Addition of contrast-enhanced mammography enhancement patterns and morphology for differentiating benign from malignant papillary breast lesions.

Objectives: To investigate the capability of morphological and enhancement pattern analysis in contrast-enhanced mammography (CEM) to differentiate between benign and malignant papillary breast lesions.

Methods: This study involved 63 patients diagnosed with papillary breast lesions between January 2018 and December 2022. CEM was performed at two time intervals: between 2-3 min (T1, early phase) and 4-5 min (T2, later phase) after the injection of the contrast agent.

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

Purpose: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver.

Methods: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation.

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

Background: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.

Purpose: To develop a deep learning-based method for automatic septum segmentation from black-blood MR images for the myocardial T2* measurement of thalassemia patients.

Study Type: Retrospective.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging.

[Cone-beam pseudo lambda tomography under FDK framework].

The medical CT scanner is rapidly evolving from the fan-beam mode to the cone-beam geometry mode. In this paper, a new cone-beam pseudo Lambda tomography was proposed based on the Noo's fan beam super-short scan formula and FDK framework. The proposed pseudo-LT algorithm, which avoids the computation of any PI line and any differential operation, has a significant practical implementation, thus leading to the images with quality improvement and reduced artifacts.

[Elimination of direct derivative of the projection data for image reconstruction based on hybrid filter in helical cone-beam CT].

Objective: We present an alternative approach for precise reconstruction of the images from helical cone-beam projections combining Hilbert filter and Ramp filter.

Methods: Based on the Katsevich algorithm framework, the proposed algorithm combined the FDK-type algorithms and Katsevich algorithm for their respective advantages, to completely avoid the direct derivatives with respect to the coordinates on the detector plane.

Results: The experimental results validated the accuracy of the new algorithm, and this approach significantly improved the resolution of the reconstructed images with much reduced artifacts.

A noval fragile watermarking method for medical images using quadtree structure.

This paper presents a new fragile watermarking scheme based on integral wavelet transform. The proposed method uses the quadtrees structures received by the wavelet decomposition, statistical information on the nodes and secret key to choose the locations where to embed the watermark. Only one bit watermark information will be hidden in the found position.