Characterization of contrast-enhanced ultrasound images

Abstract

This invention provides an image characterization method, an image processing system, and a non-temporary computer-readable medium, which are frameworks for characterizing contrast-enhanced ultrasound (CEUS) images. [Solution] This framework derives a luminance mode (B-mode) and contrast image sequence from a sequence of contrast-enhanced ultrasound (CEUS) images. The B-mode images and contrast images within the B-mode and contrast image sequences are spatially mutually registered and temporally subsampled. A region of interest can be selected within the frames of the B-mode and contrast image sequences. The region of interest is then characterized based on the B-mode and contrast image sequences.

Description

Technical Field

[0001] Broadly speaking, the present disclosure relates to medical imaging, and more particularly to a framework for characterizing contrast-enhanced ultrasound images.

Background Art

[0002] Ultrasonic imaging is a non-invasive medical examination that assists physicians in diagnosing and treating medical conditions. Ultrasonic imaging involves using a small probe called a transducer that is placed directly on the skin to transmit and receive high-frequency sound waves that propagate through the body. The sound waves are used to create medical diagnostic images of the internal structures of the body.

[0003] Contrast-enhanced ultrasound (CEUS) of the abdomen combines abdominal ultrasound with a special type of intravenous contrast agent to improve the visualization of blood vessels and organs. The contrast agent may contain microbubbles filled with gas to better visualize the organs and blood vessels in the abdomen and pelvis. Abdominal CEUS can evaluate the liver, spleen, kidneys, pancreas, intestines, and / or bladder for various diseases and conditions.

[0004] CEUS has the potential to significantly improve the diagnostic accuracy of ultrasound in the detection and characterization of focal liver lesions. In the case of a liver CEUS scan, the aim is to determine the type of focal liver lesion by injecting a contrast agent into the patient and observing the wash-in and wash-out characteristics (inflow and outflow characteristics). Wash-in generally refers to the enhancement of tissue or tumor after injection of the contrast agent and is seen in benign or non-cancerous tissue. Wash-out generally refers to the tissue or tumor losing the contrast agent faster than normal liver tissue and is seen in cancerous or malignant tissue. Since wash-in is very fast (e.g., within 20 - 30 seconds), a video can be saved to observe the wash-in process. Wash-out images can be acquired discretely (intermittently), for example, at one-minute intervals, over a period of about 5 minutes. However, it is often difficult for physicians to track the lesions that may be present in those videos and / or individual images.

[0005] In some applications, frames of data acquired over time are integrated. The resulting image can provide useful diagnostic information, such as showing small blood vessels or perfusion channels. However, due to operator movements (e.g., sweeping motions) or movements within the patient's body (e.g., rapid breathing), combining information between frames can result in image blurring or inaccuracies. [Overview of the Initiative]

[0006] A framework for characterizing contrast-enhanced ultrasound (CEUS) images is described here. This framework derives luminance mode (B-mode) and contrast-enhanced image sequences from a sequence of contrast-enhanced ultrasound (CEUS) images. The B-mode images and contrast-enhanced images within the B-mode and contrast-enhanced image sequences are spatially aligned (registered) and temporally subsampled. A region of interest can be selected within the frames of the B-mode and contrast-enhanced image sequences. This region of interest is then characterized based on the B-mode and contrast-enhanced image sequences.

[0007] This disclosure and many of its accompanying embodiments will be better understood and more easily obtained by referring to the following detailed description in conjunction with the drawings. [Brief explanation of the drawing]

[0008] [Figure 1] A block diagram showing an example of an imaging system. [Figure 2] A diagram illustrating an example of a characterization method. [Figure 3] A diagram illustrating a timeline plot. [Modes for carrying out the invention]

[0009] In the following description, numerous specific details, such as examples of particular components, apparatus, and methods, are described to provide an overall understanding of how to implement this framework. However, as will be obvious to those with ordinary skill in the art, these specific details are not necessarily required to implement this framework. In another example, well-known materials or methods are not described in detail to avoid unnecessarily obscuring the implementation of this framework. While various modifications and alternative forms of this framework are possible, specific embodiments are shown in the drawings as examples and are described in detail here. However, it should be understood that the invention is not intended to be limited to any particular form disclosed, but rather intended to encompass all modifications, equivalents, and alternatives that fall within the spirit and scope of the invention. Furthermore, for ease of understanding, certain method steps are described as separate steps, but these separately described steps should not be interpreted as necessarily dependent on an order in their function / execution.

