Patent Description:
Non-invasive imaging technologies allow images of the internal structures or features of a subject (patient, manufactured good, baggage, package, or passenger) to be obtained non-invasively. In particular, such non-invasive imaging technologies rely on various physical principles, such as the differential transmission of X-rays through the target volume or the reflection of acoustic waves, to acquire data and to construct images or otherwise represent the internal features of the subject.

For example, in X-ray-based imaging technologies, a subject of interest, such as a human patient, is irradiated with X-ray radiation and the attenuated radiation impacts a detector where the attenuated intensity data is collected. In digital X-ray systems, a detector produces signals representative of the amount or intensity of radiation impacting discrete pixel regions of a detector surface. The signals may then be processed to generate an image that may be displayed for review.

In one such X-ray based technique, known as computed tomography (CT), a scanner may project fan-shaped or cone-shaped X-ray beams from an X-ray source from numerous view-angle positions on an object being imaged, such as a patient. The X-ray beams are attenuated as they traverse the object and are detected by a set of detector elements which produce signals representing the intensity of the attenuated X-ray radiation on the detector. The signals are processed to produce data representing the line integrals of the linear attenuation coefficients of the object along the X-ray paths. These signals are typically called "projection data" or just "projections". By using reconstruction techniques, such as filtered backprojection, images may be generated that represent a volume or a volumetric rendering of a region of interest of the patient or imaged object. In a medical context, pathologies or other structures of interest may then be located or identified from the reconstructed images or rendered volume.

A bad pixel in the detector may result in missing data in the sinogram domain of the acquired CT data and undesirable artifacts (ring and streak) in the reconstructed image or volume. In addition, having a bad pixel in the CT detector impacts the ability to utilize a CT system. For example, a single bad pixel in isocenter of the CT detector keeps the CT system from being utilized. A certain number of bad pixels in the CT detector also keeps the CT system from being utilized. Publication "<NPL>, discloses flat-panel radiography detectors that acquire high-quality x-ray images, specifically practical chest x-ray images, and ways to develop an appropriate defect correction algorithm for the acquired images.

The invention is defined by the appended independent claims, wherein claim <NUM> relates to a computer-implemented method for correcting artifacts in computed tomography (CT) data from a CT system, and claim <NUM> relates to deep learning-based sinogram correction system for correcting artifacts in computed tomography (CT) data from a CT system.

In one embodiment, a computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

In another embodiment, a computer-implemented method for generating a trained neural network to estimate missing values in computed tomography data is provided. The method includes providing training data including sinograms and complementary information derived from the sinograms, wherein the sinograms include sinograms without any missing pixel values and corresponding sinograms with missing pixel values simulated from the sinograms without any missing pixel values. The method also includes training, using the training data, a neural network to correct a sinogram having a pixel value missing for at least one pixel based on utilizing a combined training loss derived from both the sinogram domain of the training data and an image reconstruction domain of images reconstructed from the training data.

In a further embodiment, a deep learning-based sinogram correction system is provided. The system includes a memory encoding processor-executable routines. The system also includes a processing component configured to access the memory and to execute the processor-executable routines, wherein the routines, when executed by the processing component, cause the processing component to perform acts. The acts include inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The acts also include processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information, wherein the complementary information includes multichannel patches, and the multi-channel patches include a local neighborhood patch from the sinogram corresponding to a portion along a channel-view direction of a row having the at least one pixel a neighboring row patch from the sinogram corresponding to an adjacent row to the row having the at least one pixel, and a conjugate patch from the sinogram corresponding to a conjugate region relative to the at least one pixel. The acts further include outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

When introducing elements of various embodiments of the present subject matter, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements. Furthermore, any numerical examples in the following discussion are intended to be nonlimiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments.

