Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ( μ -maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free μ -map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived μ -maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating μ -maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Characterizing left ventricular deformation and strain using 3D+time echocardiography provides useful insights into cardiac function and can be used to detect and localize myocardial injury. To achieve this, it is imperative to obtain accurate motion estimates of the left ventricle. In many strain analysis pipelines, this step is often accompanied by a separate segmentation step; however, recent works have shown both tasks to be highly related and can be complementary when optimized jointly. In this work, we present a multi-task learning network that can simultaneously segment the left ventricle and track its motion between multiple time frames. Two task-specific networks are trained using a composite loss function. Cross-stitch units combine the activations of these networks by learning shared representations between the tasks at different levels. We also propose a novel shape-consistency unit that encourages motion propagated segmentations to match directly predicted segmentations. Using a combined synthetic and in-vivo 3D echocardiography dataset, we demonstrate that our proposed model can achieve excellent estimates of left ventricular motion displacement and myocardial segmentation. Additionally, we observe strong correlation of our image-based strain measurements with crystal-based strain measurements as well as good correspondence with SPECT perfusion mappings. Finally, we demonstrate the clinical utility of the segmentation masks in estimating ejection fraction and sphericity indices that correspond well with benchmark measurements.

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Attenuation maps (μ-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve the diagnostic accuracy of cardiac SPECT. However, in clinical practice, SPECT and CT scans are acquired sequentially, potentially inducing misregistration between the two images and further producing AC artifacts. Conventional intensity-based registration methods show poor performance in the cross-modality registration of SPECT and CT-derived μ-maps since the two imaging modalities might present totally different intensity patterns. Deep learning has shown great potential in medical imaging registration. However, existing deep learning strategies for medical image registration encoded the input images by simply concatenating the feature maps of different convolutional layers, which might not fully extract or fuse the input information. In addition, deep-learning-based cross-modality registration of cardiac SPECT and CT-derived μ-maps has not been investigated before. In this paper, we propose a novel Dual-Channel Squeeze-Fusion-Excitation (DuSFE) co-attention module for the cross-modality rigid registration of cardiac SPECT and CT-derived μ-maps. DuSFE is designed based on the co-attention mechanism of two cross-connected input data streams. The channel-wise or spatial features of SPECT and μ-maps are jointly encoded, fused, and recalibrated in the DuSFE module. DuSFE can be flexibly embedded at multiple convolutional layers to enable gradual feature fusion in different spatial dimensions. Our studies using clinical patient MPI studies demonstrated that the DuSFE-embedded neural network generated significantly lower registration errors and more accurate AC SPECT images than existing methods. We also showed that the DuSFE-embedded network did not over-correct or degrade the registration performance of motion-free cases. The source code of this work is available at https://github.com/XiongchaoChen/DuSFE_CrossRegistration.

In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-registration/mis-segmentation would result in severe degradation in image quality after PVC. In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation. The proposed network involves a densely-connected multi-dimensional dynamic mechanism, allowing the convolutional kernels to be adapted based on the input images, even after the network is fully trained. Intramyocardial blood volume (IMBV) is introduced as an additional clinical-relevant loss function for network optimization. The proposed network demonstrated promising performance on 28 canine studies acquired on a GE Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using Technetium-99m-labeled red blood cells. This work showed that the proposed network with densely-connected dynamic mechanism produced superior results compared with the same network without such mechanism. Results also showed that the proposed network without anatomical information could produce images with statistically comparable IMBV measurements to the images generated by anatomical-guided PVC methods, which could be helpful in clinical translation.

Reliable motion estimation and strain analysis using 3D+ time echocardiography (4DE) for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. However, motion estimation is difficult due to the low-SNR that stems from the inherent image properties of 4DE, and intelligent regularization is critical for producing reliable motion estimates. In this work, we incorporated the notion of domain adaptation into a supervised neural network regularization framework. We first propose a semi-supervised Multi-Layered Perceptron (MLP) network with biomechanical constraints for learning a latent representation that is shown to have more physiologically plausible displacements. We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation. We validated the semi-supervised regularization method on in vivo data with implanted sonomicrometers. Finally, we showed the ability of our semi-supervised learning regularization approach to identify infarct regions using estimated regional strain maps with good agreement to manually traced infarct regions from postmortem excised hearts.

Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal information in the 4D dynamic image data for motion detection. The network was trained and evaluated over motion-free patient scans with simulated motion so that the motion ground-truths are available, where one million samples based on 65 patient scans were used in training, and 600 samples based on 20 patient scans were used in evaluation. The proposed method was also evaluated using additional 10 patient datasets with real motion. We demonstrated that the proposed DeepMC obtained superior performance compared to conventional registration-based methods and other convolutional neural networks (CNN), in terms of motion estimation and MBF quantification accuracy. Once trained, DeepMC is much faster than the registration-based methods and can be easily integrated into the clinical workflow. In the future work, additional investigation is needed to evaluate this approach in a clinical context with realistic patient motion.

Tl) due to its typically low injected dose. The conventional indirect method for generating parametric images typically starts by reconstructing a time series of frame images followed by fitting the time-activity curve (TAC) for each voxel or segment with an appropriate kinetic model. The indirect method is simple and easy to implement; however, it usually suffers from substantial image noise that could also lead to bias. In this paper, we developed a list mode direct parametric image reconstruction algorithm to substantially reduce noise in MBF quantification using dynamic SPECT and allow for patient radiation dose reduction. GPU-based parallel computing was used to achieve more than 2000-fold acceleration. The proposed method was evaluated in both simulation and in vivo canine studies. Compared with the indirect method, the proposed direct method achieved substantially lower image noise and variability, particularly at large number of iterations and at low-count levels.

In the above-named document [ibid, vol. 33, no. 6, pp. 1275–1289, Jun. 2014], the funding information should have appeared as follows: "This work was supported in part by the National Institutes of Health (The National Heart, Lung, and Blood Institute) through these awards: R01HL082640, 5T32HL098069, and R01HL121226. The work of B. A. Lin was supported by an ASE Foundation Career Development Award."

Quantitative analysis of left ventricular deformation can provide valuable information about the extent of disease as well as the efficacy of treatment. In this work, we develop an adaptive multi-level compactly supported radial basis approach for deformation analysis in 3D+time echocardiography. Our method combines displacement information from shape tracking of myocardial boundaries (derived from B-mode data) with mid-wall displacements from radio-frequency-based ultrasound speckle tracking. We evaluate our methods on open-chest canines (N=8) and show that our combined approach is better correlated to magnetic resonance tagging-derived strains than either individual method. We also are able to identify regions of myocardial infarction (confirmed by postmortem analysis) using radial strain values obtained with our approach.

Statistical models have shown considerable promise as a basis for segmenting and interpreting cardiac images. While a variety of statistical models have been proposed to improve the segmentation results, most of them are either static models (SMs), which neglect the temporal dynamics of a cardiac sequence, or generic dynamical models (GDMs), which are homogeneous in time and neglect the intersubject variability in cardiac shape and deformation. In this paper, we develop a subject-specific dynamical model (SSDM) that simultaneously handles temporal dynamics (intrasubject variability) and intersubject variability. We also propose a dynamic prediction algorithm that can progressively identify the specific motion patterns of a new cardiac sequence based on the shapes observed in past frames. The incorporation of this SSDM into the segmentation framework is formulated in a recursive Bayesian framework. It starts with a manual segmentation of the first frame, and then segments each frame according to intensity information from the current frame as well as the prediction from past frames. In addition, to reduce error propagation in sequential segmentation, we take into account the periodic nature of cardiac motion and perform segmentation in both forward and backward directions. We perform "leave-one-out" test on 32 canine sequences and 22 human sequences, and compare the experimental results with those from SM, GDM, and active appearance motion model (AAMM). Quantitative analysis of the experimental results shows that SSDM outperforms SM, GDM, and AAMM by having better global and local consistencies with manual segmentation. Moreover, we compare the segmentation results from forward and forward-backward segmentation. Quantitative evaluation shows that forward-backward segmentation suppresses the propagation of segmentation errors.

