Patent Publication Number: US-2023162412-A1

Title: Network determination of limited-angle reconstruction

Description:
BACKGROUND 
     Conventional medical images may be generated via transmission tomography imaging or emission tomography imaging. In transmission tomography imaging, the imaging source (e.g., an X-ray source) is external to the subject and the source radiation (e.g., X-rays) is transmitted through the subject to a detector. According to emission tomography imaging, the imaging source (e.g., a gamma ray-emitting radiopharmaceutical) is internal to the subject (e.g., due to injection or ingestion thereof) and the source radiation (e.g., gamma rays) is emitted from within the subject to a detector. In either case, absorption or scattering within the subject tissue attenuates the source radiation prior to reception of the source radiation by the detector. 
     In some applications an emission imaging system is unable to acquire a full set (e.g., a full rotation) of tomographic information in the time needed to adequately image biological/physiological processes of interest. Current practice in these applications is to perform planar imaging over a full rotation around the subject at a rate faster than sufficient data for each position can be acquired. Such images lack spatial and location information related to tracer uptake, and may therefore result in incorrect quantitative measures and/or artifacts. 
     Emission tomography imaging typically exhibits lower resolution, greater artifacts and a more-pronounced partial volume effect in comparison to transmission tomography imaging. Current techniques for improving tomographic reconstruction of emission data utilize supplemental data obtained using other imaging modalities (e.g., Computed Tomography (CT), Magnetic Resonance (MR)). The supplemental data may be obtained by segmenting the CT/MR image data to, for example, identify tissue locations and characteristics. This information may facilitate corrections for resolution, partial volume effect and attenuation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of a system deploying a trained artificial neural network to generate an image volume from an incomplete set of projection images according to some embodiments; 
         FIG.  2    is a block diagram of a system deploying a trained artificial neural network to generate an image volume from an incomplete set of projection images and a CT volume according to some embodiments; 
         FIG.  3    is a block diagram of a system to train an artificial neural network to generate an image volume from an incomplete set of projection images according to some embodiments; 
         FIG.  4    is a flow diagram of a process to train an artificial neural network to generate an image volume from an incomplete set of projection images according to some embodiments; 
         FIG.  5    is a block diagram of an artificial neural network training architecture according to some embodiments; 
         FIG.  6    is a block diagram of an artificial neural network training architecture according to some embodiments; 
         FIG.  7    is a block diagram of a system deploying a trained artificial neural network to generate acquisition parameters from a set of projection images according to some embodiments; 
         FIG.  8    is a block diagram of a system to train an artificial neural network to generate acquisition parameters according to some embodiments; 
         FIG.  9    is a flow diagram of a process to train an artificial neural network to generate acquisition parameters according to some embodiments; 
         FIG.  10    is a block diagram of a system deploying a trained artificial neural network to generate acquisition parameters from radiomics features according to some embodiments; 
         FIG.  11    is a block diagram of a system to train an artificial neural network to generate acquisition parameters from radiomics features according to some embodiments; 
         FIG.  12    is a flow diagram of a process to train an artificial neural network to generate acquisition parameters from radiomics features according to some embodiments; 
         FIG.  13    is a block diagram of a computing system to train an artificial neural network to generate an image volume from an incomplete set of projection images; and 
         FIG.  14    illustrates a dual transmission and emission imaging SPECT/CT system deploying a trained neural network according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following description is provided to enable any person in the art to make and use the described embodiments and sets forth the best mode contemplated for carrying out the described embodiments. Various modifications, however, will remain apparent to those in the art. 
     Some embodiments provide generation of an image volume from an incomplete set of projection images. For example, embodiments may generate a high-resolution reconstructed volume based on a set of PET images taken from a limited set of projections angles. Generation of the volume may be informed by a CT scan acquired contemporaneously with the projection images. 
       FIG.  1    is a block diagram of a deployed system according to some embodiments. System  100  includes trained network  110 . Training of network  110  according to some embodiments will be described below. Although depicted as a neural network, network  110  may comprise any type of processing system to implement a function resulting from the below-described training. For example, network  110  may comprise a software application programmed to implement a function generated via prior neural network training. 
     In operation, projection images 1-j  are acquired via a first imaging modality. For example, projection images 1-j  may be acquired by a PET or SPECT scanner after injection of a radioactive tracer into a subject volume (e.g., a patient or a phantom). Projection images 1-j  may be acquired at several different projection angles as is known in the art. 
     According to some embodiments, projection images 1-j  are “incomplete”, in that the projection angles represented by the projection images are insufficient to generate a satisfactory reconstructed image. For example, the projection angles at which projection images 1-j  are acquired may define an arc of less than 180 degrees. 
     Trained network  110  outputs a quantitative reconstructed volume based on the input images. According to some embodiments, trained artificial neural network  110  implements a function. The function may be characterized as a set of trained parameter values associated with various layers of network nodes. The function may be deployed as is known in the art to any computing device. 
     According to some embodiments, network  110  receives a three-dimensional volume reconstructed from incomplete projection images 1-j  and generates reconstructed volume therefrom. In such embodiments, and as will be described below, network  110  is trained using three-dimensional volumes reconstructed from incomplete projection images 1-j . 
       FIG.  2    illustrates deployed system  200  according to some embodiments. Incomplete set of projection images 1-j  are input to trained network  210  as described above with respect to system  100 . Also input to network  210  is a CT volume representing a same subject as imaged by projection images 1-j . The CT volume may be acquired contemporaneously with projection images 1-j  so as to reduce registration errors therebetween. 
     Trained network  210  generates a reconstructed volume based on the incomplete set of projection images 1-j  and the CT volume. The CT volume may improve the quality of the output reconstructed volume by providing attenuation information which is not present in the deployment illustrated in  FIG.  1   . Other structural imaging modalities may be substituted for CT to provide attenuation information, such as but not limited to MR and PET. 
       FIG.  3    illustrates architecture  300  for training network  110  to generate a volume based on an incomplete set of projection images according to some embodiments. Network  110  may be trained temporally and/or geographically distant from the deployments depicted in  FIGS.  1  and  2   . For example, architecture  300  may be implemented at a data processing facility while systems  100  or  200  may execute within an imaging theater in which a patient has just been imaged. 
     Training system  310  uses Q sets of projection images 1-k  and, in some embodiments, CT volumes 1-Q  to train artificial neural network  110 . Q sets of projection images 1-k  and CT volumes 1-Q  are also used by ground truth determination unit  320  to generate ground truth data for evaluating the performance of network  110  during training by training system  310 . For example, for a set X of Q sets of projection images 1-k , training system  310  generates a subset  1 -j and inputs the subset (and, in some embodiments, a CT volume corresponding to set X) to network  110  to generate a volume based thereon. The volume is then compared with a ground truth volume generated by unit  320  based on the full set X of projection images 1-k  (and on CT volume x ) using quantitative and iterative reconstruction methods. The process repeats for other subsets  1 -j of set X and also repeats for each other of the Q sets of projection images 1-k . Network  110  is modified based on the comparisons and the entire process repeats until satisfactory network performance is achieved. 
     Artificial neural network  110  may comprise any type of network which is trainable to approximate a function. In some embodiments, network  110  comprises an implementation of a “u-net” convolutional network architecture as is known in the art. 
     Generally, artificial neural network  110  may comprise a network of neurons which receive input, change internal state according to that input, and produce output depending on the input and internal state. The output of certain neurons is connected to the input of other neurons to form a directed and weighted graph. The weights as well as the functions that compute the internal state can be modified by a training process based on ground truth data. Artificial neural network  110  may comprise any one or more types of artificial neural network that are or become known, including but not limited to convolutional neural networks, recurrent neural networks, long short-term memory networks, deep reservoir computing and deep echo state networks, deep belief networks, and deep stacking networks. 