While aspects of the following discussion are provided in the context of medical imaging, it should be appreciated that the present techniques are not limited to such medical contexts. Indeed, the provision of examples and explanations in such a medical context is only to facilitate discussion by providing instances of real-world implementations and applications. However, the present approaches may also be utilized in other contexts, such as industrial computed tomography (CT) used in non-destructive inspection of manufactured parts or goods (i.e., quality control or quality review applications), and/or the non-invasive inspection of packages, boxes, luggage, and so forth (i.e., security or screening applications).

As discussed herein, artifacts (ring, streak, etc.) found in CT images can be symptomatic of CT component issues. For example, a bad pixel in the CT detector may cause a ring artifact (rings centered within the center of rotation) leading to structured nonuniformities and deterioration of image quality. The rings make the CT images unusable for diagnostic purposes. In addition, if the number of bad pixels is above a certain threshold or if a bad pixel is located within a central region (system isocenter) of the CT detector, it may result in large service costs and, possibly, replacement of the CT detector.

The approach discussed herein addresses these issues by applying deep learning methods (e.g., convolutional neural networks or multilayer perceptrons) utilizing supervised learning to remove these artifacts due to the one or more bad pixels. The deep learning algorithm works in both the raw sinogram domain and the reconstruction domain to learn to remove the distortions caused by the bad pixels. In particular, the deep learning algorithm learns to remove the artifacts created by the bad pixels by learning a correlation between complementary information within the sinogram domain data. For example, in one implementation a deep neural network (or other suitable machine learning architecture) may be employed in this process. As may be appreciated, a neural network as discussed herein can be trained for use across multiple types of configurations (e.g., axial or helical scan (different pitch), varying kV/mA ratings, detector size, or bad pixel configuration (single or multiple pixels within separate or group locations)). Further, in some embodiments, more than one neural network may be utilized.

With the preceding in mind, neural networks as discussed herein may encompass deep neural networks, fully connected networks, convolutional neural networks (CNNs), perceptrons (e.g., multilayer perceptrons (MLPs)), auto encoders, recurrent networks, wavelet filter banks, or other neural network architectures. These techniques are generally referred to herein as machine learning. As discussed herein, one implementation of machine learning may be deep learning techniques, and such deep learning 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 network for learning. 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 or a different stage or phase of a process or event 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 reconstruction can be performed by separate neural networks or by different parts of one larger neural network.

As discussed herein, as part of the initial training of deep learning processes to solve a particular problem, such as identification of service issues based on identified artifacts in image data, training data sets may be employed that have known initial values (e.g., input images, projection data (e.g., sinograms with or without missing values for bad pixels in the detector), and so forth) and known or desired values for a final output (e.g., corrected sinograms, reconstructed tomographic reconstructions, such as cross-sectional images or volumetric representations). The training of a single stage may have known input values corresponding to one representation space and known output values corresponding to a next-level representation space. In this manner, the deep learning algorithms may process (in a supervised manner, i.e., all of the training data is completely labeled) the known or training data sets until the mathematical relationships between the initial data and desired output(s) are discerned and/or the mathematical relationships between the inputs and outputs of each layer are discerned and characterized. Similarly, separate validation data sets may be employed in which both the initial and desired target values are known, but only the initial values are supplied to the trained deep learning algorithms, with the outputs then being compared to the outputs of the deep learning algorithm to validate the prior training and/or to prevent over-training.

With the preceding in mind, <FIG> schematically depicts an example of an artificial neural network <NUM> that may be trained as a deep learning model as discussed herein. In this example, the network <NUM> is multi-layered, with a training input <NUM> and multiple layers including an input layer <NUM>, hidden layers 58A, 58B, and so forth, and an output layer <NUM> and the training target <NUM> present in the network <NUM>. Each layer, in this example, is composed of a plurality of "neurons" or nodes <NUM>. The number of neurons <NUM> may be constant between layers or, as depicted, may vary from layer to layer. Neurons <NUM> at each layer generate respective outputs that serve as inputs to the neurons <NUM> of the next hierarchical layer. In practice, a weighted sum of the inputs with an added bias is computed to "excite" or "activate" each respective neuron of the layers according to an activation function, such as rectified linear unit (ReLU), sigmoid function, hyperbolic tangent function, or otherwise specified or programmed. The outputs of the final layer constitute the network output <NUM> (e.g., one or more convolution kernel parameters, a convolution kernel, and so forth) which, in conjunction with the training target <NUM>, are used to compute some loss or error function <NUM>, which will be backpropagated to guide the network training.

