Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

Rationale And Objectives: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).

Materials And Methods: A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction.

Machine learning prediction of brain metastasis invasion pattern on brain magnetic resonance imaging scans.

Background: Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination.

Surgeon versus radiologist: an inter-rater reliability analysis of the CLOSE checklist for preoperative CT sinus assessment.

Purpose: To assess the inter-rater agreement of the Cribriform plate, Lamina papyracea, Onodi cell, Sphenoid sinus pneumatization, and Ethmoidal artery (CLOSE) checklist results among rhinology & skull-base surgeons and a head and neck-neuroradiology specialist for pre-operative computed tomography (CT) sinus assessment.

Methods: This retrospective cross-sectional study reviewed 50 patients who underwent endoscopic sinus surgery (ESS) in the period between January 2013 and March 2014 at the Royal Victoria Hospital in Montreal, Canada. According to the CLOSE checklist, the CT scans were evaluated independently by one surgeon and one radiologist using the InteleRadiology Picture Archiving and Communication System (IntelePACS).

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.

Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption.