logical value of 0) when the corresponding pixel of the filtered image belongs to the reduction region and asserted (e.g., with a logical value of 1) otherwise. In block 745, the masking module retrieves the (current) blinking mask from the corresponding repository (starting from the first one in their storage order). In block 748, the masking module generates a reduced version of the blinking mask according to the reduction region. For example, the masking module sums the reduction mask and the blinking mask pixel by pixel to keep the blinking mask unchanged in the reduction region, while all its masking flags are forced to be asserted outside the reduction region (so that the entire area outside the reduction region becomes a new blocking region). At block 751, the masking module applies the (reduced) blinking mask to the filtered image (just added to the corresponding repository) to generate the masked image (which it stores in the corresponding repository). To this end, the masking module multiplies the filtered image pixel-by-pixel with the blinking mask, preserving pixel values ​​of the filtered image in the pass regions of the blinking mask (within the reduced region), while all other pixel values ​​(block regions of the blinking mask within the reduced region, but always outside the reduced region) are reset to null values ​​(0). At block 754, the localization module locates any distinguishable contrast particles in the masked image (just added to the corresponding repository) within the reduced region. In particular, the localization module detects (distinguishable) contrast particles, for example, by applying PSF cross-correlation techniques (searching for structures that show good correlation to the PSF of the contrast particles).Next, the localization module determines the positions of these contrast particles, e.g., their centroids, by applying peak detection techniques. In block 757, the localization module accumulates the (positions of) the localized contrast particles in a localization map associated with the filtered image (in the corresponding repository). In particular, for each localized contrast particle, the localization module asserts the location flag of the corresponding cell in the localization map (all location flags are deasserted and initialized). At this stage, it may happen that the same contrast particle is localized multiple times in different masked images, each at slightly different positions (due to corresponding different masking of the contrast particle and any other overlapping contrast particles). These multiple localizations of contrast particles do not affect the information obtained about the morphology of the body part, but may result in an overestimation of its vascular density (depending on the number of localized contrast particles). Thus, if necessary, multiple localizations of contrast particles can be filtered, for example, by combining contrast particles spaced less than a (differential) threshold (e.g., 2-3 pixels, corresponding to 0.3-0.7 times the size of the contrast particle) into a single contrast particle at their center position. In block 760, the masking module verifies whether all blinking masks have been applied to the filtered image. If not, the process returns to block 745 to repeat the same operations with the next blinking mask. Conversely (after all blinking masks have been applied), the flow of activity proceeds to block 763. The tracking module generates a tracking map associated with the filtered image (and stores it in the corresponding repository) by tracking contrast particles of the current localization map (just added to the corresponding repository) relative to contrast particles of the preceding localization map (previously added to the corresponding repository), if any, and reconstructing their trajectories. For example, the tracking module attempts to pair each contrast particle of the current localization map with its corresponding contrast particle of the preceding localization map (e.g., by applying a minimum distance technique).For each contrast particle in the current localization map, if it is paired, the tracking module sets the corresponding tracking value to the coordinate of the corresponding contrast particle in the previous localization map; otherwise (always true for the first localization map), the tracking module sets the corresponding tracking value to an occurrence flag. In either case, the tracking module adds the tracking value to the corresponding cell in the tracking map (initialized with all tracking values ​​at null). In block 766, the accumulation module updates the accumulation map (initialized with all accumulated values ​​at null in the corresponding repository) by accumulating information about the contrast particles derived from the tracking map (in the corresponding repository). For example, the accumulation module determines any (paired) set defined by paired contrast particles along a minimum number (e.g., 2 to 10) of consecutive tracking maps, thereby indicating the position of the same contrast particle along them. In particular, if the minimum number is greater than 2, this allows for rejecting contrast particles with corresponding lower persistence. The accumulation module then updates the accumulation values ​​corresponding to the contrast particle positions of each paired set accordingly. For example, if the accumulation values ​​indicate contrast particle density, the accumulation module increments each of them by one. If the accumulation values ​​indicate contrast particle velocity, the accumulation module calculates the contrast particle velocity at each of those positions (as the distance from the contrast particle position in the corresponding tracking map to the contrast particle position in the previous tracking map divided by the elapsed time between them, given by the inverse of the acquisition rate) and sets the corresponding accumulation value to that (if it is null) or the average with its previous value otherwise. In block 769, the accumulation module verifies whether the accumulation map is complete. For example, this occurs in response to any stopping condition, e.g., when no new distinguishable contrast particles are located in one or more consecutive filtered images (e.g., 1-10), after a predetermined time (e.g., 10-30 seconds) from the start of the imaging procedure, or in response to a command entered into the ultrasound scanner. If not, the process returns to block 736 and repeats the same operations for the next acquired image. Conversely (when the cumulative map is complete), the flow of activity proceeds to block 772 .