The loss or error function <NUM> measures the difference between the network output (e.g., a convolution kernel or kernel parameter) and the training target. In certain implementations, the loss function may be a mean absolute error (MAE) (e.g., between a measured sinogram and a corrected sinogram). In certain implementations, the loss function may be a mean squared error (MSE) of the voxel-level values or partial-line-integral values (e.g., between reconstructed images derived from the measured sinogram and the corrected sinogram) and/or may account for differences involving other image features, such as image gradients or other image statistics. Alternatively, the loss function <NUM> could be defined by other metrics associated (e.g., structural similarity index measure (SSIM)) with the particular task in question, such as a softmax function. As described in greater detail below, a hybrid domain loss function may be utilized during training (e.g., loss in a sinogram domain and loss in an image reconstruction domain). The following losses may also be utilized. In the sinogram domain, content loss may be utilized (e.g., L1 and/or L2 losses are computed between target and predicted sinograms). Perceptual loss may be utilized (e.g., SSIM loss computed between sinogram and reconstruction domains). Transform domain loss may be utilized (e.g., the loss can be computed over filtered domain/wavelet domain) in the sinogram domain or the image reconstruction domain. Also, adversarial loss can also be used in the training.

In a training example, the neural network <NUM> may first be constrained to be linear (i.e., by removing all non-linear units) to ensure a good initialization of the network parameters. The neural network <NUM> may also be pre-trained stage-by-stage using computer simulated input-target data sets, as discussed in greater detail below. After pre-training, the neural network <NUM> may be trained as a whole and further incorporate non-linear units.

To facilitate explanation of the present image analysis approach using deep learning techniques, the present disclosure discusses these approaches in the context of a CT system. However, it should be understood that the following discussion may also be applicable to other image modalities and systems including, but not limited to, PET, CT, CBCT, PET-CT, SPECT, multi-spectral CT, as well as to non-medical contexts or any context where tomographic reconstruction is employed to reconstruct an image.

With this in mind, an example of a CT imaging system <NUM> (i.e., a CT scanner) is depicted in <FIG>. In the depicted example, the imaging system <NUM> is designed to acquire scan data (e.g., X-ray attenuation data) at a variety of views around a patient (or other subject or object of interest) and suitable for performing image reconstruction using tomographic reconstruction techniques. In the embodiment illustrated in <FIG>, imaging system <NUM> includes a source of X-ray radiation <NUM> positioned adjacent to a collimator <NUM>. The X-ray source <NUM> may be an X-ray tube, a distributed X-ray source (such as a solid-state or thermionic X-ray source) or any other source of X-ray radiation suitable for the acquisition of medical or other images.

In the depicted example, the collimator <NUM> shapes or limits a beam of X-rays <NUM> that passes into a region in which a patient/object <NUM>, is positioned. In the depicted example, the X-rays <NUM> are collimated to be a cone-shaped beam, i.e., a cone-beam, that passes through the imaged volume. A portion of the X-ray radiation <NUM> passes through or around the patient/object <NUM> (or other subject of interest) and impacts a detector array, represented generally at reference numeral <NUM>. Detector elements of the array produce electrical signals that represent the intensity of the incident X-rays <NUM>. These signals are acquired and processed to reconstruct images of the features within the patient/object <NUM>.

