Sub-Microliter H Magnetic Resonance Spectroscopy for In Vivo High-Spatial Resolution Metabolite Quantification in the Mouse Brain.

Proton magnetic resonance spectroscopy (MRS) offers a non-invasive, repeatable, and reproducible method for in vivo metabolite profiling of the brain and other tissues. However, metabolite fingerprinting by MRS requires high signal-to-noise ratios for accurate metabolite quantification, which has traditionally been limited to large volumes of interest, compromising spatial fidelity. In this study, we introduce a new optimized pipeline that combines LASER MRS acquisition at 11.

Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation.

Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more slowly progressive than amyotrophic laterals sclerosis (ALS). `Classical` ALS is characterized by catabolism and abnormal energy metabolism preceding onset of motor symptoms, and previous studies indicated that the disease progression of ALS involves hypothalamic atrophy.

Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.

Purpose: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.

From Phantoms to Patients: Improved Fusion and Voxel-Wise Analysis of Diffusion-Weighted Imaging and FDG-Positron Emission Tomography in Positron Emission Tomography/Magnetic Resonance Imaging for Combined Metabolic-Diffusivity Index (cDMI).

Hybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses.