TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography.

Objective: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches.

Methods And Procedures: Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning.

Dynamic evaluation of unruptured intracranial aneurysms by 4D-CT angiography: comparison with digital subtraction angiography (DSA) and surgical findings.

Background: This study was to prospectively investigate the feasibility of four-dimensional computed tomography angiography (4D-CTA) with electrocardiogram-gated (ECG) reconstruction for preoperative evaluation of morphological parameters, and compared with digital subtraction angiography (DSA). We also aimed to detect pulsation in unruptured intracranial aneurysms (UIAs) by using 4D-CTA, as a potential predicting factor of growth or rupture.

Materials: 64 patients with 64 UIAs underwent ECG-gated dynamic 4D-CTA imaging before treatment, of which 46 patients additionally underwent DSA.

[F] AlF‑NOTA‑FAPI‑04 PET/CT as a promising tool for imaging fibroblast activation protein in gastrointestinal system cancers: a prospective investigation of comparative analysis with F-FDG.

Purpose: The radiopharmaceutical [F]AlF-NOTA-FAPI-04 presents a promising alternative to  Ga-FAPI owing to its relatively longer half-life. This study aimed to evaluate the clinical usefulness of [F]AlF-NOTA-FAPI-04 PET/CT for the diagnosis of primary and metastatic lesions in various types of gastrointestinal system cancers, compared with F-FDG PET/CT.

Methods: Patients diagnosed with gastrointestinal system malignancies were prospectively enrolled.

PET/CT-based radiomics analysis may help to predict neoadjuvant chemotherapy outcomes in breast cancer.

Background: The aim of this study was to evaluate the clinical usefulness of radiomics signature-derived F-fluorodeoxyglucose (F-FDG) positron emission tomography-computed tomography (PET-CT) for the early prediction of neoadjuvant chemotherapy (NAC) outcomes in patients with (BC).

Methods: A total of 124 patients with BC who underwent pretreatment PET-CT scanning and received NAC between December 2016 and August 2019 were studied. The dataset was randomly assigned in a 7:3 ratio to either the training or validation cohort.

Feasibility Study of Intelligent Three-Dimensional Accurate Liver Reconstruction Technology Based on MRI Data.

Intelligent three-dimensional (3D) reconstruction technology plays an important role in the diagnosis and treatment of diseases. It has been widely used in assisted liver surgery. At present, the 3D reconstruction information of liver is mainly obtained based on CT enhancement data.

A Giant Borderline Phyllodes Tumor of Breast With Skin Ulceration Leading to Non-Insular Tumorigenic Hypoglycemia: A Case Report and Literature Review.

Phyllodes tumor (PT) is a special type of breast tumors, including three types: malignant, borderline, and benign. Most of these tumors form unilateral disease and can rapidly increase in size. The occurrence of axillary lymph node metastasis is rare.

Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer.

Background: Determining the status of lymph node (LN) metastasis in rectal cancer patients preoperatively is crucial for the treatment option. However, the diagnostic accuracy of current imaging methods is low.

Purpose: To develop and test a model for predicting metastatic LNs of rectal cancer patients based on clinical data and MR images to improve the diagnosis of metastatic LNs.

Dynamically constructed network with error correction for accurate ventricle volume estimation.

Automated ventricle volume estimation (AVVE) on cardiac magnetic resonance (CMR) images is very important for clinical cardiac disease diagnosis. However, current AVVE methods ignore the error correction for the estimated volume. This results in clinically intolerable ventricle volume estimation error and further leads to wrong ejection fraction (EF) assessment, which significantly limits the application potential of AVVE methods.

Advanced myocardial characterization in hypertrophic cardiomyopathy: feasibility of CMR-based feature tracking strain analysis in a case-control study.

Objectives: This study aimed to evaluate the feasibility and reproducibility of using cardiovascular magnetic resonance feature tracking (CMR-FT) for analysis of bi-ventricular strain and strain rate (SR) in hypertrophic cardiomyopathy (HCM) patients as well as to explore the correlation between right ventricular (RV) and left ventricular (LV) deformation.

Methods: A total of 60 HCM patients and 48 controls were studied. Global and segmental peak values of bi-ventricular longitudinal, circumferential, radial strain, and systolic SR were analyzed.

Cardiac Magnetic Resonance Feature Tracking: A Novel Method to Assess Left Ventricular Three-Dimensional Strain Mechanics After Chronic Myocardial Infarction.

Rationale And Objectives: This study was designed to assess left ventricular deformation after chronic myocardial infarction (CMI) using cardiac magnetic resonance feature tracking (CMR-FT) technology, and analyze its relationship with left ventricular ejection fraction (LVEF) and infarcted transmurality.

Materials And Methods: Ninety-six patients with CMI and 72 controls underwent 3.0 T CMR scanning.

Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography.

We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography's challenges: high dimensional data, complex anatomical environments, and limited annotation data. First, we proposed a deep atlas network, which integrated LV atlas into the deep learning framework to address the 3D LV segmentation problem on echocardiography for the first time, and improved the performance based on limited annotation data.

Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification.

Accurate and automated cardiac bi-ventricle quantification based on cardiac magnetic resonance (CMR) image is a very crucial procedure for clinical cardiac disease diagnosis. Two traditional and commensal tasks, i.e.

A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography.

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework.

Imaging c-Met expression using 18F-labeled binding peptide in human cancer xenografts.

Objectives: c-Met is a receptor tyrosine kinase shown inappropriate expression and actively involved in progression and metastasis in most types of human cancer. Development of c-Met-targeted imaging and therapeutic agents would be extremely useful. Previous studies reported that c-Met-binding peptide (Met-pep1, YLFSVHWPPLKA) specifically targets c-Met receptor.

Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images.

Objective: Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task.

Methods: In this paper, we propose a direct volume prediction method based on the end-to-end deep convolutional neural networks.