Source <NUM> is controlled by a system controller <NUM>, which furnishes both power, and control signals for CT examination sequences, including acquisition of two-dimensional localizer or scout images used to identify anatomy of interest within the patient/object for subsequent scan protocols. In the depicted embodiment, the system controller <NUM> controls the source <NUM> via an X-ray controller <NUM> which may be a component of the system controller <NUM>. In such an embodiment, the X-ray controller <NUM> may be configured to provide power and timing signals to the X-ray source <NUM>.

Moreover, the detector <NUM> is coupled to the system controller <NUM>, which controls acquisition of the signals generated in the detector <NUM>. In the depicted embodiment, the system controller <NUM> acquires the signals generated by the detector using a data acquisition system <NUM>. The data acquisition system <NUM> receives data collected by readout electronics of the detector <NUM>. The data acquisition system <NUM> may receive sampled analog signals from the detector <NUM> and convert the data to digital signals for subsequent processing by a processor <NUM> discussed below. Alternatively, in other embodiments the digital-to-analog conversion may be performed by circuitry provided on the detector <NUM> itself. The system controller <NUM> may also execute various signal processing and filtration functions with regard to the acquired image signals, such as for initial adjustment of dynamic ranges, interleaving of digital image data, and so forth.

In the embodiment illustrated in <FIG>, system controller <NUM> is coupled to a rotational subsystem <NUM> and a linear positioning subsystem <NUM>. The rotational subsystem <NUM> enables the X-ray source <NUM>, collimator <NUM> and the detector <NUM> to be rotated one or multiple turns around the patient/object <NUM>, such as rotated primarily in an x, y-plane about the patient. It should be noted that the rotational subsystem <NUM> might include a gantry or C-arm upon which the respective X-ray emission and detection components are disposed. Thus, in such an embodiment, the system controller <NUM> may be utilized to operate the gantry or C-arm.

The linear positioning subsystem <NUM> may enable the patient/object <NUM>, or more specifically a table supporting the patient, to be displaced within the bore of the CT system <NUM>, such as in the z-direction relative to rotation of the gantry. Thus, the table may be linearly moved (in a continuous or step-wise fashion) within the gantry to generate images of particular areas of the patient <NUM>. In the depicted embodiment, the system controller <NUM> controls the movement of the rotational subsystem <NUM> and/or the linear positioning subsystem <NUM> via a motor controller <NUM>.

In general, system controller <NUM> commands operation of the imaging system <NUM> (such as via the operation of the source <NUM>, detector <NUM>, and positioning systems described above) to execute examination protocols and to process acquired data. For example, the system controller <NUM>, via the systems and controllers noted above, may rotate a gantry supporting the source <NUM> and detector <NUM> about a subject of interest so that X-ray attenuation data may be obtained at one or more views relative to the subject. In the present context, system controller <NUM> may also include signal processing circuitry, associated memory circuitry for storing programs and routines executed by the computer (one or more neural networks (e.g., multi-channel sinogram correction network)), as well as configuration parameters, image data, and so forth.

In the depicted embodiment, the image signals acquired and processed by the system controller <NUM> are provided to a processing component <NUM> for reconstruction of images. The processing component <NUM> may be one or more general or application-specific microprocessors. The data collected by the data acquisition system <NUM> may be transmitted to the processing component <NUM> directly or after storage in a memory <NUM>. Any type of memory suitable for storing data might be utilized by such an exemplary system <NUM>. For example, the memory <NUM> may include one or more optical, magnetic, and/or solid state memory storage structures. Moreover, the memory <NUM> may be located at the acquisition system site and/or may include remote storage devices for storing data, processing parameters, and/or routines for tomographic image reconstruction and analysis, as described below.

The processing component <NUM> may be configured to receive commands and scanning parameters from an operator via an operator workstation <NUM>, typically equipped with a keyboard and/or other input devices. An operator may control the system <NUM> via the operator workstation <NUM>. Thus, the operator may observe the reconstructed images and/or otherwise operate the system <NUM> using the operator workstation <NUM>. For example, a display <NUM> coupled to the operator workstation <NUM> may be utilized to observe the reconstructed images and to control imaging. Additionally, the images may also be printed by a printer <NUM> which may be coupled to the operator workstation <NUM>.