[0066] At this point, the analysis module can preprocess the cumulative map (in the corresponding repository), for example, by applying spatial low-pass filtering techniques to fill in discontinuities. Proceeding to block 775, if the analysis information includes one or more super-resolution images (e.g., manually selected, defined by default, or only available), the analysis module generates each of them from the (possibly preprocessed) cumulative map (extracted from the corresponding repository) (saves it in the corresponding repository). For example, each super-resolution image is generated by rendering the corresponding cumulative value, density, or velocity according to a given color map palette (such that brightness increases with cumulative value). Proceeding to block 778, if the analysis information includes one or more global parameters in addition to or instead of the super-resolution images (e.g., again manually selected, defined by default, or only available), the analysis module calculates each of them from the (possibly preprocessed) cumulative map (extracted from the corresponding repository) (saves it in the corresponding repository). For example, the analysis module may prompt the physician to select a desired ROI (up to its entirety) within any (acquired / super-resolution) image of the body part, and then the analysis module may calculate global parameters from the corresponding accumulated values ​​(i.e., density or velocity), e.g., by averaging, integration, etc. At block 781, the output module outputs the analysis information (retrieved from the corresponding repository). The output information may be provided in different modes (e.g., manually selected, defined by default, or the only available mode). For example, the super-resolution image and / or the global parameters may be displayed alone or alongside the acquired image, each super-resolution image may be displayed overlaid on the acquired image, etc. The above operations may be repeated one or more times by selecting different types of analysis information and / or different levels of accuracy.For example, a (relatively) low precision level can be selected to be used during the imaging procedure to obtain analytical information in substantially real time, and if necessary, an increased precision level can be used for the acquired images extracted from the corresponding repository after the imaging procedure is completed to allow for deeper offline investigation of analytical information. Upon completion of the process (as indicated by a corresponding command entered into the ultrasound scanner), the flow of activity terminates at the concentric white / black stop circle 784.

[0067] Referring now to FIG. 8, there is shown a comparative example of an in vitro imaging procedure known in the art according to one embodiment of the present disclosure.

[0068] In particular, a sequence of acquired images was acquired from a microflow phantom with a high concentration of contrast agent over a short acquisition time of a few seconds, and the sequence of acquired images was processed using conventional UML techniques to obtain a corresponding super-resolution image 800a, and using the techniques described above according to embodiments of the present disclosure to obtain another corresponding super-resolution image 800b, both of which show the velocity of localized contrast agent particles using corresponding pixel values ​​with brightness that increases with velocity.

[0069] As can be seen, super-resolution image 800b shows more detail in the micro-flow phantom than super-resolution image 800a (despite the higher concentration of contrast agent and shorter acquisition time).

[0070] Variations (In order to satisfy local and specific requirements, those skilled in the art may apply many logical and / or physical modifications and changes to the present disclosure while remaining within the scope of the claims. More specifically, while the present disclosure has been described with a degree of particularity with reference to one or more embodiments thereof, it should be understood that various omissions, substitutions, and changes in form and details, as well as other embodiments, are possible. In particular, different embodiments of the present disclosure may be practiced without specific details (such as numerical values) set forth in the foregoing description to provide a more thorough understanding thereof, and conversely, well-known features may be omitted or simplified so as not to obscure the description with unnecessary detail. Furthermore, particular elements and / or method steps described in connection with any embodiment of the present disclosure may be combined with any other embodiment as a matter of general design choice. It is expressly intended that the terms "substantially," "about," "approximately," and the like, may be included within the scope of the present invention. Furthermore, items presented in the same group and in different embodiments, examples, or alternatives should not be construed as de facto equivalents of one another (but as separate, autonomous entities). In all cases, each numerical value should be read as modified according to applicable tolerances, and, unless otherwise indicated, the terms "substantially," "about," "approximately," and the like, should be understood as within 10%, preferably 5%, and even more preferably 1%. Furthermore, each range of numerical values ​​should be intended as explicitly specifying any possible number along the continuum within the range, including its endpoints. Ordinal numbers or other modifiers are merely used as labels to distinguish between elements having the same name and do not, by themselves, imply any priority, precedence, or order.The terms "including," "comprising," "having," "containing," "involving," etc. are intended to have an open, non-exhaustive meaning, i.e., not limited to the listed items; terms such as "based on," "depends on," "according to," "function of," etc., are intended to have a non-exclusive relationship, i.e., possible additional variables are involved; the term "a / an" contemplates one or more items (unless expressly indicated otherwise); and the term "means for" (or any means-plus-function formulation) contemplates any structure adapted or configured to perform the relevant function.