[0010] As will be evident from the following description, unless otherwise specified, terms such as “segment,” “generate,” “register,” “determine / measure,” “align,” “position,” “process,” “calculate,” “select,” “estimate,” “detect,” and “track” may refer to the actions and processes of a computer system or similar electronic computing device that process and transform data represented as physical (e.g., electronic) quantities in the registers and memory of the computer system into other data similarly represented as physical quantities in the computer system memory or registers, or in other similar information storage, transmission, or display devices. Embodiments of the methods described herein may be implemented using computer software. When written in a programming language compliant with certified standards, sequences of instructions designed to implement the methods may be compiled for execution on various hardware platforms and for interfacing with various operating systems. In addition, the implementation of this framework is not described with reference to any particular programming language; it will be understood that various programming languages ​​may be used.

[0011] One aspect of this framework provides a tool for generating summaries of acquired data, enabling physicians to review data in a single still image timeline instead of viewing videos or observing individual images. Another aspect of this framework derives a spatially cross-registered image sequence of selected frames from a sequence of ultrasound images, including B-mode and contrast-enhanced images. Using both B-mode and contrast-enhanced images ensures reliable tracking of lesions throughout the imaging agent lifecycle and compensates for motion (e.g., operator and / or internal body movement), facilitating accurate visualization and measurement. Using only B-mode images does not enable accurate tracking because B-mode images are affected by contrast agent wash-in and are therefore unreliable for tracking. These and other exemplary advantages and features are described in more detail in the following description.

[0012] Figure 1 is a block diagram illustrating an example of a medical imaging system 100. In some embodiments, the system 100 includes a computer system 101 connected to a medical imaging device 114. The computer system 101 includes one or more non-temporary computer-readable media 105 (e.g., computer storage or memory devices), input / output devices 108 (e.g., monitor, mouse, touchpad or keyboard), and a processor device 104 connected to a communication module 110. The processor device 104 may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), or a combination thereof. The computer system 101 may further include support circuits such as a cache, power supply or battery, clock circuitry, and a communication bus (not shown). Various other peripheral devices such as client devices (e.g., workstations) and additional data storage devices and printing devices may also be connected to the computer system 101.

[0013] This technology can be implemented in various forms of hardware, software, firmware, a dedicated processor, or a combination thereof, as part of microinstruction code executed via an operating system, as part of an application program or software product or a combination thereof. In some embodiments, the technology described herein is implemented as computer-readable program code specifically embodied in one or more non-temporary computer-readable media 105. In particular, this technology may be implemented by a processing engine 107. The non-temporary computer-readable media 105 may include random access memory (RAM), read-only memory (ROM), magnetic flexible disks, flash memory, and other types of memory, or a combination thereof. The computer-readable program code is executed by a processor device 104 to process, for example, data acquired by a medical imaging device 114. The computer-readable program code is not limited to a specific programming language and its implementation. It will be understood that various programming languages ​​and their codings may be used to implement the teachings of this disclosure contained herein. One or each computer-readable medium 105 may be used to store databases including, but not limited to, image datasets, knowledge bases, individual subject data, medical records, subject diagnostic reports (or documents), or combinations thereof.

[0014] The communication module 110 enables the computer system 101 to communicate with external systems and / or networks. In some embodiments, the communication module 110 includes high-speed digital interfaces such as Thunderbolt®, Universal Serial Bus (USB), Multi Gigabit Ethernet (Ethernet is a registered trademark), optical fiber, waveguide technology, or wireless interfaces. Other types of interfaces are also useful. In some embodiments, the communication module 110 includes wireless signal transceivers that communicate signals using common communication protocols such as GSM (Global System for Mobile Communications), WiFi, Bluetooth®, Zigbee, LoRa®, and TCP / IP.