Further, the processing component <NUM> and operator workstation <NUM> may be coupled to other output devices, which may include standard or special purpose computer monitors and associated processing circuitry. One or more operator workstations <NUM> may be further linked in the system for outputting system parameters, requesting examinations, viewing images, and so forth. In general, displays, printers, workstations, and similar devices supplied within the system may be local to the data acquisition components, or may be remote from these components, such as elsewhere within an institution or hospital, or in an entirely different location, linked to the image acquisition system via one or more configurable networks, such as the Internet, virtual private networks, and so forth.

It should be further noted that the operator workstation <NUM> may also be coupled to a picture archiving and communications system (PACS) <NUM>. PACS <NUM> may in turn be coupled to a remote client <NUM>, radiology department information system (RIS), hospital information system (HIS) or to an internal or external network, so that others at different locations may gain access to the raw or processed image data.

While the preceding discussion has treated the various exemplary components of the imaging system <NUM> separately, these various components may be provided within a common platform or in interconnected platforms. For example, the processing component <NUM>, memory <NUM>, and operator workstation <NUM> may be provided collectively as a general or special purpose computer or workstation configured to operate in accordance with the aspects of the present disclosure. In such embodiments, the general or special purpose computer may be provided as a separate component with respect to the data acquisition components of the system <NUM> or may be provided in a common platform with such components. Likewise, the system controller <NUM> may be provided as part of such a computer or workstation or as part of a separate system dedicated to image acquisition.

As discussed herein, the system <NUM> of <FIG> may be used to conduct a CT scan by measuring a series of views or projections from many different angles around the patient <NUM> or object. Each view has a transaxial dimension and a longitudinal dimension that correspond to the number of columns and rows, respectively of the CT detector <NUM>. The projections acquired at different view angles are combined into a sinogram, which collects the multiple views into a single data set. The sinogram represents the spatial distribution of the X-ray attenuation coefficient within the patient. Typically, the sinogram represents the spatial distribution of the X-ray attenuation coefficient over the full rotation of the CT gantry (e.g., at a single axial position). A reconstruction algorithm processes the sinogram to produce a space-domain image representing the patient <NUM> or object.

As discussed above, the CT detector may have one or more bad pixels that may result in artifacts in the reconstructed images. <FIG> is a schematic diagram depicting the training of a neural network to correct for a bad pixel. To generate training data for training a neural network (multi-channel sinogram correction network) <NUM>, a measured sinogram <NUM> (a good sinogram that is not missing any values for any pixels) is obtained utilizing the CT imaging system described above. From the measured sinogram <NUM>, a simulated sinogram <NUM> with a bad pixel is generated synthesis of bad pixels <NUM>. For example, a bad pixel mask <NUM> is utilized to simulate the bad pixel at a random location within the measured sinogram <NUM>. The bad pixel is represented by one or more lines <NUM> in the simulated sinogram <NUM> which lack any pixel values for a specific detector pixel. One or more bad pixels may be simulated in the simulated sinogram <NUM>. The simulated bad pixels may be in separate locations or grouped together in a particular location. In certain embodiments, the simulated bad pixels may correspond to the central region (isocenter) of the CT detector. The bad pixel mask <NUM> and corresponding simulated sinogram <NUM> (e.g., as a patch) are provided as inputs to the multi-channel sinogram correction network <NUM>. The network <NUM> learns to predict the missing pixel value(s) in the simulated sinograms <NUM>. In particular, the network <NUM> learns to predict the missing pixel value(s) from complementary information available in the sinogram <NUM>. The complementary information is fed in the form of multi-channel input data (e.g., multi-channel two-dimensional (2D) or three-dimensional (3D) patches) to the network <NUM> as described in greater detail below. When multiple views are utilized a 3D patch may be utilized. The complementary information may include local neighborhood sinogram information and conjugate sinogram information (e.g., information from a conjugate region relative to the location of the bad pixel (e.g., <NUM> degrees away from the bad pixel location along the CT detector)).