     According to some embodiments, trained artificial neural network  110  implements a function of its inputs. The function may be characterized as a set of parameter values associated with each network node. The function may be deployed as is known in the art to an external system such as system  100  of  FIG.  1   . In one example, the training generates parameter values for kernels of a fully convolutional network. Another fully convolutional network comprising thusly-parameterized kernels may be efficiently incorporated within a system such as system  100  to generate a high-resolution volume as described herein. 
     Training system  310  may comprise any system or systems for training an artificial neural network that are or become known. For example, training system  310  may employ supervised learning, unsupervised learning and/or reinforcement learning. 
     The Q sets of projection images 1-k  and corresponding CT volumes 1-Q  may represent many different patients, phantoms, or other subjects. Moreover, each of the Q sets of projection images 1-k  and corresponding CT volumes 1-Q  may be acquired at different locations using different contrast settings. Generally, trained network  110  may be used to generate a volume based on input data of any modalities so long as those modalities were well-represented in the training data sets. 
       FIG.  4    is a flow diagram of a network training process according to some embodiments. Process  400  and the other processes described herein may be performed using any suitable combination of hardware and software. Software program code embodying these processes may be stored by any non-transitory tangible medium, including but not limited to a fixed disk, a volatile or non-volatile random access memory, a DVD, a Flash drive, or a magnetic tape. Embodiments are not limited to the examples described below. 
     Initially, at S 410 , a plurality of sets of two-dimensional projection data are acquired. The projection images may be acquired via a nuclear imaging scan and/or any other imaging modality that is or becomes known. Optionally, at S 420 , a three-dimensional CT volume associated with each set of two-dimensional projection data is acquired. According to some embodiments, each CT volume was acquired substantially contemporaneously with its associated set of projection data as is known in the art. S 410  and S 420  may simply comprise accessing a large repository of previously-acquired imaging data. 
     An artificial neural network is trained at S 430  based on the data acquired at S 410  and S 420 . The artificial neural network is trained to generate a reconstructed three-dimensional volume, based on the plurality of sets of two-dimensional projection data and, optionally, on respective ones of the three-dimensional CT volumes. In some embodiments, training of the network involves determining a loss based on the output of the network and iteratively modifying the network based on the loss until the loss reaches an acceptable level or training otherwise terminates (e.g., due to time constraints or to the loss asymptotically approaching a lower bound). Training of the network at S 430  may occur well after and separate from acquisition of the training data. For example, the training data may be acquired and accumulated in an image repository over several months or years prior to execution of S 430 . 
       FIG.  5    illustrates training, by training architecture  500 , at  5430  according to some embodiments. During training, reconstruction component  520  generates a ground-truth volume for each of Q sets of projection images 1-k  acquired at S 410 . Reconstruction component  520  may apply conjugate gradient, attenuation and scatter (CGAS) reconstruction, filtered back-projection (FBP) reconstruction or any other suitable technique to projection images 1-4 . Subsets of each of the Q sets are input to network  510  and, in response, network  510  outputs a volume corresponding to each subset. According to some embodiments, a three-dimensional volume may be reconstructed from each subset and input to network  510  instead of or in addition to the input of each subset. 
     Loss layer component  530  determines a loss by comparing each output volume to a corresponding ground truth volume. More specifically, an output volume based upon a particular subset of a set of projection images is compared against a volume reconstructed by component  520  based on the same set of projection images. Any number of subsets of a particular set of projection images may be used during training at S 430 . 
     The total loss is back-propagated from loss layer component  530  to network  510 . The loss may comprise an L1 loss, and L2 loss, or any other suitable measure of total loss. An L1 loss is the sum of the absolute differences between each output volume and its corresponding ground truth volume, and an L2 loss is the sum of the squared differences between each output volume and its corresponding ground truth volume. 
     Network  510  changes its internal weights, or kernel parameter values, based on the back-propagated loss as is known in the art. The training data is again processed by network  510  and loss layer  530  as described above, and the process repeats, until it is determined that the loss has reached an acceptable level or training otherwise terminates. At termination, network  510  may be considered trained. In some embodiments, the function implemented by now-trained network  510  (e.g., embodied in parameter values of trained convolutional kernels) may then be deployed as shown in  FIG.  1   . 