[0015] The medical imaging device 114 is a radiographic imaging device that acquires medical image data showing internal structures hidden by the skin and bones of a subject. In some embodiments, the medical imaging device 114 is a contrast-enhanced ultrasound (CEUS) system. The CEUS system acquires and displays B-mode images of tissue together with contrast-enhanced images that are overlaid on or displayed side-by-side with the B-mode images. The CEUS system may include a transmitting beamformer, a transducer, and a receiving beamformer. Additional or different components may be provided, or fewer components may be provided. For example, a separate memory may be provided for buffering or storing frames of data.

[0016] Since some of the components and method steps of the system configuration shown in the drawings can be implemented in software, it should be further understood that the actual connections between system components (or process steps) may differ depending on how this framework is programmed. Considering the teachings provided herein, a person with ordinary knowledge in the relevant field may be able to conceive of these and similar embodiments or configurations of this framework.

[0017] Figure 2 shows an example of characterization method 200. It should be understood that each step of method 200 can be performed in the order shown or in a different order. Additional or different steps may be provided, or there may be fewer steps. Furthermore, method 200 can be implemented using system 100 in Figure 1, another system, or a combination thereof.

[0018] In step 202, the processing engine 107 receives a sequence of contrast-enhanced ultrasound (CEUS) images of at least one structure of interest. The sequence of CEUS images is acquired by the medical imaging device 114 (e.g., in real time) or is previously generated and retrieved from, for example, a non-temporary computer-readable medium 105. The sequence of CEUS images may be substantially continuous or periodic (e.g., acquired once or multiple times per cardiac cycle). The CEUS sequence may also be a video containing frames of data representing scanned regions of interest or structures of interest at separate time points after the administration of the contrast agent.

[0019] The structure of interest may also be any anatomical structure (e.g., the liver) that is identified for further investigation. The structure of interest includes a region that contains or is likely to contain a contrast agent. The contrast agent may respond to ultrasound energy. Some or all of the frames of the sequence include responses from tissue or fluid along with information from the contrast agent.

[0020] In some embodiments, CEUS image sequences are acquired in two modes. Both B-mode images and contrast-enhanced images are recorded simultaneously, for example, at the same frame rate and duration. B-mode images, also known as luminance-mode images, are two-dimensional ultrasound images produced by converting the amplitude of echoes into varying shades of gray. Contrast-enhanced images are two-dimensional color images that reflect the spatial distribution of a contrast agent (e.g., microbubbles). Contrast-enhanced images are produced by detecting and separating nonlinear components from both linear and nonlinear signals from the contrast agent in the tissue.

[0021] In step 204, the processing engine 107 derives B-mode and contrast-enhanced image sequences from the CEUS image sequence. The B-mode and contrast-enhanced image sequences include a series (continuous group) of B-mode images and a corresponding series (continuous group) of contrast-enhanced images extracted from the CEUS image sequence. Each frame in the B-mode series corresponds to a frame in the contrast-enhanced image series and is spatially mutually registered. In concurrent processing mode, paired B-mode images and contrast-enhanced images can be acquired in parallel. In alternating mode, paired B-mode images and contrast-enhanced images can be acquired alternately. Typically, B-mode images have better image quality in alternating mode, but the contrast-enhanced and B-mode images are not temporally aligned. Parallel processing mode provides temporally aligned B-mode and contrast-enhanced images.

[0022] The B-mode and contrast-enhanced images in the derived image sequence are spatially cross-registered and temporally subsampled. In some embodiments, deriving the image sequence involves subsampling frames from a sequence of CEUS images that are equally spaced in time. In other words, the time intervals between acquisition times of consecutive frames in each series are the same.