A corrected (e.g., estimated or predicted) sinogram (e.g., as a patch) <NUM> may be outputted by the network <NUM>. The corrected sinogram or sinogram patch <NUM> may be compared to the measured sinogram or sinogram patch <NUM> (which serves as the ground truth) to determine the training loss in the sinogram domain <NUM>. The sinogram domain loss <NUM> may be in the form of MAE loss. The sinogram loss <NUM> may be in the form of content loss (e.g., L1 and/or L2 losses are computed between target and predicted sinograms). In certain embodiments, the sinograms <NUM> may be provided to the network <NUM> as a raw sinogram. In certain embodiments, the sinogram <NUM> may be transformed (e.g., filtered) prior to being provided to the network <NUM>. Correction may then be performed in the transformed domain (e.g., wavelet domain) before eventually being transformed back to the normal or native sinogram domain and outputted. Processing the sinogram in the transform domain highlights detail features that drive the training and enhancing the sinogram domain loss.

A tomographic image or volume <NUM> is generated from the corrected sinogram <NUM> via reconstruction <NUM>. In addition, a tomographic image or volume is generated from the measured sinogram <NUM> via reconstruction <NUM>. Patches of the tomographic image or volume <NUM> is compared to patches of the tomographic image or volume (which serves as the ground truth) generated from the measured sinogram <NUM> to determine the training loss in the image reconstruction domain <NUM>. The image reconstruction domain loss <NUM> may be in the form of MSE loss or SSIM. Other losses may be utilized. For example, perceptual loss may be utilized (e.g., SSIM loss computed between sinogram and reconstruction domains). Transform domain loss may be utilized (e.g., the loss can be computed over filtered domain/wavelet domain) in the sinogram domain or the image reconstruction domain. Also, adversarial loss can also be used in the training.

Training weights (network weights) are updated (as indicated by reference numeral <NUM>) at least via the sinogram domain loss <NUM>. In certain embodiments, updating of the training weights occurs <NUM> occurs via a dual domain loss function utilizing both the sinogram domain loss <NUM> and the image reconstruction domain loss <NUM>. In certain embodiments, a single correction network utilizes the dual domain loss function. In other embodiments, separate correction networks may be utilized in a serial manner. For example, a first network (e.g., sinogram domain correction network) that corrects the sinogram having the bad pixel data based on loss defined in the native (raw) or transformed domain of the sinogram may be utilized. Then, a second network (e.g., image reconstruction domain correction network) that corrects for any perceived artifacts in the reconstructed tomographic image derived from the corrected sinogram to improve the final image based on the reconstruction domain loss may be utilized.

As mentioned above, the multi-channel sinogram correction network <NUM> learns to predict the missing pixel value(s) from complementary information available in the sinogram with the missing pixel value(s). <FIG> is a schematic diagram depicting the multi-channel sinogram correction network <NUM> that is trained and eventually utilized to correct for predicting missing pixel values in sinograms that lack data due to bad pixels. As depicted, the network <NUM> may include multiple networks (e.g., feature networks <NUM>, <NUM>, <NUM>; fusion network <NUM>; blending network <NUM>). Complementary information is fed in the form of multi-channel input data (e.g., multi-channel 2D or 3D patches) to the network <NUM>. In certain embodiments, some of the networks may be 2D-CNN networks.

An input patch <NUM> is derived from the sinogram missing the pixel value(s) for at least one bad pixel. The input patch <NUM> is a local neighborhood patch from the bad sinogram within the vicinity of the bad pixel (e.g., corresponding to a portion along a channel view direction of a row having the bad pixel). The local neighborhood path exploits the spatial correlation within the neighboring channels and views. The input patch <NUM> is inputted into the feature network <NUM>.

