CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation

Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer)

SCAview: an Intuitive Visual Approach to the Integrative Analysis of Clinical Data in Spinocerebellar Ataxias

With SCAview, we present a prompt and comprehensive tool that enables scientists to browse large datasets of the most common spinocerebellar ataxias intuitively and without technical effort. Basic concept is a visualization of data, with a graphical handling and filtering to select and define subgroups and their comparison. Several plot types to visualize all data points resulting from the selected attributes are provided. The underlying synthetic cohort is based on clinical data from five different European and US longitudinal multicenter cohorts in spinocerebellar ataxia type 1, 2, 3, and 6 (SCA1, 2, 3, and 6) comprising > 1400 patients with overall > 5500 visits. First, we developed a common data model to integrate the clinical, demographic, and characterizing data of each source cohort. Second, the available datasets from each cohort were mapped onto the data model. Third, we created a synthetic cohort based on the cleaned dataset. With SCAview, we demonstrate the feasibility of mapping cohort data from different sources onto a common data model. The resulting browser-based visualization tool with a thoroughly graphical handling of the data offers researchers the unique possibility to visualize relationships and distributions of clinical data, to define subgroups and to further investigate them without any technical effort. Access to SCAview can be requested via the Ataxia Global Initiative and is free of charge

Repetition time and flip angle variation in SPRITE imaging for acquisition time and SAR reduction

Single point imaging methods such as SPRITE are often the technique of choice for imaging fast-relaxing nuclei in solids. Single point imaging sequences based on SPRITE in their conventional form are ill-suited for in vivo applications since the acquisition time is long and the SAR is high. A new sequence design is presented employing variable repetition times and variable flip angles in order to improve the characteristics of SPRITE for in vivo applications. The achievable acquisition time savings as well as SAR reductions and/or SNR increases afforded by this approach were investigated using a resolution phantom as well as PSF simulations. Imaging results in phantoms indicate that acquisition times may be reduced by up to 70% and the SAR may be reduced by 40% without an appreciable loss of image quality

Helium bubble formation in 800 MeV proton-irradiated 304L stainless steel and alloy 718 during post-irradiation annealing

The bubble formation in AISI 304L and Alloy 718 irradiated with 800 MeV protons and annealed at temperatures up to 1100 degreesC has been studied by TEM. The specimens were obtained from spent target components of the LANSCE facility at the Los Alamos National Laboratory. In this and other spallation sources high concentrations of helium are generated concurrent with the displacement damage. In the specimens irradiated with around 3 x 10(25) p/m(2) (approximate to8.4 dpa), first visible bubbles appeared at 700 degreesC in Alloy 718 and at 800 degreesC in 304L stainless steel, respectively. Two temperature regions with different coarsening mechanisms could be identified and interpreted as bubble migration and coalescence at lower temperatures and Ostwald ripening at higher temperatures. From the measured bubble densities and size distributions, He concentrations were determined and compared to values obtained by release and nuclear reaction measurements, respectively. (C) 2002 Elsevier Science B.V. All rights reserved