[0071] For example, one embodiment provides an ultrasound-type imaging method for imaging a body part of a patient. However, the body part may be of any type (e.g., organ, region thereof, tissue, bone, joint, etc.) and any state (e.g., healthy, pathological state with any pathology, etc.), may belong to any patient (e.g., human, animal, etc.), and the imaging method may be of any ultrasound type (e.g., different frequencies of ultrasound, gain, time gain compensation, etc.) and may be used in any medical imaging application (e.g., diagnosis, treatment, surgery, etc.). In any case, this imaging method may facilitate the work of a physician, but only provides intermediate results that can help the physician with medical activities (in the strict sense) that are always performed by the physician himself.

[0072] In one embodiment, the imaging method comprises the following steps under the control of a computing system: However, the computing system may be of any type (see below).

[0073] In one embodiment, the imaging method includes receiving (by a computing system) a sequence of input images. However, the input images may be received in any manner (e.g., real-time / offline, acquired, downloaded from a network, retrieved from storage, any number, any frequency, etc.) and they may be of any type (e.g., any size, chromaticity, bit depth, real / complex valued, etc.).

[0074] In one embodiment, the input images represent any contrast particles of the contrast agent contained in the body part. However, the contrast particles may be of any type (e.g., gas-filled bubbles, phase-change nanodroplets, etc.), and each input image may represent any number of contrast particles in any manner (e.g., using pixel / voxel values, in positive / negative format, using any range, etc.). The contrast agent may be administered to the patient in any manner (e.g., as a bolus via a syringe, as a continuous infusion via a pump, etc.) and at any time (e.g., before, immediately before, during, etc.) the imaging method. The contrast agent may also be administered to the patient non-invasively (e.g., orally to image the gastrointestinal tract or via a nebulizer into the respiratory tract) or without substantial physical intervention to the patient that requires specialized medical expertise or involves any health risks (e.g., intramuscularly). Furthermore, application to endogenous contrast agents (which do not require administration to the patient) is not excluded. In either case, this is a computer-implemented method that includes only steps performed by a computing system (these steps can be performed independently of the acquisition of input images and without the need for patient interaction).

[0075] In one embodiment, the imaging method includes generating (by a computing system) a plurality of mask images by applying a corresponding blinking mask from each of at least a portion of the input image. However, the blinking masks may be any number and may be provided in any manner (e.g., generated, read, downloaded, etc.), and further, the blinking masks may be applied to the input images in any manner (e.g., to all input images, to a subset thereof obtained by temporal subsampling until any stopping condition is met, to each input image in its entirety, to a portion thereof, etc.).

[0076] In one embodiment, each of the blinking masks alternates between a plurality of blocking regions and a plurality of passing regions, and the input image has masked and unmasked regions, respectively. However, the blocking / passing regions may be in any number, any pattern (e.g., regular, irregular, etc.), and any range (e.g., the same or different between them), for example, the blocking / passing regions may be in the form of a checkerboard, alternating strips with any direction, a star, a vertical rectangle, a "+"-like region, an "X"-like region, a "T"-like region, a circle, an ellipse, etc.

[0077] In one embodiment, the imaging method includes determining (by a computing system) in each of the mask images the corresponding locations of any distinguishable ones of the contrast particles that are distinguishable from any other ones of the contrast particles. However, there may be any number of distinguishable contrast particles in each mask image, and their locations may be determined in any manner (e.g., detecting distinguishable contrast particles with techniques such as PSF cross-correlation, deconvolution, entropy, etc., and determining their locations with techniques such as peak detection, weighted averaging, curve fitting, etc., locating contrast particles only in the mask image or in addition to their preliminary location in the unmasked input image, distinguishing between contrast particles that overlap to any degree, filtering or not filtering multiple detections of the same contrast particle, etc.).