       FIG.  6    illustrates training at S 430  according to some embodiments. Training architecture  600  may be used to train network  610  for deployment in an architecture such as architecture  200  of  FIG.  2   . Similar to training architecture  500 , reconstruction component  620  generates a ground-truth volume for each of Q sets of projection images 1-k  acquired at S 410 . However, for each of the Q sets of projection images 1-k , component  620  also uses a corresponding CT volume q to generate the associated ground truth volume. Structural image volumes other than CT may be employed, including but not limited to MR and PET. According to some embodiments, component  620  generates each ground truth volume q by segmenting and registering a corresponding CT volume q as is known in the art, and executing multi-modal reconstruction based thereon and on a corresponding set of projection images 1-k . 
     During training, subsets of each of the Q sets are input to network  610 , which outputs a volume corresponding to each subset. Any number of subsets of a particular set of projection images, including any number of projection images, may be used during. Again, a three-dimensional volume may be reconstructed from each subset and input to network  610  instead of or in addition to the input of each subset. 
     Loss layer component  630  may determine a loss by comparing each output volume to a corresponding ground truth volume as described above, and network  610  is modified until it is determined that the loss has reached an acceptable level or training otherwise terminates. The function implemented by now-trained network  160  may then be deployed, for example, as shown in  FIG.  2   . 
     Reconstruction component  420 , segmentation/reconstruction component  620 , and each functional component described herein may be implemented in computer hardware, in program code and/or in one or more computing systems executing such program code as is known in the art. Such a computing system may include one or more processing units which execute processor-executable program code stored in a memory system. Moreover, networks  510  and  610  may comprise hardware and software specifically-intended for executing algorithms based on a specified network architecture and trained kernel parameters. 
       FIG.  7    is a block diagram of system  700  deploying a trained artificial neural network  710  to generate image acquisition parameters  720  from a set of projection images according to some embodiments. Training of network  710  according to some embodiments will be described below. Although depicted as a classification-type neural network, network  710  may comprise any type of processing system to implement a learned function and output one or more image acquisition parameters (e.g., a probability associated with each of one or more acquisition parameters). For example, network  710  may comprise a software application programmed to implement a function generated via prior neural network training. 
     In operation, a set of projection images 1-k  are acquired via a suitable imaging modality. For example, projection images 1-k  may be acquired by a PET or SPECT scanner after injection of a radioactive tracer into a subject volume. Projection images 1-k  may comprise CT images as is known in the art. 
     The set of projection images 1-k  is used by reconstruction component  730  to reconstruct a volume as is known in the art. The reconstruction technique applied by reconstruction component  730  may depend upon the type of modality used to acquire the set of projection images 1-k . Embodiments of reconstruction component  730  may employ any suitable reconstruction algorithm. 
     Trained network  710  receives the reconstructed volume and outputs an indication of acquisition parameters  720 . Acquisition parameters  720  may comprise acquisition parameters which address deficiencies in projection images 1-k  and therefore result in a higher-quality reconstructed volume. Therefore, in some embodiments, trained network  710  models correlations between undesirable characteristics of a reconstructed volume and parameters for acquiring projection images which may be used to reconstruct a volume that exhibits a reduction in the undesirable characteristics. 
     Accordingly, in some examples, a first set of projection images 1-k  is acquired and reconstruction component  730  reconstructs a first volume therefrom. The first volume is input to network  710  and network  710  outputs acquisition parameters  720 . A second set of projection images is acquired based on the output acquisition parameters  720  and a second volume is reconstructed from the second set of projection images. The second volume exhibits improved characteristics with respect to the first volume. 
     The characteristics which are improved depend upon the data used to train network  710 . Moreover, “improvement” of the characteristics is relative to the desired usage of the resulting reconstructed volumes. For example, a high level of a particular image characteristic may be desirable for one type of diagnostic review, while a low level of the particular image characteristic may be desirable for treatment planning. In the former case, “improving” the particular image characteristic consists of increasing the level of the image characteristic and, in the latter case, the particular image characteristic is improved by decreasing the level of the image characteristic. 