[0023] Alternatively, deriving an image sequence involves subsampling frames from a sequence of CEUS images at non-uniform time intervals. In this embodiment, specific frames are selected from the sequence of CEUS images to create a series of B-mode images and a series of contrast images. The frames can be selected for inclusion in each series based on at least one characteristic of the image data. The characteristics include, but are not limited to, motion displacement and similarity between frames. For example, frames with significant motion and / or frames that closely resemble previously selected frames are excluded from the selection for inclusion. The selected frames are the "best" for quantification of tissue perfusion. The "best" frames have no significant motion (e.g., out-of-plane motion) and have sufficient signal intensity. This signal intensity indicates sufficient penetration of the contrast agent and appropriate sampling of the temporal dynamics. Out-of-plane motion cannot be compensated, but in-plane motion can be compensated using registration techniques.

[0024] The frames within each series can be spatially aligned to compensate for in-plane motion between the frames. In-plane motion can be caused by transducer movement, patient movement, and / or movement of tissue within the region of interest. To compensate for the motion, relative translations (parallel movements) and / or rotations along one or more dimensions are determined. Data from one frame is correlated with different regions within another data frame to identify the best or sufficient match. Correlation, cross-correlation, sum of absolute differences, and / or another measure of similarity can be used. A rigid body motion model or a non-rigid body motion model can be provided, such as adding warping (distortion) to translations and rotations in the non-rigid body motion model. The entire frame of data or a window of data can be used to determine the best match and the corresponding motion.

[0025] In some embodiments, extracted B-mode and contrast-enhanced images are spatially cross-registered to compensate for (or correct) motion (e.g., in-plane motion). Unlike conventional methods, this framework uses B-mode images in conjunction with contrast-enhanced images for motion correction. B-mode and / or contrast-enhanced images may be used for motion correction based on the timing of contrast agent administration. For example, only B-mode images acquired in the early wash-in phase may be used for motion correction because they provide good representation of the lesion boundary, while only contrast-enhanced images acquired in the late wash-out phase may be used for motion correction because they provide good information. Early wash-in generally refers to a wash-in that is clearly detectable before a predetermined time (e.g., 60 seconds) has elapsed after contrast agent administration. Late wash-out generally refers to a wash-out that is clearly detectable after a predetermined time (e.g., 60 seconds) has elapsed or thereafter after contrast agent administration. Alternatively, if the lesion-liver contrast is high and the B-mode images are not affected by contrast agent filling, both B-mode and contrast-enhanced images may be used for motion correction.

[0026] In step 206, the processing engine 107 selects at least one region of interest in the first frame of a series of B-mode images and / or a series of contrast-enhanced images. The region of interest in the first frame may be selected, for example, by an operator via a user interface. Alternatively, the region of interest may be detected semi-automatically or automatically in the first frame, for example, by machine learning techniques. The region of interest may correspond to a lesion or other abnormality in a structure of interest. Two or more regions of interest (e.g., multiple lesions) may be selected.

[0027] In step 208, the processing engine 107 characterizes the region of interest based on the derived image sequence. The output of the characterization can be, for example, qualitative parameters of the region of interest, quantitative parameters of the region of interest, a timeline plot, or a combination thereof. The characterization output can be presented (or displayed), for example, in a user interface on a user device to facilitate diagnosis. The characterization output can also be stored, for example, in an electronic health record accessed by the patient and healthcare providers.

[0028] To generate qualitative parameters, the derived image sequence can be input into a diagnostic function module. The B-mode image series can be used, for example, to evaluate the size, volume, shape, echogenicity, and location of the region of interest, while the contrast-enhanced image series can be used, for example, to evaluate hemodynamic and angiogenesis patterns, such as arterial-phase contrast agent wash-in characteristics (e.g., filling pattern, dynamics, timing) and portal-phase wash-out characteristics (e.g., degree of wash-out, timing). Additionally, the characteristics can further include the type of lesion (e.g., benign or malignant). The diagnostic function module can implement deep learning techniques or other appropriate techniques, for example, to extract contrast agent characteristics or to predict lesion type (e.g., malignant, benign), lesion class (e.g., hepatocellular carcinoma or HCC, focal nodular hyperplasia or FNH, hemangioma), or Liver Imaging Reporting and Data System (LI-RADS) category.