[0078] In one embodiment, the imaging method includes outputting (by a computing system) analytical information of the body part, although the analytical information may be output in any manner (e.g., as post-processing of the input images, substantially simultaneously with receipt of the input images or with any delay thereafter, displayed on a monitor or virtual reality glasses, printed, transmitted remotely, etc.).

[0079] In one embodiment, analytical information is provided based on the locations of the distinguishable contrast particles, however, the analytical information may be of any type (e.g., one or more super-resolution images, global parameters, histograms, video clips, etc.) and may be based on the locations of the distinguishable contrast particles in any way (e.g., derived from their number, movement, presence, etc.).

[0080] Further embodiments provide additional advantageous features that may be omitted entirely in the basic implementation. In this regard, it is expressly contemplated that the features of each of the following embodiments may be combined with the features described above, either alone or in combination with the features of any number of the other following embodiments.

[0081] In one embodiment, the pass regions of all blinking masks do not mask the entire analysis region of the input image, however, this result can be achieved in any manner (e.g., using different blinking mask pass regions that are non-overlapping, at least partially overlapping, etc.) for any analysis region of the input image (e.g., equal to the entire extent of the input image, equal to a portion thereof that is manually or automatically determined to be of interest for the medical imaging application, etc.).

[0082] In one embodiment, the imaging method includes receiving (by a computing system) a sequence of acquired images, although the acquired images may be received in any manner (e.g., either the same or different from the input images) and may be of any type (e.g., the same number or different from the input images, and the same type or different from the input images, etc.).

[0083] In one embodiment, the acquired images are images acquired from a body part, however, the acquired images may be acquired in any manner (e.g., at different frequencies, mechanical indexes, etc.).

[0084] In one embodiment, the imaging method comprises generating (by a computing system) an input image from acquired images by filtering the tissue contribution of the body-part. However, the acquired images may be filtered in any way (e.g. by applying spatiotemporal, non-local means, high-pass or thresholding techniques, with or without any pre- and / or post-processing such as motion correction, noise removal, etc.) to reduce the tissue contribution to any extent; in any case, the possibility of receiving already filtered acquired images is not excluded (e.g. if they were acquired in a contrast eigenmode).

[0085] In one embodiment, the imaging method includes tracking (by a computing system) the positions of the distinguishable contrast particles determined in the mask image of each of the input images to reconstruct one or more of their trajectories within the body part. However, the distinguishable contrast particles may be tracked in any manner (e.g., by applying techniques such as minimum distance, Markov chains, cross-correlation, assignment, with or without persistence control of any length).

[0086] In one embodiment, the imaging method includes generating (by a computing system) analytical information according to the trajectories of the distinguishable contrast particles, although the analytical information may be generated according to the trajectories of the distinguishable contrast particles in any manner (e.g., based on the density, velocity, etc. of the distinguishable contrast particles, with or without the application of any processing to reduce discontinuities, such as based on techniques such as averaging, interpolation, sparsity promotion, etc.).

[0087] In one embodiment, the imaging method includes generating (by a computing system) analytical information including at least one super-resolution image showing the positions and trajectories of distinguishable contrast particles, although the super-resolution images may be of any number and type (e.g., in color or black and white, with distinguishable contrast particles represented along their trajectories by arbitrary markers smaller than the resolution limit, such as single pixels corresponding to the positions of the distinguishable contrast particles, spots around their positions having extents corresponding to the size of the contrast particles, etc.) and may be output in any manner (e.g., alone, together with the acquired image, overlaid on the acquired image, etc.).

[0088] In one embodiment, the imaging method includes generating (by a computing system) analytical information including at least one global parameter based on the trajectories of the distinguishable contrast particles, although the global parameters may be of any number and type (e.g., partial, different, and additional global parameters to those described above), and they may be output in any manner (e.g., in numerical form, graphical form, alone or together with the acquired image, etc.).

[0089] In one embodiment, each of the passing regions has an extent with at least one dimension equal to 0.5 to 1.5 times the extent of the representation of each of the contrast particles in the input image. However, the extent of the representation of each contrast particle may be determined in any manner (e.g., analytically, experimentally, etc.), and the passing areas may be defined accordingly in any manner (e.g., by setting any number of dimensions thereto to any multiple thereof, either the same or different therebetween).

