STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging.

PURPOSE: This study aims to develop deep learning techniques on total-body PET to bolster the feasibility of sedation-free pediatric PET imaging. METHODS: A deformable 3D U-Net was developed based on 245 adult subjects with standard total-body PET imaging for the quality enhancement of simulated rapid imaging. The developed method was first tested on 16 children receiving total-body [18F]FDG PET scans with standard 300-s acquisition time with sedation. Sixteen rapid scans (acquisition time about 3 s, 6 s, 15 s, 30 s, and 75 s) were retrospectively simulated by selecting the reconstruction time window. In the end, the developed methodology was prospectively tested on five children without sedation to prove the routine feasibility. RESULTS: The approach significantly improved the subjective image quality and lesion conspicuity in abdominal and pelvic regions of the generated 6-s data. In the first test set, the proposed method enhanced the objective image quality metrics of 6-s data, such as PSNR (from 29.13 to 37.09, p < 0.01) and SSIM (from 0.906 to 0.921, p < 0.01). Furthermore, the errors of mean standardized uptake values (SUVmean) for lesions between 300-s data and 6-s data were reduced from 12.9 to 4.1% (p < 0.01), and the errors of max SUV (SUVmax) were reduced from 17.4 to 6.2% (p < 0.01). In the prospective test, radiologists reached a high degree of consistency on the clinical feasibility of the enhanced PET images. CONCLUSION: The proposed method can effectively enhance the image quality of total-body PET scanning with ultrafast acquisition time, leading to meeting clinical diagnostic requirements of lesion detectability and quantification in abdominal and pelvic regions. It has much potential to solve the dilemma of the use of sedation and long acquisition time that influence the health of pediatric patients.

Trust-aware conditional adversarial domain adaptation with feature norm alignment.

Adversarial learning has proven to be an effective method for capturing transferable features for unsupervised domain adaptation. However, some existing conditional adversarial domain adaptation methods assign equal importance to different samples, ignoring the fact that hard-to-transfer samples might damage the conditional adversarial adaptation procedure. Meanwhile, some methods can only roughly align marginal distributions across domains, but cannot ensure category distributions alignment, causing classifiers to make uncertain or even wrong predictions for some target data. Furthermore, we find that the feature norms of real images usually follow a complex distribution, so directly matching the mean feature norms of two domains cannot effectively reduce the statistical discrepancy of feature norms and may potentially induce feature degradation. In this paper, we develop a Trust-aware Conditional Adversarial Domain Adaptation (TCADA) method for solving the aforementioned issues. To quantify data transferability, we suggest utilizing posterior probability modeled by a Gaussian-uniform mixture, which effectively facilitates conditional domain alignment. Based on this posterior probability, a confidence-guided alignment strategy is presented to promote precise alignment of category distributions and accelerate the learning of shared features. Moreover, a novel optimal transport-based strategy is introduced to align the feature norms and facilitate shared features becoming more informative. To encourage classifiers to make more accurate predictions for target data, we also design a mixed information-guided entropy regularization term to promote deep features being away from the decision boundaries. Extensive experiments show that our method greatly improves transfer performance on various tasks.

Partial Unbalanced Feature Transport for Cross-Modality Cardiac Image Segmentation.

Deep learning based approaches have achieved great success on the automatic cardiac image segmentation task. However, the achieved segmentation performance remains limited due to the significant difference across image domains, which is referred to as domain shift. Unsupervised domain adaptation (UDA), as a promising method to mitigate this effect, trains a model to reduce the domain discrepancy between the source (with labels) and the target (without labels) domains in a common latent feature space. In this work, we propose a novel framework, named Partial Unbalanced Feature Transport (PUFT), for cross-modality cardiac image segmentation. Our model facilities UDA leveraging two Continuous Normalizing Flow-based Variational Auto-Encoders (CNF-VAE) and a Partial Unbalanced Optimal Transport (PUOT) strategy. Instead of directly using VAE for UDA in previous works where the latent features from both domains are approximated by a parameterized variational form, we introduce continuous normalizing flows (CNF) into the extended VAE to estimate the probabilistic posterior and alleviate the inference bias. To remove the remaining domain shift, PUOT exploits the label information in the source domain to constrain the OT plan and extracts structural information of both domains, which are often neglected in classical OT for UDA. We evaluate our proposed model on two cardiac datasets and an abdominal dataset. The experimental results demonstrate that PUFT achieves superior performance compared with state-of-the-art segmentation methods for most structural segmentation.

AIGAN: Attention-encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images.