[0029] In some embodiments, the processing engine 107 performs quantification of the region of interest based on the derived image sequence and generates quantitative parameters. Quantification may be performed by extracting a time-luminance ratio curve between the calculated lesion and liver luminance, or by using a deep learning-based technique. The curve may provide information regarding contrast agent wash-in and wash-out characteristics, as described above. The processing engine 107 may select the "best" frame from each of the series for quantification, for example, by using similarity measurements to a reference frame (e.g., a first frame) or by using a deep learning-based technique used in object tracking.

[0030] In some embodiments, the processing engine 107 generates a timeline plot from the derived image sequence that tracks the region of interest. This timeline plot is a visual representation of a series of B-mode images adjacent to (e.g., above or below) a series of contrast-enhanced images progressing over time. The timeline plot can provide a single static representation of the characteristics of the region of interest, such as arterial phase (AP) wash-in patterns (e.g., centrifugal, concentric, rim-like, spherical) and wash-out timings.

[0031] Figure 3 shows a timeline plot 302 as an example. The timeline plot 302 includes a series of B-mode images 304a-b displayed on top of a series of contrast-enhanced images 306a-b. Although a static (still image) horizontal timeline plot 302 is shown, it should be understood that other configurations of timeline plots (e.g., vertical) are also possible. The timeline plot 302 includes both arterial phase (AP) wash-in images (304a, 306a). The AP wash-in images (304a, 306a) are finely subsampled and pre-selected. Additionally, portal phase wash-out images (304b, 306b) may also be included. The portal phase wash-out images (304b, 306b) may be sparsely sampled. The acquisition time 308 may be displayed on top of the timeline plot 302.

[0032] Additional timeline plots may be generated to represent additional regions of interest relative to a single region of interest, or multiple contrast agent administrations. For example, multiple lesions may exist in the liver of one person, and multiple contrast agent injections may be administered to visualize a single lesion. Multiple timeline plots may be shown to the user to provide additional information about contrast agent behavior. Multiple timeline plots for multiple lesions may be shown simultaneously to facilitate visual comparison of lesions.

[0033] The following is a list of non-limiting exemplary embodiments disclosed herein.

[0034] Exemplary Embodiment 1: A method for characterizing images, Receiving a sequence of contrast-enhanced ultrasound (CEUS) images, The luminance mode (B-mode) and contrast-enhanced image sequence are derived from the sequence of CEUS images, and the B-mode images and contrast-enhanced images within the B-mode and contrast-enhanced image sequence are spatially mutually registered and temporally subsampled. Selecting at least one region of interest in the B-mode and contrast-enhanced image sequences, A method comprising characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences.

[0035] Exemplary Embodiment 2: The method of Exemplary Embodiment 1, The method for deriving the B-mode and contrast-enhanced image sequences includes subsampling frames that are equally spaced in time.

[0036] Exemplary Embodiment 3: A method according to exemplary embodiment 1 or 2, The method for deriving the B-mode and contrast-enhanced image sequences includes subsampling frames with non-uniform time intervals.

[0037] Exemplary Embodiment 4: The method of the exemplary embodiment 3, A method by which the frame is selected from the sequence of CEUS images based on motion displacement, similarity between frames, or a combination thereof.

[0038] Exemplary Embodiment 5: One of the exemplary embodiments 1 to 4, A method for deriving the B-mode and contrast-enhanced image sequences, comprising spatially registering the B-mode image and the contrast-enhanced image for motion correction.

[0039] Exemplary Embodiment 6: The method of exemplary embodiment 5, A method in which only B-mode images acquired in the early wash-in phase and only contrast-enhanced images acquired in the late wash-out phase are used for motion correction.

[0040] Exemplary Embodiment 7: The method of exemplary embodiment 5, A method in which both the B-mode image and the contrast-enhanced image are used for motion correction.

[0041] Exemplary Embodiment 8: One of the exemplary embodiments 1 to 7, A method for characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences, comprising generating one or more qualitative parameters.

[0042] Exemplary Embodiment 9: One of the exemplary embodiments 1 to 8, A method for characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences, comprising generating one or more quantitative parameters.

