Patent Publication Number: US-2022211352-A1

Title: System and method for utilizing deep learning techniques to enhance color doppler signals

Description:
BACKGROUND 
     The subject matter disclosed herein relates to ultrasound image processing and, more particularly, utilizing deep learning techniques to enhance ultrasound color Doppler signals. 
     Ultrasound color flow imaging is a Doppler technique utilized in medical diagnostics to assess the dynamics and spatial distribution of blood flow. The color Doppler signal contains blood flow information but also clutter or motion artifacts (e.g., due to the pulsation of vessel walls, heart motion, intestinal peristalsis, etc.). During signal processing, a filter (e.g., clutter filter such as a wall filter, singular value decomposition filter, etc.) to reduce the clutter may be applied to the color Doppler signals to enable obtaining high quality ultrasound color flow images. These filters include a threshold (e.g., tissue/blood threshold) or cut off based on empirical values to remove the clutter signals. However, for fine blood vessels, if the threshold is too low or small, the tissue signal may be mixed with the blood signal in the color Doppler signal and the generated image may be of poor quality due to the color Doppler signal overwhelming the displayed blood vessels (e.g., the color Doppler signal being displayed on and beyond the walls of the blood vessels as opposed to within the walls); thus, making it difficult to visualize the fine blood vessels. If the threshold is too high, the color Doppler signal may be cut off and the color Doppler signal displayed within the fine blood vessels may be difficult to visualize. 
     BRIEF DESCRIPTION 
     A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. 
     In one embodiment, a computer implemented method is provided. The method includes receiving, via a processor, a first ultrasound color Doppler image having a color Doppler signal that is inaccurate. The method also includes outputting, via the processor utilizing a generative adversarial network (GAN) system that has been trained, a second ultrasound color Doppler image based on the first ultrasound color Doppler image, wherein the second ultrasound color Doppler image accurately represents the color Doppler signal. 
     In another embodiment, a computer implemented method is provided. The method includes training, via a processor, a generative adversarial network comprising a generator and a discriminator. Training includes providing to the generator, via the processor, a first ultrasound color Doppler image having an inaccurate color Doppler signal. Training also includes generating at the generator, via the processor, a first distribution-based image based on the first ultrasound color Doppler image. Training further includes determining at the discriminator, via the processor, whether a color Doppler signal of the first distribution-based image is accurately represented within the first distribution-based image by comparing the first distribution-based image to a second ultrasound color Doppler image having an accurate color Doppler signal. Training even further includes determining at the discriminator, via the processor, whether a color Doppler signal of the first distribution-based image is accurately represented within the first distribution-based image by comparing the first distribution-based image to a second ultrasound color Doppler image having an accurate color Doppler signal. 
     In a further embodiment, a generative adversarial network (GAN) system is provided. The GAN system includes a generator sub-network configured to receive a first ultrasound color Doppler image having an inaccurate color Doppler signal, wherein the generator sub-network is configured to generate a distribution-based image based on the first ultrasound color Doppler image. The GAN system also includes a discriminator sub-network configured to determine one or more loss functions indicative of errors in the distribution-based image based on a comparison of the first ultrasound color Doppler image to the second ultrasound color Doppler image having an accurate color Doppler signal. The generator sub-network is configured to be updated based on the one or more loss functions so that the generator sub-network generates subsequent distribution-based images having respective color Doppler signals that are more accurate that previous iterations of the distribution-based images. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  is an embodiment of a block diagram of an ultrasound system, in accordance with aspects of the present disclosure; 
         FIG. 2  is an embodiment of a schematic diagram of the generation of color Doppler images from the clutter filtering of color Doppler signals; 
         FIG. 3  is an embodiment of a schematic diagram of a neural network architecture for use in image processing (e.g., enhancing color Doppler signals), in accordance with aspects of the present disclosure; 
         FIG. 4  is an embodiment of a flow chart of a method for training a generative adversarial network (GAN), in accordance with aspects of the present disclosure; and 
         FIG. 5  is an embodiment of a flow chart of a method for utilizing a trained GAN to enhance color Doppler signals in ultrasound color Doppler images, in accordance with aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     When introducing elements of various embodiments of the present invention, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments. 
     Some generalized information is provided to provide both general context for aspects of the present disclosure and to facilitate understanding and explanation of certain of the technical concepts described herein. 