X-ray computed tomography (CT) and positron emission tomography (PET) are two of the most commonly used medical imaging technologies for the evaluation of many diseases. Full-dose imaging for CT and PET ensures the image quality but usually raises concerns about the potential health risks of radiation exposure. The contradiction between reducing the radiation exposure and remaining diagnostic performance can be addressed effectively by reconstructing the low-dose CT (L-CT) and low-dose PET (L-PET) images to the same high-quality ones as full-dose (F-CT and F-PET). In this paper, we propose an Attention-encoding Integrated Generative Adversarial Network (AIGAN) to achieve efficient and universal full-dose reconstruction for L-CT and L-PET images. AIGAN consists of three modules: the cascade generator, the dual-scale discriminator and the multi-scale spatial fusion module (MSFM). A sequence of consecutive L-CT (L-PET) slices is first fed into the cascade generator that integrates with a generation-encoding-generation pipeline. The generator plays the zero-sum game with the dual-scale discriminator for two stages: the coarse and fine stages. In both stages, the generator generates the estimated F-CT (F-PET) images as like the original F-CT (F-PET) images as possible. After the fine stage, the estimated fine full-dose images are then fed into the MSFM, which fully explores the inter- and intra-slice structural information, to output the final generated full-dose images. Experimental results show that the proposed AIGAN achieves the state-of-the-art performances on commonly used metrics and satisfies the reconstruction needs for clinical standards.

Altered nonlinear Granger causality interactions in the large-scale brain networks of patients with schizophrenia.

Objective.It has been demonstrated that schizophrenia (SZ) is characterized by functional dysconnectivity involving extensive brain networks. However, the majority of previous studies utilizing resting-state functional magnetic resonance imaging (fMRI) to infer abnormal functional connectivity (FC) in patients with SZ have focused on the linear correlation that one brain region may influence another, ignoring the inherently nonlinear properties of fMRI signals.Approach. In this paper, we present a neural Granger causality (NGC) technique for examining the changes in SZ's nonlinear causal couplings. We develop static and dynamic NGC-based analyses of large-scale brain networks at several network levels, estimating complicated temporal and causal relationships in SZ patients.Main results. We find that the NGC-based FC matrices can detect large and significant differences between the SZ and healthy control groups at both the regional and subnetwork scales. These differences are persistent and significantly overlapped at various network sparsities regardless of whether the brain networks were built using static or dynamic techniques. In addition, compared to controls, patients with SZ exhibited extensive NGC confusion patterns throughout the entire brain.Significance. These findings imply that the NGC-based FCs may be a useful method for quantifying the abnormalities in the causal influences of patients with SZ, hence shedding fresh light on the pathophysiology of this disorder.

DeU-Net 2.0: Enhanced deformable U-Net for 3D cardiac cine MRI segmentation.

Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient and accurate volume measurement in clinical applications. However, due to anisotropic resolution, ambiguous borders and complicated shapes, existing methods suffer from the degradation of accuracy and robustness in cardiac MRI segmentation. In this paper, we propose an enhanced Deformable U-Net (DeU-Net) for 3D cardiac cine MRI segmentation, composed of three modules, namely Temporal Deformable Aggregation Module (TDAM), Enhanced Deformable Attention Network (EDAN), and Probabilistic Noise Correction Module (PNCM). TDAM first takes consecutive cardiac MR slices (including a target slice and its neighboring reference slices) as input, and extracts spatio-temporal information by an offset prediction network to generate fused features of the target slice. Then the fused features are also fed into EDAN that exploits several flexible deformable convolutional layers and generates clear borders of every segmentation map. A Multi-Scale Attention Module (MSAM) in EDAN is proposed to capture long range dependencies between features of different scales. Meanwhile, PNCM treats the fused features as a distribution to quantify uncertainty. Experimental results show that our DeU-Net achieves the state-of-the-art performance in terms of the commonly used evaluation metrics on the Extended ACDC dataset and competitive performance on other two datasets, validating the robustness and generalization of DeU-Net.

RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.

The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed tomography (CT) and X-ray images have been well recognized to be two effective techniques for clinical COVID-19 disease diagnoses. Due to faster imaging time and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR) images is preferred for efficient diagnosis, assessment, and treatment. However, considering the similarity between COVID-19 and pneumonia, CXR samples with deep features distributed near category boundaries are easily misclassified by the hyperplanes learned from limited training data. Moreover, most existing approaches for COVID-19 detection focus on the accuracy of prediction and overlook uncertainty estimation, which is particularly important when dealing with noisy datasets. To alleviate these concerns, we propose a novel deep network named RCoNet ks for robust COVID-19 detection which employs Deformable Mutual Information Maximization (DeIM), Mixed High-order Moment Feature (MHMF), and Multiexpert Uncertainty-aware Learning (MUL). With DeIM, the mutual information (MI) between input data and the corresponding latent representations can be well estimated and maximized to capture compact and disentangled representational characteristics. Meanwhile, MHMF can fully explore the benefits of using high-order statistics and extract discriminative features of complex distributions in medical imaging. Finally, MUL creates multiple parallel dropout networks for each CXR image to evaluate uncertainty and thus prevent performance degradation caused by the noise in the data. The experimental results show that RCoNet ks achieves the state-of-the-art performance on an open-source COVIDx dataset of 15 134 original CXR images across several metrics. Crucially, our method is shown to be more effective than existing methods with the presence of noise in the data.