Statistical model-based segmentation of the left ventricle from cardiac images has received considerable attention in recent years. While a variety of statistical models have been shown to improve segmentation results, most of them are either static models (SM) which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM) which neglect the inter-subject variability of cardiac shapes and deformations. In this paper, we use a subject-specific dynamical model (SSDM) that handles inter-subject variability and temporal dynamics (intra-subject variability) simultaneously. It can progressively identify the specific motion patterns of a new cardiac sequence based on the segmentations observed in the past frames. We formulate the integration of the SSDM into the segmentation process in a recursive Bayesian framework in order to segment each frame based on the intensity information from the current frame and the prediction from the past frames. We perform "Leave-one-out" test on 32 sequences to validate our approach. Quantitative analysis of experimental results shows that the segmentation with the SSDM outperforms those with the SM and GDM by having better global and local consistencies with the manual segmentation.

The effective visualization of vascular structures is critical for diagnosis, surgical planning as well as treatment evaluation. In recent work, we have developed an algorithm for vessel detection that examines the intensity profile around each voxel in an angiographic image and determines the likelihood that any given voxel belongs to a vessel; we term this the "vesselness coefficient" of the voxel. Our results show that our algorithm works particularly well for visualizing branch points in vessels. Compared to standard Hessian based techniques, which are fine-tuned to identify long cylindrical structures, our technique identifies branches and connections with other vessels. Using our computed vesselness coefficient, we explore a set of techniques for visualizing vasculature. Visualizing vessels is particularly challenging because not only is their position in space important for clinicians but it is also important to be able to resolve their spatial relationship. We applied visualization techniques that provide shape cues as well as depth cues to allow the viewer to differentiate between vessels that are closer from those that are farther. We use our computed vesselness coefficient to effectively visualize vasculature in both clinical neurovascular x-ray computed tomography based angiography images, as well as images from three different animal studies. We conducted a formal user evaluation of our visualization techniques with the help of radiologists, surgeons, and other expert users. Results indicate that experts preferred distance color blending and tone shading for conveying depth over standard visualization techniques.

This paper presents a new shape registration algorithm that establishes "meaningful correspondence " between objects, in that it preserves the local shape correspondence between the source and target objects. By observing that an object's skeleton corresponds to its local shape peaks, we use skeleton to characterize the local shape of the source and target objects. Unlike traditional graph-based skeleton matching algorithms that focus on matching skeletons alone and ignore the overall alignment of the boundaries, our algorithm is formulated in a variational framework which aligns local shape by registering two potential fields that are associated with skeletons. Also, we add a boundary constraint term to the energy functional, such that our algorithm can be applied to match bulky objects where skeleton and boundary are far away to each other. To increase the robustness of our algorithm, we incorporate M-estimator and dynamic pruning algorithm to form a feedback system that eliminates local shape outliers caused by nonrigid deformation, occlusion, and missing parts. Experiments on 2D binary shapes and 3D cardiac sequences validate the accuracy and robustness of this algorithm.

A sampled-data H∞filtering strategy is proposed for cardiac kinematics estimation from periodic medical image sequences. Stochastic multi-frame filtering frameworks are constructed to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data in a coordinated fashion. As robustness is of paramount importance in cardiac motion estimation, this mini-max H∞strategy is particularly powerful for real-world problems where the types and levels of model uncertainties and data disturbances are not available a priori. For the hybrid cardiac analysis system with continuous dynamics and discrete measurements, the state estimates are predicted according to the continuous-time state equation between observation time points, and updated with the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results for the continuously evolving cardiac dynamics. The strategy is validated through synthetic data experiments to illustrate its advantages and on canine MR phase contrast images to show its clinical relevance.

Proper spatial and temporal constraints are essential for image-based motion recovery of deforming objects. Since biological organs, such as the heart, are typically composed of fibrous tissues of anisotropic nature, one must adopt realistic spatial models, in addition to those important considerations for temporal modeling, in order to properly regularize the object behavior for kinematics recovery. We present a biomechanically constrained state space analysis framework for the multiframe estimation of the heart motion and deformation. While the anisotropic physical constraints enforce spatial regulations on the myocardial behavior and spatial filtering of the image data measurements, statistical filtering techniques impose temporal constraints to incorporate multiframe information. Implemented within a mesh-free particle representation and computation framework, excellent experimental results are achieved for both synthetic data with known ground truth and canine magnetic resonance image sequences with known clinical gold standard.

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.

We propose and validate the hypothesis that we can use differential shape properties of the myocardial surfaces to recover dense field motion from standard three-dimensional (3-D) image sequences (MRI and CT). Quantitative measures of left ventricular regional function can be further inferred from the point correspondence maps. The noninvasive, algorithm-derived results are validated on two levels. First, the motion trajectories are compared to those of implanted imaging-opaque markers of a canine model in two imaging modalities, where subpixel accuracy is achieved. Second, the validity of using motion parameters (path length and thickness changes) for detecting myocardial injury area is tested by comparing algorithms derived results to postmortem analysis TTC staining of myocardial tissue, where the achieved Pearson product-moment correlation value is 0.968.

We have addressed the problem of tracking the non-rigid motion of the heart using a sequence of velocity fields and a sequence of contours. The information from both the contours and the dense velocity fields is integrated into a deforming mesh that is placed over the myocardium at one time frame and then tracked over the entire cardiac cycle. The deformation is guided by a smoothing filter that provides a compromise between (i) believing the dense field velocity and the contour data when it is crisp and coherent in a local spatial and temporal sense and (ii) employing a temporally smooth cyclic model of cardiac motion when contour and velocity data are not trustworthy. The method has been carefully evaluated with simulated data and phantom data. Experiments with in vivo data have also been conducted.

The authors propose a new approach for tracking the deformation of the left-ventricular (LV) myocardium from two-dimensional (2-D) magnetic resonance (MR) phase contrast velocity fields. The use of phase contrast MR velocity data in cardiac motion problems has been introduced by others (N.J. Pelc et al., 1991) and shown to be potentially useful for tracking discrete tissue elements, and therefore, characterizing LV motion. However, the authors show here that these velocity data: 1) are extremely noisy near the LV borders; and 2) cannot alone be used to estimate the motion and the deformation of the entire myocardium due to noise in the velocity fields. In this new approach, the authors use the natural spatial constraints of the endocardial and epicardial contours, detected semiautomatically in each image frame, to help remove noisy velocity vectors at the LV contours. The information from both the boundaries and the phase contrast velocity data is then integrated into a deforming mesh that is placed over the myocardium at one time frame and then tracked over the entire cardiac cycle. The deformation is guided by a Kalman filter that provides a compromise between 1) believing the dense field velocity and the contour data when it is crisp and coherent in a local spatial and temporal sense and 2) employing a temporally smooth cyclic model of cardiac motion when contour and velocity data are not trustworthy. The Kalman filter is particularly well suited to this task as it produces an optimal estimate of the left ventricle's kinematics (in the sense that the error is statistically minimized) given incomplete and noise corrupted data, and given a basic dynamical model of the left ventricle. The method has been evaluated with simulated data; the average error between tracked nodes and theoretical position was 1.8% of the total path length. The algorithm has also been evaluated with phantom data; the average error was 4.4% of the total path length. The authors show that in their initial tests with phantoms that the new approach shows small, but concrete improvements over previous techniques that used primarily phase contrast velocity data alone. They feel that these improvements will be amplified greatly as they move to direct comparisons in in vivo and three-dimensional (3-D) datasets.

Accurate estimation of heart wall dense field motion and deformation could help to better understand the physiological processes associated with ischemic heart diseases, and to provide significant improvement in patient treatment. We present a new method of estimating left ventricular deformation which integrates instantaneous velocity information obtained within the mid-wall region with shape information found on the boundaries of the left ventricle. Velocity information is obtained from phase contrast magnetic resonance images, and boundary information is obtained from shape-based motion tracking of the endo- and cardial boundaries. The integration takes place within a continuum biomechanical heart model which is embedded in a finite element framework. We also employ a feedback mechanism to improve tracking accuracy. The integration of the two disparate but complementary sources overcomes some of the limitations of previous work in the field which concentrates on motion estimation from a single image-derived source.>

Cardiac SPECT (single photon emission computed tomography) is an important tool for evaluating heart disease in terms of diagnosis and treatment, especially for determining myocardial perfusion and thus the degree of myocardial injury. The distortion of left ventricular (LV) geometry can cause errors in defect size determination from SPECT perfusion images due to partial volume effects. Constrained iterative restoration, using an anatomic constraint from a registered magnetic resonance (MR) image of the heart, can be used to correct these errors and thus improve the interpretation and measurement of cardiac perfusion images.