[0090] In one embodiment, each of the pass-through regions has an area with at least one dimension equal to 0.25 to 0.75 times the wavelength of the ultrasound signal used to acquire the input images, although the possibility of setting the pass-through regions in other ways, such as according to or independently of the wavelength used to acquire (directly or indirectly) the input images, is not excluded.

[0091] In one embodiment, the blinking mask includes one or more mask sets for the corresponding mask pattern, however, there may be any number of mask sets for any mask pattern (e.g., checkerboard, strip, grid, etc.), such as having a single mask set for the checkerboard pattern, a single mask set for one of the strip patterns (corresponding to the general direction of motion of the contrast particles), two mask sets for a horizontal / vertical strip pattern, two mask sets for a diagonal down / diagonal up strip pattern, any combination thereof, etc.

[0092] In one embodiment, each of the mask sets includes a plurality of blinking masks having blocking and passing regions characterized by the mask pattern of the mask set with corresponding displacements along one or more shift directions, although the displacements may be of any type (e.g., along any number of shift directions, with any shift stride, etc.).

[0093] In one embodiment, the mask pattern comprises a checkerboard pattern with alternating rectangular blocking and passing regions along two perpendicular directions, however the checkerboard pattern may be of any type (e.g., rectangular (square / non-square) of any extent, any orientation, etc.).

[0094] In one embodiment, the mask pattern includes at least one strip pattern having blocking regions and passing regions with strip shapes extending along a common direction, but the strip pattern may be of any type (e.g., having any range of strips, the same or different, between the blocking and passing regions).

[0095] In one embodiment, the blinking mask is defined by a corresponding matrix of cells, each having multiple rows and multiple columns, however, the blinking mask may have any number of rows and columns, and the cells correspond to body part locations of any type (e.g., 2D / 3D with any size, density, etc.).

[0096] In one embodiment, the common direction of the blocking and passing regions extends along the rows, columns, first diagonal and / or second diagonal of the matrix, however, the common direction of the blocking / passing regions may be of any type (e.g., partial, different and additional directions relative to the above-mentioned directions, such as extending diagonally relative to the rows / columns at any angle).

[0097] In one embodiment, the imaging method includes providing (by a computing system) an indication of the shift directions (for each of the mask sets) and an indication of the shift stride and shift number for each of the shift directions. However, the shift directions may be of any number and any type (e.g., leftward, rightward, downward, upward, diagonal with any direction and angle, etc.), each of which may have any shift stride and shift number (e.g., the same or different between shift directions), and this information may be provided in any manner (e.g., looked up according to a selected precision level, directly input, predefined with shift numbers received or calculated according to a mask pattern, etc.).

[0098] In one embodiment, the imaging method includes providing (by a computing system) (for each of the mask sets) a seed mask of the blinking masks of the mask set. However, the seed mask may be any blinking mask of the mask set and may be provided in any manner (e.g., constructed according to its definition, retrieved from its stored representation, etc.).

[0099] In one embodiment, the imaging method includes generating (by a computing system) other blinking masks of the mask set by shifting the seed mask along each of the shift directions by a shift stride of the number of shifts in the shift direction (for each of the mask set). However, this operation may be performed at any time (e.g., at runtime to dynamically generate blinking masks for each imaging procedure, in advance to predefine blinking masks, etc.) and in any way (e.g., by simply shifting in a wraparound manner, expanding and / or shrinking pass / block regions accordingly, etc.), and in any case the possibility of generating the blinking masks in a different way (e.g., randomly) is not excluded.

[0100] In one embodiment, the imaging method includes receiving (by a computing system) an indication of a precision level for imaging the body part, although the precision level may be of any type (e.g., discrete / continuous, qualitative / quantitative, with any number of possible values ​​or ranges of values, etc.) and may be received in any manner (e.g., entered via any software / hardware input unit such as a slider, up / down button, knob, input box, etc., read from any memory structure, etc.).

[0101] In one embodiment, the imaging method includes selecting (by a computing system) a mask set according to an accuracy level, although the mask set may be selected in any manner (e.g., via a table, formula, rule, etc.) from among any number and type of available mask sets.