       FIG.  8    is a block diagram of system  800  to train artificial neural network  710  to generate acquisition parameters according to some embodiments. Training system  810  uses reconstructed volumes 1-Q  and remedial acquisition parameters 1-Q  to train artificial neural network  710 . Each of reconstructed volumes 1-Q  is associated with a respective one of remedial acquisition parameters 1-Q . 
     In some embodiments, the remedial acquisition parameters which are associated with a given training volume are parameters for projection image acquisition which may address deficiencies in the given training volume. These parameters may be defined by a human upon review of the given training volume. The remedial acquisition parameters may include any parameters related to the imaging modality used to acquire projection images that are or become known. For example, in the case of SPECT imaging, remedial acquisition parameters  820  may include the duration of each projection, the number of frames per projection, the number of projections per scan, and the size of acquisition matrix. In the case of CT imaging, remedial acquisition parameters  820  may include X-ray beam energy, X-ray tube current, integration time, frames per projection, and acquisition time. 
     During training of system  710 , reconstructed volumes 1-Q  are input to training system  810 , which outputs a set of acquisition parameters for each of reconstructed volumes 1-Q . As mentioned above, an output set of acquisition parameters may comprise a set of probabilities for each of several possible acquisition parameters. Training system  810  compares each output set of acquisition parameters with a corresponding set of remedial acquisition parameters stored among parameters  820 . A total loss is computed and network  710  is modified based on the loss. The training process repeats until satisfactory network performance is achieved. 
       FIG.  9    is a flow diagram of process  900  to train an artificial neural network to generate acquisition parameters according to some embodiments. At S 910 , a plurality of reconstructed volumes of imaging data are acquired. The volumes may be reconstructed from projection images as is known in the art. S 910  may comprise accessing a repository of reconstructed three-dimensional image data. 
     Remedial acquisition parameters associated with each of the reconstructed volumes are determined at S 920 . Remedial acquisition parameters which are associated with a given volume are projection image acquisition parameters which may address deficiencies in the given volume if used to acquire projection images for subsequent reconstruction. A radiologist may review each reconstructed volume in order to determine remedial acquisition parameters associated with the reconstructed volumes. 
     An artificial neural network is trained at S 930  based on the acquired volumes and determined remedial acquisition parameters. In some embodiments, training of the network involves determining a loss based on the output of the network and iteratively modifying the network based on the loss until the loss reaches an acceptable level or training otherwise terminates (e.g., due to time constraints or to the loss asymptotically approaching a lower bound). Training of the network at S 930  may occur well after and separate from acquisition of the training data at S 910  and S 920 . 
       FIG.  10    is a block diagram of system  1000  deploying trained artificial neural network  1010  to generate acquisition parameters  1020  from radiomics features  1030  according to some embodiments. Network  1010  may comprise any type of processing system to implement a learned function and output image acquisition parameters  1020  (e.g., a probability associated with each of one or more acquisition parameters). 
     Radiomics refers to the extraction of features from radiographic medical images. The extraction is based on programmed and/or learned algorithms, and the features may provide insight to diagnosis, prognosis and therapeutic response which might not be appreciated by the naked eye. 
     Radiomics features  1030  of system  1000  may be acquired in any manner that is or becomes known. According to some embodiments, radiomic features  1030  may include size and shape-based features, descriptors of an image intensity histogram, descriptors of the relationships between image voxels (e.g., gray-level co-occurrence matrix (GLCM), run length matrix (RLM), size zone matrix (SZM), and neighborhood gray tone difference matrix (NGTDM)) derived textures, textures extracted from filtered images, and fractal features. 
     In operation, radiomics features  1030  are acquired based on one or more images of a subject. In some embodiments, a set of projection images is acquired and a volume is reconstructed therefrom, and radiomics features  1030  are extracted from the volume. Trained network  1010  receives the radiomics features and outputs acquisition parameters  1020 . The output acquisition parameters  1020  may comprise acquisition parameters which address deficiencies in the image(s) from which radiomics features  1030  were extracted. 