     Deep-learning (DL) approaches discussed herein may be based on artificial neural networks, and may therefore encompass one or more of deep neural networks, fully connected networks, convolutional neural networks (CNNs), perceptrons, encoders-decoders, recurrent networks, wavelet filter banks, u-nets, generative adversarial networks (GANs), or other neural network architectures. The neural networks may include shortcuts, activations, batch-normalization layers, and/or other features. These techniques are referred to herein as deep-learning techniques, though this terminology may also be used specifically in reference to the use of deep neural networks, which is a neural network having a plurality of layers. 
     As discussed herein, deep-learning techniques (which may also be known as deep machine learning, hierarchical learning, or deep structured learning) are a branch of machine learning techniques that employ mathematical representations of data and artificial neural networks for learning and processing such representations. By way of example, deep-learning approaches may be characterized by their use of one or more algorithms to extract or model high level abstractions of a type of data-of-interest. This may be accomplished using one or more processing layers, with each layer typically corresponding to a different level of abstraction and, therefore potentially employing or utilizing different aspects of the initial data or outputs of a preceding layer (i.e., a hierarchy or cascade of layers) as the target of the processes or algorithms of a given layer. In an image processing or reconstruction context, this may be characterized as different layers corresponding to the different feature levels or resolution in the data. In general, the processing from one representation space to the next-level representation space can be considered as one ‘stage’ of the process. Each stage of the process can be performed by separate neural networks or by different parts of one larger neural network. 
     The present disclosure provides for utilizing deep learning techniques to enhance color Doppler signals from fine blood vessels. In particular, a generative adversarial network (GAN) system or model is trained to receive ultrasound color Doppler images (i.e., grayscale images with superimposed color Doppler signals) of a fine blood vessel area having inaccurate color Doppler signals and to output ultrasound color Doppler images having accurate color Doppler signals. The color Doppler signals of the received ultrasound color Doppler images were filtered via a clutter filter (e.g., a singular value decomposition filter or a wall filter). Due to an empirical threshold utilized by the clutter filter, the filtered color Doppler signals may be inaccurate. For example, the color Doppler signal may have been cutoff (e.g., due to utilization of a threshold that is too large), thus, making the color Doppler signal displayed within the fine blood vessels difficult to visualize. In another scenario, the color Doppler signal may have a blood signal mixed with a tissue signal (e.g., due to utilization of a threshold that is too small) resulting in the color Doppler signal overwhelming the displayed blood vessels (i.e., blooming or color bleeding) making it difficult to visualize the fine blood vessels. The trained GAN system can improve the image quality of color Doppler images by taking an inaccurate color Doppler signal (e.g., weak color Doppler signal or color Doppler signal with blooming artifact due to mixed tissue/blood) and enhancing the color Doppler signal to generate high quality color Doppler images (i.e., equivalent to color Doppler images where an appropriate threshold was utilized during clutter filtering) with accurate color Doppler signals. 
     With the preceding in mind, and by way of providing useful context,  FIG. 1  depicts a high-level view of components of an ultrasound system  10  that may be employed in accordance with the present approach. The illustrated ultrasound system  10  includes a transducer array  14  having transducer elements suitable for contact with a subject or patient  18  during an imaging procedure. The transducer array  14  may be configured as a two-way transducer capable of transmitting ultrasound waves into and receiving such energy from the subject or patient  18 . In such an implementation, in the transmission mode the transducer array elements convert electrical energy into ultrasound waves and transmit it into the patient  18 . In reception mode, the transducer array elements convert the ultrasound energy received from the patient  18  (backscattered waves) into electrical signals. 
     Each transducer element is associated with respective transducer circuitry, which may be provided as one or more application specific integrated circuits (ASICs)  20 , which may be present in a probe or probe handle. That is, each transducer element in the array  14  is electrically connected to a respective pulser  22 , transmit/receive switch  24 , preamplifier  26 , swept gain  34 , and/or analog to digital (A/D) converter  28  provided as part of or on an ASIC  20 . In other implementations, this arrangement may be simplified or otherwise changed. For example, components shown in the circuitry  20  may be provided upstream or downstream of the depicted arrangement, however, the basic functionality depicted will typically still be provided for each transducer element. In the depicted example, the referenced circuit functions are conceptualized as being implemented on a single ASIC  20  (denoted by dashed line), however it may be appreciated that some or all of these functions may be provided on the same or different integrated circuits. 