A neighbor patch <NUM> (e.g., neighboring row patch) is derived from the portion of the bad sinogram corresponding to an adjacent row to the row having the bad pixel. The neighbor row patch exploits the neighboring sensor correlation in the z-direction. The neighbor patch <NUM> is inputted into the feature network <NUM>.

A conjugate patch <NUM> is derived from a conjugate region of the bad sinogram that is relative the bad pixel. For example, a conjugate region of the sinogram may contain data acquired at a pixel location <NUM> degrees away from the bad pixel location along the CT detector. The conjugate patch <NUM> exploits the complementary information available due to the CT geometry. The conjugate patch <NUM> is inputted into the feature network <NUM>.

The data outputted from feature network <NUM> and feature network <NUM> are combined in the fusion network <NUM> (e.g., via data concatenation). It should be noted that the feature networks <NUM>, <NUM> and fusion network <NUM> may utilize deep residual learning in learning to estimate a pixel value for the missing pixel value in the bad sinogram due to the bad pixel. The output of the fusion network <NUM> along with the outputs from the feature networks <NUM>, <NUM> are utilized in the blending network <NUM> to generate an output patch <NUM> for the corrected sinogram. In certain embodiments, the blending network <NUM> may generate the output patch via mask addition (e.g., generating a mask that includes the pixel value that is applied to the patch missing the pixel value).

Besides the complementary information provided via the patches <NUM>, <NUM>, <NUM>, other inputs that provide complementary information for the training of the multi-channel sinogram correction network <NUM>. For example, in the case of a dual energy CT scan, the second energy scan be used as an input channel. Also, alternative patches may be utilized that include a different definition of similarity that is based on a user defined neighborhood.

As mentioned above, training the neural network involves determining the training loss in the image reconstruction domain. <FIG> is a schematic diagram depicting reconstruction domain analysis for determining image reconstruction domain loss. A reconstructed image <NUM> derived from a good sinogram (i.e., not missing a pixel value due to a bad pixel) is utilized as a ground truth. A reconstructed image <NUM> is derived from a corrected sinogram (e.g., estimated sinogram with estimated or predicted pixel value for bad pixel) that was outputted from the sinogram correction network. Reconstructed image <NUM> derived from the uncorrected or bad sinogram (e.g., having the missing pixel value) has a ring artifact. The reconstructed image <NUM> have the ring artifact. The reconstructed image <NUM> is compared to the ground truth (reconstructed image <NUM>) to determine a reconstruction difference <NUM> (e.g., the image reconstruction domain training loss). In certain embodiments, patches of the images <NUM>, <NUM> may be compared to determine the image reconstruction domain training loss. In certain embodiments, the image domain may be transformed (e.g., filtered) into a different domain prior to performing reconstruction domain loss analysis. As mentioned above, this training loss <NUM> is utilized to update the training weights utilized in training the sinogram correction network as part of a dual domain or hybrid domain loss. In certain embodiments, a separate network (separate from the sinogram correction network) may be utilized (e.g., in a serial manner) to correct for any artifacts (e.g., ring artifacts) still present in a reconstructed image generated from a corrected sinogram. The separate network may learn utilizing the image reconstruction domain loss.