     Therefore, in some embodiments, a first set of projection images is acquired and a volume is reconstructed therefrom. The volume is input to network  1010  and network  1010  outputs acquisition parameters  1020 . A second set of projection images is then acquired based on the output acquisition parameters  1020  and a second volume is reconstructed from the second set of projection images. Due to the use of the output acquisition parameters  1020  to acquire the second set of projection images, the second volume exhibits improved characteristics with respect to the first volume. As mentioned above, the characteristics which are improved and the manner in which the characteristics are improved depend upon the data used to train network  1010 . 
       FIG.  11    is a block diagram of system  1100  to train artificial neural network  1010  to generate acquisition parameters according to some embodiments. Training system  1110  uses sets of radiomics features 1-Q    1130  and remedial acquisition parameters 1-Q    1140  to train artificial neural network  1010 . Each set of radiomics features 1-Q    1130  is extracted from a respective one of reconstructed volumes 1-3 Q  by radiomic feature extraction component  1120  as is or becomes known. Each set of radiomics features 1-Q    1130  is associated with a respective one of remedial acquisition parameters 1-Q    1140  which corresponds to the reconstructed volume from which the set of radiomics features was extracted. In other words, the remedial acquisition parameters which are associated with a given set of radiomics features are parameters for projection image acquisition which may address deficiencies in an image volume from which the given set of radiomics features was extracted. Remedial acquisition parameters 1-Q    1140  may comprise any parameters described herein or otherwise known. 
       FIG.  12    is a flow diagram of process  1200  to train an artificial neural network to generate acquisition parameters according to some embodiments. Process  1200  will be described below with respect to system  1100 , but embodiments are not limited thereto. At S 1210 , a plurality of reconstructed volumes of imaging data are acquired. The volumes may be reconstructed from projection images as is known in the art. In some embodiments, S 1210  comprises accessing a repository of reconstructed three-dimensional image data. 
     Next, at S 1220 , multi-dimensional radiomics features are determined for each of the plurality of reconstructed volumes of image data. For example, radiomic feature extraction component  1120  may extract radiomics features 1-Q    1130  from respective ones of reconstructed volumes 1-3 Q  at S 1220  as is or becomes known. 
     Remedial acquisition parameters associated with each of the reconstructed volumes are determined at S 1230 . The remedial acquisition parameters may be determined via human review. For example, a radiologist may review each reconstructed volume in order to determine acquisition parameters which may remedy deficiencies in the volumes if used to acquire projection images for subsequent reconstruction. 
     An artificial neural network is trained at S 1240  based on the multi-dimensional radiomics features and determined remedial acquisition parameters. In some embodiments, training of the network involves inputting the multi-dimensional radiomics features to training system  1110 , which outputs a set of acquisition parameters for each set of radiomics features 1-Q . An output set of acquisition parameters may comprise a set of probabilities for each of several possible acquisition parameters. Training system  1110  compares each output set of acquisition parameters with a corresponding set of remedial acquisition parameters stored among parameters  1140 . A total loss is computed and network  1010  is modified based on the loss. The training process repeats until satisfactory network performance is achieved. 
       FIG.  13    is a block diagram of a computing system to train an artificial neural network to generate an image volume from an incomplete set of projection images according to some embodiments. System  1300  may comprise a computing system to facilitate the design and training of an artificial neural network as is known in the art. Computing system  1300  may comprise a standalone system, or one or more elements of computing system  1300  may be located in the cloud. 
     System  1300  includes communication interface  1310  to communicate with external devices via, e.g., a network connection. Processing unit(s)  1320  may comprise one or more processors, processor cores, or other processing units to execute processor-executable process steps. In this regard, storage system  1330 , which may comprise one or more memory devices (e.g., a hard disk drive, a solid-state drive), stores processor-executable process steps of training program  1331  which may be executed by processing unit(s)  1330  to train a network as described herein. 