[0043] Exemplary Embodiment 10: One of the exemplary embodiments 1 to 9, A method for characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences, comprising generating a timeline plot.

[0044] Exemplary Embodiment 11: A method of exemplary embodiment 10, The method for generating the aforementioned timeline plot includes generating a static horizontal timeline plot.

[0045] Exemplary Embodiment 12: One of the exemplary embodiments 1 to 11, A method for characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences, comprising generating multiple timeline plots relating to multiple contrast agent administrations to the region of interest.

[0046] Exemplary Embodiment 13: An image processing system, A non-temporary memory device for storing computer-readable program code, A processor device that communicates with the aforementioned non-temporary memory device, The processor device, according to the computer-readable program code, The process involves deriving a luminance mode (B-mode) and a contrast-enhanced image sequence from a sequence of contrast-enhanced ultrasound (CEUS) images, and spatially registering the B-mode images and the contrast-enhanced images within the B-mode and contrast-enhanced image sequences, while also subsampling them temporally. Selecting at least one region of interest in the B-mode and contrast-enhanced image sequences, An image processing system that operates to perform the step of characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequence.

[0047] Exemplary Embodiment 14: An exemplary embodiment 13 of the image processing system, The processor device, according to the computer-readable program code, An image processing system that operates to derive the B-mode and contrast-enhanced image sequences using subsampling frames that are equally spaced in time.

[0048] Exemplary Embodiment 15: An exemplary embodiment 13 or 14 of the image processing system, The processor device, according to the computer-readable program code, An image processing system that operates to derive the B-mode and contrast-enhanced image sequences using subsampling frames with non-uniform time intervals.

[0049] Exemplary Embodiment 16: An image processing system, which is one of the exemplary embodiments 13 to 15, The processor device, according to the computer-readable program code, An image processing system that operates to select at least one region of interest by selecting one or more lesions.

[0050] Exemplary Embodiment 17: An image processing system, which is one of the exemplary embodiments 13 to 16, The processor device, according to the computer-readable program code, An image processing system that operates to characterize the at least one region of interest based on the B-mode and contrast-enhanced image sequence by generating one or more qualitative parameters, quantitative parameters, timeline plots, or combinations thereof.

[0051] Exemplary Embodiment 18: An image processing system, which is one of the exemplary embodiments 13 to 17, The processor device, according to the computer-readable program code, An image processing system that operates to characterize the at least one region of interest based on the B-mode and contrast-enhanced image sequences by generating a static horizontal timeline plot.

[0052] Exemplary Embodiment 19: An image processing system, which is one of the exemplary embodiments 13 to 18, The processor device, according to the computer-readable program code, An image processing system that operates to characterize the at least one region of interest based on the B-mode and contrast-enhanced image sequence by generating multiple timeline plots relating to multiple contrast agent administrations to the region of interest.

[0053] Exemplary Embodiment 20: One or more non-temporary computer-readable media implementing machine-executable instructions, The aforementioned instruction is, The process involves deriving a luminance mode (B-mode) and a contrast-enhanced image sequence from a sequence of contrast-enhanced ultrasound (CEUS) images, and spatially registering the B-mode images and the contrast-enhanced images within the B-mode and contrast-enhanced image sequences, while also subsampling them temporally. Selecting at least one region of interest in the B-mode and contrast-enhanced image sequences, A non-temporary computer-readable medium is an instruction for performing an operation which includes characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences.

[0054] While the framework of this disclosure has been described in detail with reference to exemplary embodiments, those with ordinary skill in the art will understand that various modifications and substitutions can be made without departing from the spirit and scope of the invention as described in the claims. For example, elements and / or features of each exemplary embodiment can be combined with and / or substituted for each other within the scope of this disclosure and claims.

