MRI-based fat quantification of the liver: Is it time for commercially available products?

Purpose: (1) To assess the clinical applicability of commercially available solutions for MR-based quantification of the hepatic fat fraction (HFF) and (2) to compare their results with clinically established in-phase/oppose-phase (IP/OP) imaging as proposed by Dixon.

Methods: Twenty-eight patients underwent MRI examinations using multigradient-echo sequences including multi-peak modeling and T correction, IP/OP imaging and multi-echo spectroscopy with successive HFF evaluation. Histopathological examination yielded the fraction of adipose hepatocytes (fAH) and the presence of increased liver iron concentration (LIC).

Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.

The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial neural network) in its development to provide automatic calculation, feature extraction, and diagnostic support. As its performance in tumor entities, not including prostate cancer, remains unclear, our aim was to obtain more data about this aspect.