     Training program  1331  may utilize node operator libraries  1332 , which includes code to execute various operations associated with node operations. According to some embodiments, computing system  1300  provides interfaces and development software (not shown) to enable development of training program  1331  and generation of network definition  1335  which specifies the architecture of the neural network to be trained. Storage device  1330  may also include program code  1333  of reconstruction component  520  and/or segmentation/reconstruction component  620 . 
     Data used for training the network may also be stored in storage device  1330 , including but not limited to projection data  1334  as described with respect to  FIG.  5   . Once trained, the parameters of the neural network may be stored as trained network parameters  1336 . As mentioned above, these trained parameters may be deployed in other systems as is known in the art to provide the trained functionality. 
       FIG.  14    illustrates SPECT-CT system  1400  which may deploy a trained network to generate high-resolution volumes based on CT data and lower-resolution nuclear imaging data as described herein. 
     System  1400  includes gantry  1402  to which two or more gamma cameras  1404   a ,  1404   b  are attached, although any number of gamma cameras can be used. A detector within each gamma camera detects gamma photons (i.e., emission data)  1403  emitted by a radioisotope within the body of a patient  1406  lying on a bed  1408 . 
     Bed  1408  is slidable along axis-of-motion A. At respective bed positions (i.e., imaging positions), a portion of the body of patient  1406  is positioned between gamma cameras  1404   a,    1404   b  in order to capture emission data  1403  from that body portion. Gamma cameras  1404   a,    1404   b  may include multi-focal cone-beam collimators or parallel-hole collimators as is known in the art. 
     System  1400  also includes CT housing  1410  including an X-ray imaging system (unshown) as is known in the art. Generally, and according to some embodiments, the X-ray imaging system acquires two-dimensional X-ray images of patient  1406  before, during and/or after acquisition of emission data using gamma cameras  1404   a  and  1404   b.    
     Control system  1420  may comprise any general-purpose or dedicated computing system. Accordingly, control system  1420  includes one or more processing units  1422  configured to execute processor-executable program code to cause system  1420  to operate as described herein, and storage device  1430  for storing the program code. Storage device  1430  may comprise one or more fixed disks, solid-state random access memory, and/or removable media (e.g., a thumb drive) mounted in a corresponding interface (e.g., a USB port). 
     Storage device  1430  stores program code of system control program  1431 . One or more processing units  1422  may execute system control program  1431  to, in conjunction with SPECT system interface  1440 , control motors, servos, and encoders to cause gamma cameras  1404   a,    1404   b  to rotate along gantry  1402  and to acquire two-dimensional emission data  1403  at defined imaging positions during the rotation. The acquired data  1432  may comprise projection images as described herein and may be stored in memory  1430 . Reconstructed volumes  1434  as described herein may be stored in memory  1430 . 
     One or more processing units  1422  may also execute system control program  1431  to, in conjunction with CT system interface  1445 , cause a radiation source within CT housing  1410  to emit radiation toward body  1406  from different projection angles, to control a corresponding detector to acquire two-dimensional CT images, and to reconstruct three-dimensional CT images from the acquired images. The CT images may be acquired substantially contemporaneously with the emission data as described above, and volumes reconstructed therefrom reconstructed images may be stored as CT data  1433 . 
     Trained network parameters  1435  may comprise parameters of a neural network trained as described herein. For example, projection images of emission data  1432  and, optionally, a corresponding CT volume, may be input to a network implementing trained network parameters  1435  to generate remedial acquisition parameters as described above. 
     Terminal  1450  may comprise a display device and an input device coupled to system  1420 . Terminal  1450  may display any of projection images, reconstructed volumes, and remedial acquisition parameters, and may receive user input for controlling display of the data, operation of imaging system  1400 , and/or the processing described herein. In some embodiments, terminal  1450  is a separate computing device such as, but not limited to, a desktop computer, a laptop computer, a tablet computer, and a smartphone. 
     Each of component of system  1400  may include other elements which are necessary for the operation thereof, as well as additional elements for providing functions other than those described herein. 
     Those in the art will appreciate that various adaptations and modifications of the above-described embodiments can be configured without departing from the claims. Therefore, it is to be understood that the claims may be practiced other than as specifically described herein.