[0102] In one embodiment, the imaging method includes selecting (by a computing system) the range of blocking and passing regions of each blinking mask of the mask set according to a precision level, however the range of blocking / passing regions may be selected in any way (e.g., either the same or different as above) and among any values ​​(e.g., in any range, at any pitch, etc.).

[0103] In one embodiment, the imaging method includes selecting (by a computing system) a shift direction for each mask set according to a precision level, however, the shift direction may be selected in any way (e.g., either the same or different as above) and from among any number and type of available shift directions.

[0104] In one embodiment, the imaging method includes selecting (by a computing system) a shift stride for each of the shift directions for each mask set according to a precision level, however, the shift stride may be selected in any manner (e.g., the same or different as above) and among any value (e.g., within any range, any pitch, etc.).

[0105] In one embodiment, the imaging method includes determining (by a computing system) a reduced region of the input image in which (for each of the input images) the corresponding contrast particle is represented, although the reduced region may be determined in any manner (e.g., the largest rectangle containing all contrast particles, any percentage thereof, with or without excluding any single contrast particle whose distance from other contrast particles is greater than any threshold, etc.).

[0106] In one embodiment, the imaging method includes configuring (by a computing system) the application of a blinking mask to the input image according to a reduced region of the input image (for each input image). However, the application of the blinking mask may be configured in any way depending on the reduced region (e.g., forcing parts of the blinking mask outside the reduced region to be masked into the input image and always ignoring the corresponding parts of the input image, reducing the size of the blinking mask according to the reduced region directly or via an enlarged seed mask to make it regular, applying it only to the reduced region of the input image, etc.), and in any case does not exclude the possibility of applying the blinking mask uniformly to the entire input image.

[0107] In one embodiment, the imaging method includes filtering (by a computing system) any multiple determinations of different positions of each distinguishable contrast particle in each masked image of the input image, although multiple determinations of the same distinguishable contrast particle may be filtered in any way (e.g., incrementally / cumulatively, according to any minimum distance between them, using spatial filtering techniques, etc.).

[0108] In general, similar considerations apply if the same solution is implemented in an equivalent manner by using more steps or parts of similar steps with the same function, by removing some steps that are not essential, or by adding further optional steps, and further, steps may be performed in different orders, simultaneously, or in a (at least partially) interleaved manner, as long as it is within the scope of the claims.

[0109] An embodiment of the present invention provides a computer program that, when executed on a computing system, causes the computing system to perform the imaging method described above. One embodiment provides a computer program product comprising a computer-readable storage medium embodied with a computer program, the computer program being loadable into the working memory of the computing system and thereby configuring the computing system to perform the same imaging method. However, the (computer) program may be of any type (e.g., a stand-alone module, a plug-in for an existing application such as the ultrasound scanner's manager, implemented directly in the ultrasound scanner, etc.) and may be used on any computing system (see below).

[0110] Generally, similar considerations apply if the program is configured differently or if additional modules or functions are provided; similarly, memory structures may be of other types, or they may be replaced with equivalent entities (not necessarily consisting of physical storage media). The program may take any form suitable for use by a computing system to configure the computing system to perform the desired operations; in particular, the program may be in the form of, for example, external or resident software that is compiled or interpreted, firmware, or microcode (either object code or source code). The program may also be stored on any computer-readable storage medium. A storage medium is any tangible medium (as distinct from the transitory signal itself) that can hold and store instructions used by a computing system. For example, the storage medium may be of the electronic, magnetic, optical, electromagnetic, infrared, or semiconductor type; examples of such storage media include a fixed disk (which may be pre-loaded with the program), a removable disk, a memory key (e.g., USB), etc. The program may be downloaded to the computing system from a storage medium or via a network (e.g., the Internet, a wide area network, and / or a local area network, including transmission cables, optical fibers, wireless connections, and network devices), where one or more network adapters in the computing system receive the program from the network and transfer it to one or more storage devices of the computing system for storage. In either case, the solution according to the embodiments of the present disclosure lends itself to being implemented by a hardware structure (e.g., by electronic circuits integrated on one or more chips of semiconductor material), or by a combination of appropriately programmed or otherwise configured software and hardware.

[0111] One embodiment provides a computing system comprising means configured to perform the steps of the imaging method described above. One embodiment provides a computing system comprising corresponding circuitry (i.e., any hardware suitably configured, e.g., by software) for performing the same imaging method steps. However, the computing system may be of any type (e.g., a central unit of an imaging system, a separate computer, etc.).