     Also depicted in  FIG. 1 , a variety of other imaging components are provided to enable image formation with the ultrasound system  10 . Specifically, the depicted example of an ultrasound system  10  also includes a beam former  32 , a control panel  36 , a receiver  38 , and a scan converter  40  that cooperate with the transducer circuitry to produce an image or series of images  42  that may be stored and/or displayed to an operator or otherwise processed as discussed herein. A processing component  44  (e.g., a microprocessor) and a memory  46  of the system  10 , such as may be present control panel  36 , may be used to execute stored routines for processing the acquired ultrasound signals to generate meaningful images and/or motion frames (including color Doppler images with color Doppler signals superimposed on grayscale images), which may be displayed on a monitor of the ultrasound system  10 . The processing component  44  may also filter (e.g., clutter filter) the color Doppler signals utilizing a single value decomposition filter or a wall filter. The processing component  44  may further utilize a generative adversarial network (GAN) system or model stored on the memory  46  to generate ultrasound color Doppler images with enhanced color Doppler signals (e.g., improved image quality) from ultrasound color Doppler images having color Doppler signals that are inaccurate (e.g., of poor image quality). 
     In a present embodiment, the ultrasound system  10  is capable of acquiring one or more types of volumetric flow information within a vessel or vessels (e.g., fine blood vessels). That is, the plurality of reflected ultrasound signals received by the transducer array  14  are processed to derive a spatial representation that describes one or more flow characteristics of blood within the imaged vasculature. For example, in one embodiment, the ultrasound system  10  is suitable for deriving spectral or color-flow type Doppler information pertaining to one or more aspects of blood flow or velocity within the region undergoing imaging (e.g., color Doppler or color flow Doppler velocity information for planar or volume flow estimation). Similarly, various volumetric flow algorithms may be used to process or integrate acquired ultrasound data to generate volumetric flow information corresponding to the sample space inside a blood vessel. 
       FIG. 2  is an embodiment of a schematic diagram of the generation of color Doppler images from clutter filtering of color Doppler signals. As depicted, a fine blood vessel area  48  (as depicted in grayscale image  50 ) may be subjected to ultrasound color flow imaging utilizing the ultrasound system  10  described in  FIG. 1 . A filter (e.g., clutter filter) may be applied to color Doppler signal to reduce clutter or motion artifacts (e.g., due to the pulsation of vessel walls, heart motion, intestinal peristalsis, etc.). The filter may be a singular value decomposition (SVD) filter that separates the blood signal from tissue clutter and noise based on different characteristics of different components of the signal when projected onto a singular value domain. For example, a covariance matrix  52  of the color Doppler signal is subject to thresholding (e.g., one or more empirical thresholds) to remove a certain number of singular vectors from the color Doppler signal. If the threshold is too small, the color Doppler signal data utilized  54  (labeled 1 on the covariance matrix  52 ) may include the blood signal being mixed with a tissue signal resulting in the color Doppler signal overwhelming the displayed blood vessels (i.e., blooming or color bleeding) making it difficult to visualize the fine blood vessels as illustrated in the ultrasound color Doppler image  56 . If the threshold too big, the color Doppler signal data utilized  56  (labeled  3  on the covariance matrix  52 ) may cut off the blood signal and the color Doppler signal displayed within the fine blood vessels  48  may be difficult to visualize as illustrated in the ultrasound color Doppler image  58 . If the color Doppler signal data utilized  60  (labeled  2  on the covariance matrix  52 ) is between the low and high thresholds, the color Doppler signal obtained more accurately reflects the blood flow information as indicated in the ultrasound Doppler image  62 . 
