Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular subtypes at the same time and provide reliable imaging biomarkers is still challenging.

Novel MRI-based Hyper-Fused Radiomics for Predicting Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer.

Rationale And Objectives: To propose a novel MRI-based hyper-fused radiomic approach to predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC).

Materials And Methods: Pretreatment dynamic contrast-enhanced (DCE) MRI and ultra-multi-b-value (UMB) diffusion-weighted imaging (DWI) data were acquired in BC patients who received NAT followed by surgery at two centers. Hyper-fused radiomic features (RFs) and conventional RFs were extracted from DCE-MRI or UMB-DWI.

MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy.

Purpose: To develop a novel MRI-based vector radiomic approach to predict breast cancer (BC) human epidermal growth factor receptor 2 (HER2) status (zero, low, and positive; task 1) and its changes after neoadjuvant therapy (NAT) (positive-to-positive, positive-to-negative, and positive-to-pathologic complete response; task 2).

Materials And Methods: Both dynamic contrast-enhanced (DCE) MRI data and multi-b-value (MBV) diffusion-weighted imaging (DWI) data were acquired in BC patients at two centers. Vector-radiomic and conventional-radiomic features were extracted from both DCE-MRI and MBV-DWI.

Deep wavelet scattering orthogonal fusion network for glioma IDH mutation status prediction.

Accurately predicting the isocitrate dehydrogenase (IDH) mutation status of gliomas is greatly significant for formulating appropriate treatment plans and evaluating the prognoses of gliomas. Although existing studies can accurately predict the IDH mutation status of gliomas based on multimodal magnetic resonance (MR) images and machine learning methods, most of these methods cannot fully explore multimodal information and effectively predict IDH status for datasets acquired from multiple centers. To address this issue, a novel wavelet scattering (WS)-based orthogonal fusion network (WSOFNet) was proposed in this work to predict the IDH mutation status of gliomas from multiple centers.

MD-SGT: Multi-dilation spherical graph transformer for unsupervised medical image registration.

Deformable medical image registration is an essential preprocess step for several clinical applications. Even though the existing convolutional neural network and transformer based methods achieved the promising results, the limited long-range spatial dependence and non-uniform attention span of these models prohibit further improving the registration performance. To deal with this issue, we proposed a multi-dilation spherical graph transformer (MD-SGT), in which the encoder combined the advantages of convolutional and graph transformer blocks to distinguish effectively the differences between the reference and the template images at various scales.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

Background: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.

Purpose: To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.

Background: The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.

Connecting macroscopic diffusion metrics of cardiac diffusion tensor imaging and microscopic myocardial structures based on simulation.

To investigate the relationship between microscopic myocardial structures and macroscopic measurements of diffusion tensor imaging (DTI), we proposed a cardiac DTI simulation method using the Bloch equation and the Monte Carlo random walk in a realistic myocardium model reconstructed from polarized light imaging (PLI) data of the entire human heart. To obtain a realistic simulation, with the constraints of prior knowledge pertaining to the maturational change of the myocardium structure, appropriate microstructure modeling parameters were iteratively determined by matching DTI simulations and real acquisitions of the same hearts in terms of helix angle, fractional anisotropy (FA) and mean diffusivity (MD) maps. Once a realistic simulation was obtained, we varied the extra-cellular volume (ECV) ratio, myocyte orientation heterogeneity and myocyte size, and explored the effects of microscopic changes in tissue structure on macroscopic diffusion metrics.

An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and Parameter Estimation in the Liver.

Background: Dynamic-exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver tumors. However, the use of such technique at voxel level is still limited.

Purpose: To develop an unsupervised deep learning approach for voxel-wise dynamic-exponential IVIM modeling and parameter estimation in the liver.

Short-Term Repeatability of in Vivo Cardiac Intravoxel Incoherent Motion Tensor Imaging in Healthy Human Volunteers.

Background: Intravoxel incoherent motion (IVIM) tensor imaging is a promising technique for diagnosis and monitoring of cardiovascular diseases. Knowledge about measurement repeatability, however, remains limited.

Purpose: To evaluate short-term repeatability of IVIM tensor imaging in normal in vivo human hearts.

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT.

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules.

Investigation of intravoxel incoherent motion tensor imaging for the characterization of the in vivo human heart.

Purpose: To investigate intravoxel incoherent motion (IVIM) tensor imaging of the in vivo human heart and elucidate whether the estimation of IVIM tensors is affected by the complexity of pseudo-diffusion components in myocardium.

Methods: The cardiac IVIM data of 10 healthy subjects were acquired using a diffusion weighted spin-echo echo-planar imaging sequence along 6 gradient directions with 10 b values (0~400 s/mm ). The IVIM data of left ventricle myocardium were fitted to the IVIM tensor model.

Non-mono-exponential diffusion models for assessing early response of liver metastases to chemotherapy in colorectal Cancer.

Background: Preoperative chemotherapy is becoming standard therapy for liver metastasis from colorectal cancer, so early assessment of treatment response is crucial to make a reasonable therapeutic regimen and avoid overtreatment, especially for patients with severe side effects. The role of three non-mono-exponential diffusion models, such as the kurtosis model, the stretched exponential model and the statistical model, were explored in this study to early assess the response to chemotherapy in patients with liver metastasis from colorectal cancer.

Methods: Thirty-three patients diagnosed as colorectal liver metastasis were evaluated in this study.

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks.

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net).

Structure Prior Constrained Estimation of Human Cardiac Diffusion Tensors.

Objective: The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions.

Methods: A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator.

Pulmonary nodule segmentation with CT sample synthesis using adversarial networks.

Purpose: Segmentation of pulmonary nodules is critical for the analysis of nodules and lung cancer diagnosis. We present a novel framework of segmentation for various types of nodules using convolutional neural networks (CNNs).

Methods: The proposed framework is composed of two major parts.

Evaluation of non-monoexponential diffusion models for hepatocellular carcinoma using b values up to 2000 s/mm : A short-term repeatability study.

Background: Non-monoexponential diffusion models are being used increasingly for the characterization and curative effect evaluation of hepatocellular carcinoma (HCC). But the fitting quality of the models and the repeatability of their parameters have not been assessed for HCC.

Purpose: To evaluate kurtosis, stretched exponential, and statistical models for diffusion-weighted imaging (DWI) of HCC, using b-values up to 2000 s/mm , in terms of fitting quality and repeatability.

Quantitative comparison of human myocardial fiber orientations derived from DTI and polarized light imaging.

Diffusion tensor imaging (DTI) is a non-invasive technique used to obtain the 3D fiber structure of whole human hearts, for both in vivo and ex vivo cases. However, by essence, DTI does not measure directly the orientations of myocardial fibers. In contrast, polarized light imaging (PLI) allows for physical measurements of fiber orientations, but only for ex vivo case.

Determination of Hepatocellular Carcinoma and Characterization of Hepatic Focal Lesions with Adaptive Multi-Exponential Intravoxel Incoherent Motion Model.

Purpose: To distinguish hepatocellular carcinoma (HCC) from other types of hepatic lesions with the adaptive multi-exponential IVIM model.

Methods: 94 hepatic focal lesions, including 38 HCC, 16 metastasis, 12 focal nodular hyperplasia, 13 cholangiocarcinoma, and 15 hemangioma, were examined in this study. Diffusion-weighted images were acquired with 13 b values (b = 0, 3, …, 500 s/mm) to measure the adaptive multi-exponential IVIM parameters, namely, pure diffusion coefficient (D), diffusion fraction (f), pseudo-diffusion coefficient (D*) and perfusion-related diffusion fraction (f) of the ith perfusion component.

Effect of multiple perfusion components on pseudo-diffusion coefficient in intravoxel incoherent motion imaging.

The aim of this work was to investigate the effect of multiple perfusion components on the pseudo-diffusion coefficient D in the bi-exponential intravoxel incoherent motion (IVIM) model. Simulations were first performed to examine how the presence of multiple perfusion components influences D . The real data of livers (n  =  31), spleens (n  =  31) and kidneys (n  =  31) of 31 volunteers was then acquired using DWI for in vivo study and the number of perfusion components in these tissues was determined together with their perfusion fraction and D , using an adaptive multi-exponential IVIM model.

Image-Based Investigation of Human in Vivo Myofibre Strain.

Cardiac myofibre deformation is an important determinant of the mechanical function of the heart. Quantification of myofibre strain relies on 3D measurements of ventricular wall motion interpreted with respect to the tissue microstructure. In this study, we estimated in vivo myofibre strain using 3D structural and functional atlases of the human heart.

Generalization of intravoxel incoherent motion model by introducing the notion of continuous pseudodiffusion variable.

Purpose: A generalized intravoxel incoherent motion (IVIM) model, called the GIVIM, was proposed to better account for complex perfusion present in the tissues having various vessels and flow regimes, such as the liver.

Theory And Methods: The notions of continuous pseudodiffusion variable as well as perfusion fraction density function were introduced to describe the presence of multiple perfusion components in a voxel. The mean and standard deviation of Gaussian perfusion fraction density function were then used to define two new parameters, the mean pseudodiffusivity ( D¯) and pseudodiffusion dispersion (σ).

Assessment of the Characteristics of Orientation Distribution Functions in HARDI Using Morphological Metrics.

Orientation distribution functions (ODFs) are widely used to resolve fiber crossing problems in high angular resolution diffusion imaging (HARDI). The characteristics of the ODFs are often assessed using a visual criterion, although the use of objective criteria is also reported, which are directly borrowed from classic signal and image processing theory because they are intuitive and simple to compute. However, they are not always pertinent for the characterization of ODFs.

Quantitative representation and description of intravoxel fiber complexity in HARDI.

Diffusion tensor imaging and high angular resolution diffusion imaging are often used to analyze the fiber complexity of tissues. In these imaging techniques, the most commonly calculated metric is anisotropy, such as fractional anisotropy (FA), generalized anisotropy (GA), and generalized fractional anisotropy (GFA). The basic idea underlying these metrics is to compute the deviation from free or spherical diffusion.