[0112] One embodiment provides an ultrasound-type imaging system comprising the computing system described above, although the imaging system may be of any type (e.g., scanner, endoscope, laparoscope, etc.).

[0113] In one embodiment, the imaging system comprises an imaging probe for acquiring the sequence of input images, however the imaging probe may be of any type (e.g. handheld, fixed, matrix or liner type, etc.).

[0114] In one embodiment, the computing system is coupled to the imaging probe to receive the sequence of input images from the imaging probe, however, the computing system and scanner may be coupled in any manner (e.g., locally / remotely via any type of wired and / or wireless connection, etc.).

[0115] In general, if the computing system and the imaging system have different structures or have equivalent components or other operational characteristics, similar considerations apply as long as they fall within the scope of the claims. In either case, all of the components may be separated into more elements, or two or more components may be combined together into a single element, and each component may be duplicated to support parallel execution of corresponding operations. Furthermore, unless otherwise specified, any interaction between different components generally need not be continuous, but may be direct or indirect via one or more intermediaries.

[0116] One embodiment provides a medical method for imaging a body part of a patient, however, the medical method may be used to image any body part of any patient (see above).

[0117] In one embodiment, the medical method includes administering to the patient an imaging agent, however the imaging agent may be of any type and may be administered in any manner (see above).

[0118] In one embodiment, the medical method comprises acquiring a sequence of input images representing any contrast particles of a contrast agent contained in the body-part, although the input images may be acquired in any manner (see above).

[0119] In one embodiment, the medical method comprises imaging a body part according to the imaging method described above and thereby outputting analytical information, however the analytical information may be of any type and may be output in any manner (see above).

[0120] In one embodiment, the medical method includes performing a medical procedure related to the body part according to the analytical information. However, the medical procedure may be of any type (e.g., diagnostic procedure, therapeutic procedure, surgical procedure, etc.).

[0121] In one embodiment, the medical method is a diagnostic method involving assessing the health state of a body part according to analytical information. However, the proposed solution may be applied to any kind of diagnostic method in the broadest sense of the term (e.g., aiming to discover new pathologies, monitor known pathologies, etc.).

[0122] In one embodiment, the medical method is a therapeutic method comprising treating a body part according to the analytical information, however, the proposed solution may be applied to any kind of therapeutic method in the broadest sense of the term (e.g., aiming to cure a pathological condition, avoid its progression, prevent the occurrence of a pathological condition, improve patient comfort, etc.).

[0123] In one embodiment, the medical procedure is a surgical procedure involving manipulating a body part according to analytical information, however the proposed solution may be applied to any kind of surgical procedure in the broadest sense of the term (e.g. for therapeutic, preventive, aesthetic purposes, etc.).

Claims

1. 1. An ultrasound-type imaging method (700) for imaging a body part of a patient, said imaging method (700) under the control of a computing system (112) comprising: receiving, by said computing system (112), a sequence of input images (736-739) representing any contrast particles of a contrast agent contained in said body-part; generating, by the computing system (112), a plurality of mask images from each of at least a portion of the input image by applying a corresponding blinking mask (742-751), each of the blinking masks having a plurality of alternating blocking regions and a plurality of passing regions, the input image being masked in the blocking regions and unmasked in the passing regions, respectively; determining, by the computing system (112), corresponding locations of any distinguishable ones of the contrast enhancing particles that are distinguishable from any other ones of the contrast enhancing particles in each of the masked images (754-769); and outputting, by the computing system (112), analytical information of the body part based on the locations of the distinguishable contrast particles (772-781). Imaging method (700).

2. 2. The imaging method (700) of claim 1, wherein the passing regions of all the blinking masks do not mask the entire analysis region of the input image.

3. The imaging method (700) receiving (736) a sequence of acquired images of the body part by the computing system (112); and generating (739), by the computing system (112), the input image from the acquired image by filtering tissue contributions of the body part; An imaging method (700) according to claim 1 or 2.

4. The imaging method (700) determining (763-766) by the computing system (112) the positions of the distinguishable contrast particles in the masked image of each of the input images to reconstruct their one or more trajectories in the body part; and generating, by the computing system (112), the analytical information according to the trajectories of the distinguishable contrast particles (772-775); An imaging method (700) according to any one of claims 1 to 3.