     Alternatively, the filter may be a wall filter (e.g., high pass filter) that separates the blood signal from the tissue clutter and noise. In utilizing the wall filter, the color Doppler signal is subjected to thresholding (e.g., one or more empirical thresholds). The wall filter may remove low and/or high frequency portions of the color Doppler signal. The application of wall filtering to a color Doppler signal  64  is illustrated in graph  66 . Similar to the SVD filter, if the threshold is too small, the color Doppler signal data utilized  68  (labeled 1 on the graph  66 ) may include the blood signal being mixed with a tissue signal resulting in the color Doppler signal overwhelming the displayed blood vessels (i.e., blooming or color bleeding) making it difficult to visualize the fine blood vessels as illustrated in the ultrasound color Doppler image  56 . If the threshold too big, the color Doppler signal data utilized  70  (labeled 3 on the graph  66 ) may cut off the blood signal and the color Doppler signal displayed within the fine blood vessels  48  may be difficult to visualize as illustrated in the ultrasound color Doppler image  58 . If the color Doppler signal data utilized  72  (labeled 2 on the graph  66 ) is between the low and high thresholds, the color Doppler signal obtained more accurately reflects the blood flow information as indicated in the ultrasound Doppler image  62 . The inaccurate ultrasound color Doppler images  56  and  58  although having inaccurate color Doppler signals still include valuable blood flow information that may be resolved utilizing the deep learning techniques described herein. 
       FIG. 3  is a schematic diagram of the neural network architecture of a GAN system or model  74  for use in enhancing color Doppler signals from fine blood vessels. The GAN  74  includes a generator or generator sub-network or model  76  (e.g., de-convolutional neural network) and a discriminator or discriminator sub-network or model  78  (e.g., convolutional neural network). The generator  76  is trained to produce improved (in image quality due to an enhanced color Doppler signal) ultrasound color Doppler images with accurate color Doppler signals from ultrasound color Doppler signals with inaccurate color Doppler signals (e.g., due to blooming or a cutoff signal). The discriminator  78  distinguishes between real data (e.g., from ultrasound color Doppler images having accurate color Doppler signals) and generated data (generated by the generator  76 ). In addition, the discriminator  78  enables the generator  76  to generate more realistic information from the learned data distribution. 
     The GAN  74  may receive color Doppler images of poor quality  80  (e.g., having inaccurate color Doppler signals similar to the images  56 ,  58  in  FIG. 2 ). The color Doppler signals in these poor quality ultrasound color Doppler images  80  were subjected to clutter filtering (e.g., wall filtering or SVD filtering). These poor quality ultrasound color Doppler images  80  are provided to the generator  76  as an input. The generator  76  generates samples or distribution-based images  84  from these poor quality ultrasound color Doppler images  80 . The GAN  74  also receives reference images  82  (e.g., ultrasound color Doppler images having accurate color Doppler signals) that are provided to the discriminator  78  for comparison by the discriminator  78  to the reference images  82 . In particular, the discriminator  78  maps the generated images (i.e., distribution-based images  84 ) to a real data distribution D: D(x i ) [0, 1] derived from the reference images  82 . The generator  76  learns to map the representations of latent space to a space of data distribution G→   |x|,  where z∈   |x|  represents the samples from the latent space x∈   |x|  of image distribution. The generator  76  is configured to learn the distribution p θ (x), approximate to the real distribution p r (x) derived from the reference images  82 , and to generate samples p G (x) (i.e., the distribution-based images  84 ) where the probability distribution function of the generated samples p G  (x) equals the probability density function of the real samples p r (x). This can be achieved by learning directly and optimizing through maximum likelihood the differential function p θ (x) so that that p θ (x)&gt;0 and ƒ x  p θ (x)dx=1. Alternatively, the differential transformation function q θ (z) of p θ (x) can be learned and optimized through maximum likelihood where z is the existing common distribution (e.g., uniform or Gaussian distribution). 
     The discriminator  78  has to recognize the data from the real data distribution p r (x), where D indicates the estimated probability of data points x i ∈   n . In case of binary classification, if the estimated probability D(x i ): -&gt; n[0, 1] is the positive class p i  and 1-D(x i ) [0, 1] is the negative class q i , the cross entropy distribution between p i  and q i  is, L(p, q)=−Σ i   n p i  log q i . For a given point x i  and corresponding label y i , the data distribution x i  can be from the real data x i ˜p r (x) (e.g., from the reference images  82 ) or the generator data x i ˜p g  (z) (e.g., from the distribution-based images  84 ). Considering exactly half of data from the two sources such as real, fake, the generator  76  and discriminator  78  tend to fight each other in a minmax game to minimize the loss function. The loss function is as follows: 
     
       
         
           
             
               
                 
                   
                     
                       
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     where λΨ=E x˜p     r     (x     ˜     ) [(∥□ x     ˜   ∥ 2 −1) 2 ] is a term that enables overcoming the gradient vanish effect. 
