Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.

Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction.

Apparent Variation in Measurement Size of Colorectal Cancer Metastases to the Liver on Dual Contrast MRI.

Purpose: The aim of this study was to formally investigate the apparent variation in lesion size of hepatic metastatic lesions from colorectal cancer on hepatobiliary phase (HBP) and dual contrast images of magnetic resonance imaging performed with both hepatobiliary and extracellular contrast agents.

Methods: Patients with known colorectal carcinoma who had undergone dual contrast liver magnetic resonance imaging were identified in our institutional database. Metastatic lesions were measured semiautomatically on both HBP and dual contrast images with a custom software tool that automatically identifies the lesion edge and thereby the lesion diameter.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI.

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters.

Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI.

Purpose: Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI.

Fast acquisition with seamless stage translation (FASST) for a trimodal x-ray breast imaging system.

Purpose: A major technical obstacle to bringing x-ray multicontrast (i.e., attenuation, phase, and dark-field) imaging methodology to clinical use is the prolonged data acquisition time caused by the phase stepping procedure.