Claims

1. A method for image feature identification, Receiving a sequence of contrast-enhanced ultrasound (CEUS) images, To derive the brightness mode (B-mode) and contrast-enhanced image sequence from the aforementioned CEUS image sequence, The B-mode image and the B-mode image and contrast-enhanced image within the aforementioned B-mode and contrast-enhanced image sequence are spatially mutually registered and temporally subsampled. Selecting at least one region of interest in the B-mode and contrast-enhanced image sequences, A method comprising characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences.

2. The method according to claim 1, wherein the derivation of the B-mode and contrast-enhanced image sequence includes subsampling frames that are equally spaced in time.

3. The method according to claim 1, wherein the derivation of the B-mode and contrast-enhanced image sequence includes subsampling frames with non-uniform time intervals.

4. The method according to claim 3, wherein the frame is selected from the sequence of CEUS images based on motion displacement, similarity between frames, or a combination thereof.

5. The method according to claim 1, wherein deriving the B-mode and contrast-enhanced image sequences includes spatially registering the B-mode image and the contrast-enhanced image for motion correction.

6. The method according to claim 5, wherein only B-mode images acquired in the early wash-in phase and only contrast-enhanced images acquired in the late wash-out phase are used for motion correction.

7. The method according to claim 5, wherein both the B-mode image and the contrast-enhanced image are used for motion correction.

8. The method according to claim 1, wherein characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequence comprises generating one or more qualitative parameters.

9. The method according to claim 1, wherein characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences comprises generating one or more quantitative parameters.

10. The method according to claim 1, wherein characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequence includes generating a timeline plot.

11. The method according to claim 10, wherein generating the timeline plot includes generating a static horizontal timeline plot.

12. The method according to claim 1, wherein characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequence comprises generating a plurality of timeline plots relating to a plurality of contrast agent administrations to the region of interest.

13. An image processing system, A non-temporary memory device for storing computer-readable program code, A processor device that communicates with the aforementioned non-temporary memory device, The processor device, according to the computer-readable program code, To derive the brightness mode (B-mode) and contrast image sequence from the sequence of contrast-enhanced ultrasound (CEUS) images. The B-mode image and the B-mode image and contrast-enhanced image within the aforementioned B-mode and contrast-enhanced image sequence are spatially mutually registered and temporally subsampled. Selecting at least one region of interest in the B-mode and contrast-enhanced image sequences, An image processing system that operates to perform the step of characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequence.

14. The processor device, according to the computer-readable program code, The image processing system according to claim 13, which operates to derive the B-mode and contrast-enhanced image sequences using subsampling frames that are equally spaced in time.

15. The processor device, according to the computer-readable program code, The image processing system according to claim 13, which operates to derive the B-mode and contrast-enhanced image sequences using subsampling frames with non-uniform time intervals.

16. The processor device, according to the computer-readable program code, The image processing system according to claim 13, which operates to select at least one region of interest by selecting one or more lesions.

17. The processor device, according to the computer-readable program code, The image processing system according to claim 13, which operates to characterize the at least one region of interest based on the B-mode and contrast-enhanced image sequence by generating one or more qualitative parameters, quantitative parameters, timeline plots, or combinations thereof.

18. The processor device, according to the computer-readable program code, The image processing system according to claim 13, which operates to characterize the at least one region of interest based on the B-mode and contrast-enhanced image sequences by generating a static horizontal timeline plot.

19. The processor device, according to the computer-readable program code, The image processing system according to claim 13, which operates to characterize the at least one region of interest based on the B-mode and contrast image sequence by generating a plurality of timeline plots relating to the administration of a plurality of contrast agents to the region of interest.

20. One or more non-temporary computer-readable media implementing machine-executable instructions, The aforementioned instruction is, To derive the brightness mode (B-mode) and contrast image sequence from the sequence of contrast-enhanced ultrasound (CEUS) images. The B-mode image and the B-mode image and contrast-enhanced image within the aforementioned B-mode and contrast-enhanced image sequence are spatially mutually registered and temporally subsampled. Selecting at least one region of interest in the B-mode and contrast-enhanced image sequences, A non-temporary computer-readable medium is an instruction for performing an operation which includes characterizing the at least one region of interest based on the B-mode and contrast-enhanced image sequences.

Images