5. The imaging method (700) generating, by the computing system (112), the analytical information (775-778), comprising at least one super-resolution image showing the positions and the trajectories of the distinguishable contrast-enhancing particles and / or at least one global parameter based on the trajectories of the distinguishable contrast-enhancing particles.

5. The imaging method (700) of claim 4.

6. each of the passing regions has an area having at least one dimension equal to 0.5 to 1.5 times the area of ​​the representation of each of the contrast particles in the input image; An imaging method (700) according to any one of claims 1 to 5.

7. each of the pass regions has a range equal to 0.25 to 0.75 times the wavelength of an ultrasound signal used to obtain the input image; An imaging method (700) according to any one of claims 1 to 6.

8. 8. The imaging method (700) of any one of claims 1 to 7, wherein the blinking mask includes one or more mask sets for corresponding mask patterns, each of the mask sets including a plurality of the blinking masks having the blocking regions and the passing regions characterized by the mask patterns of the mask set having corresponding displacements along one or more shift directions.

9. the mask pattern includes a checkerboard pattern having rectangular blocking regions and passing regions alternately arranged along two perpendicular directions; The imaging method (700) of claim 8.

10. the mask pattern includes at least one strip pattern having the blocking region and the passing region each having a strip shape extending along a common direction; An imaging method (700) according to claim 8 or 9.

11. the blinking masks are defined by corresponding matrices of cells, each having a plurality of rows and a plurality of columns, and the common direction of the blocking region and the passing region extends along the rows, the columns, a first diagonal and / or a second diagonal of the matrix; The imaging method (700) of claim 10.

12. The imaging method (700) includes, for each of the mask sets: providing, by said computing system (112), an indication of said shift directions and an indication of a shift stride and a shift number for each of said shift directions (709-712); providing (718), by the computing system (112), a seed mask for the blinking mask of the mask set; and generating (721-730) the other blinking masks of the mask set by shifting the seed mask along each of the shift directions by the shift stride of the number of shifts in the shift directions, by the computing system (112); An imaging method (700) according to any one of claims 8 to 11.

13. The imaging method (700) receiving, by the computing system (112), an indication of a level of accuracy for imaging the body part (709); and selecting (712) by the computing system (112) the mask sets, the ranges of the blocking regions and the passing regions of the blinking masks of each of the mask sets, the shift directions of each mask set, and / or the shift strides of each of the shift directions of each mask set according to the accuracy level; An imaging method (700) according to any one of claims 8 to 12.

14. The imaging method (700) includes, for each of the input images: determining, by the computing system (112), a reduced region of the input image in which the corresponding contrast particle is represented (742); and configuring (748), by the computing system (112), application of the blinking mask to the input image according to the reduced region of the input image; An imaging method (700) according to any one of claims 1 to 13.

15. The imaging method (700) comprises: filtering (757), by the computing system (112), any plurality of determinations of different positions of each of the distinguishable contrast particles within the mask image for each of the input images; An imaging method (700) according to any one of claims 1 to 14.

16. A computer program (600) configured, when executed on a computing system (112), to cause said computing system (112) to perform an imaging method (700) according to any one of claims 1 to 15. A computer program (600).

17. A computer program product comprising a computer readable storage medium embodying a computer program, said computer program being loadable into a working memory of a computing system, thereby configuring said computing system to perform the imaging method according to any one of claims 1 to 15. Computer program products.

18. means (136) configured to perform the steps of the imaging method (700) according to any one of claims 1 to 15, A computing system (112).

19. 16. A method for imaging an image sensor comprising: Computing system.

20. A computing system (112) according to claim 18 or 19, an imaging probe (109) for acquiring said sequence of input images, the computing system (112) is coupled to the imaging probe (109) to receive the sequence of input images; An ultrasound type imaging system (100).

21. 1. A medical method for imaging a body part of a patient, comprising: administering a contrast agent to the patient; acquiring a sequence of input images representing any contrast particles of said contrast agent contained in said body-part; imaging the body part according to the imaging method of any one of claims 1 to 15, thereby outputting the analytical information; and performing a medical procedure associated with the body part in accordance with the analytical information. Medical methods.

22. the medical method is a diagnostic method comprising assessing the health status of the body part according to the analytical information, a therapeutic method comprising treating the body part according to the analytical information, or a surgical method comprising treating the body part according to the analytical information; 22. The medical method of claim 21.

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