     The loss function, which is indicative of errors is fed back (via back propagation) to the generator  76  and/or the discriminator  78 . This enables the generator  76  to become further trained and once trained enough to generate distribution-based images  84  (derived from the poor quality color Doppler images  80 ) that may fool the discriminator  78  and be outputted by the GAN  74  as ultrasound color Doppler images  86  having accurate color Doppler signals. The trained GAN  74  will provide higher quality images to practitioners in diagnosing patients. 
       FIG. 4  is an embodiment of a flow chart of a method  88  for training a generative adversarial network (GAN) (e.g., GAN  74  in  FIG. 3 ), in accordance with aspects of the present disclosure. The method  88  may be performed by the control panel  36  of the ultrasound system  10  in  FIG. 1  or a remote processing device. The method  88  includes receiving one or more poor quality ultrasound color Doppler images at a generator of a GAN (block  90 ). The poor quality ultrasound color Doppler images are ultrasound color Doppler signals with inaccurate color Doppler signals (e.g., due to blooming or a cutoff signal). In addition, the color Doppler signals of the poor quality ultrasound color Doppler images were subjected to clutter filtering (e.g., wall filtering or SVD filtering). The method  88  also includes receiving one or more reference images at the GAN (block  92 ). The references images are ultrasound color Doppler images having accurate color Doppler signals. In addition, the color Doppler signals of the reference images were subjected to clutter filtering (e.g., wall filtering or SVD filtering). 
     The method  88  further includes generating one or more distribution based images (i.e., ultrasound color Doppler images) based on the poor quality ultrasound color Doppler images (block  94 ). The method  88  includes comparing the distribution-based images to the reference images to determine whether the respective color Doppler signals are accurately represented within the distribution-based images (block  96 ). In particular, the comparison includes the discriminator determining one or more loss functions indicative of errors based on the comparison between the distribution-based images and the reference images. The method  88  includes updating the generator and/or discriminator based on the comparison between the distribution-based images and the reference images (block  98 ). In particular, the generator and/or discriminator is updated based on the one or more loss functions. Updating the generator based on the loss functions enables the generator to generate subsequent distribution-based images having respective color Doppler signals that are more accurate than the color Doppler signals of earlier iterations of distribution-based images. These steps in the method  88  repeat until the generator is trained to generate distribution-based images where the loss functions are minimal enough that the discriminator cannot distinguish the distribution-based images from the reference images. 
       FIG. 5  is an embodiment of a flow chart of a method  100  for utilizing a trained GAN to enhance color Doppler signals in ultrasound color Doppler images, in accordance with aspects of the present disclosure. The method  100  may be performed by the control panel  36  of the ultrasound system  10  in  FIG. 1  or a remote processing device. The method  100  includes receiving one or more poor quality ultrasound color Doppler images (e.g., as input to the generator of a GAN) (block  102 ). The poor quality ultrasound color Doppler images are ultrasound color Doppler signals with inaccurate color Doppler signals (e.g., due to blooming or a cutoff signal). In addition, the color Doppler signals of the poor quality ultrasound color Doppler images were subjected to clutter filtering (e.g., wall filtering or SVD filtering). The method  100  also includes utilizing a trained GAN on the poor quality ultrasound color Doppler images to generate improved quality ultrasound color Doppler images (e.g., having accurate color Doppler signals) based on the poor quality ultrasound color Doppler images (block  104 ). The method  100  further includes outputting the improved quality ultrasound color Doppler images (e.g., having accurate color Doppler signals) from the GAN (block  106 ). 
     Technical effects of the disclosed embodiments include utilizing deep learning techniques to enhance color Doppler signals from fine blood vessels. In particular, a generative adversarial network (GAN) system or model is trained to receive ultrasound color Doppler images (i.e., grayscale images with superimposed color Doppler signals) of a fine blood vessel area having inaccurate color Doppler signals and to output ultrasound color Doppler images having accurate color Doppler signals. The techniques provide a way to process poor quality color ultrasound Doppler images to generate improved quality color ultrasound Doppler images (e.g., having more accurate or enhanced color Doppler signals) to assist practitioners in diagnosing patients. 
     This written description uses examples to disclose the present subject matter, including the best mode, and also to enable any person skilled in the art to practice the subject matter, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.