<FIG> is a schematic diagram depicting the utilization of a trained sinogram correction network. The trained multi-channel sinogram correction network or model <NUM> is configured to receive a bad sinogram <NUM> (i.e., a sinogram missing a pixel value due to a bad pixel) and output a corrected sinogram <NUM>. Pixel values are inferred (e.g., predicted or estimated) for any missing pixels values in the bad sinogram <NUM>. In particular, trained weights <NUM> are utilized by the network <NUM> in inferring the missing pixel value(s). The trained weights <NUM> may have been adjusted by at least the sinogram domain training loss as noted above. In certain embodiments, both the sinogram domain training loss and the image reconstruction domain loss were utilized in updating the trained weights. Multi-channel data (e.g., 2D or 3D patches) are inputted into the network. Correction solely occurs in the sinogram domain. The network <NUM> is solely dependent on the inputted patches in the sinogram domain. There is no need to go into the reconstruction domain. Thus, the transformation is independent of anatomy, display field of view or reconstruction field of view, and reconstruction parameters (including reconstruction kernel). Since the process is independent of reconstruction parameters, there is no need for tuning parameters. As noted above, the network <NUM> may work in the raw sinogram domain or a transformed (e.g., filtered) domain of the sinogram. With the inferencing occurring completely in the sinogram domain, it enables faster prediction.

In certain embodiments, as noted above, separate correction networks may be utilized in a serial manner. For example, a first network (e.g., sinogram domain correction network) that corrects the sinogram having the bad pixel data may be utilized followed by a second network (e.g., image reconstruction domain correction network) that corrects for any perceived artifacts (ring or streak artifacts) in the reconstructed tomographic image derived from the corrected sinogram to improve the final image.

The trained network <NUM> in <FIG> was trained with data from <NUM> exams which resulted in approximately <NUM>,<NUM> multi-channel training patches (neighbor, row, conjugate). For the ground truth, patches were taken from a good sinogram (with missing data from a bad pixel and a reconstructed image from the good sinogram. Of the <NUM> exams, <NUM> exams were utilized for training and <NUM> exams were utilized for validation. The training parameters includes <NUM> epochs, a learning rate of <NUM> x <NUM>-<NUM> with a LR decay function of <NUM> x <NUM>-<NUM> * <NUM>epoch/<NUM>, and a loss function of MAE + SSIM.

<FIG> depicts different types of sinograms and corresponding image reconstructions. Sinograms <NUM>, <NUM>, <NUM> represent a true sinogram (i.e., without any missing values due to a bad pixel), a bad sinogram missing a pixel value as indicated by line <NUM> due to a bad pixel, and a corrected sinogram outputted utilizing the deep learning-based sinogram correction model discussed above. As depicted in the sinogram <NUM>, the line <NUM> in sinogram <NUM> is no longer present as a pixel value has been estimated for the missing pixel value. Reconstructed images <NUM>, <NUM>, and <NUM> represent the corresponding images derived from sinograms <NUM>, <NUM>, and <NUM>. As depicted in the image <NUM>, ring and streak artifacts are present due to the missing data from the bad pixel. As depicted in the image <NUM>, the ring and streak artifacts are absent and the image <NUM> looks similar to image <NUM>.

Technical effects of the disclosed subject matter include providing a deep learning-based technique for correcting missing pixel value(s) due to one or more bad pixels in a CT detector. Multi-channel input data having complementary information in the sinogram is utilized in a trained network to predict the missing pixel value. In particular, predicting the missing pixel value occurs completely in the sinogram domain to provide a faster prediction. Correction occurs solely in the sinogram domain making the process independent of anatomy, display field of view, and reconstruction parameters (including reconstruction kernel). By correcting for bad pixels, the deep-learning based technique provides quality reconstructed images lacking ring and streak artifacts due to bad detector pixels that suitable for diagnostic purposes. The deep-learning based technique enables the relaxation in constraints during production of CT detectors and utilization of the CT detectors in the field. In particular, CT detectors with a bad pixel in a central region of the detector or multiple bad pixels in general on the detector may still be utilized. This may reduce service costs (e.g., associated with detector panel replacement) due to bad pixels.

Claim 1:
A computer-implemented method for correcting artifacts in computed tomography,CT, data from a CT system, comprising:
obtaining the CT data from a CT scan based on measuring a series of views or projections from many different angles around a patient or object;
combining projections acquired at different view angles into a sinogram;
inputting the sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel, wherein the sinogram is a combination of multiple projections acquired at different view angles;
processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram comprises deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information; and
outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.