Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors.

BACKGROUND: The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. METHODS: Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session. RESULTS: Significant MRI site and vendor effects (p â€‹< â€‹.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman's r correlations >0.43, p â€‹< â€‹1.39E-36). In particular, volumes larger than 200 â€‹mm3 (for amygdalar nuclei) and 300 â€‹mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε â€‹< â€‹5% and DICE â€‹> â€‹0.80). CONCLUSION: Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.

Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation.

Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of several clinical outcomes. In this paper, we propose and evaluate a novel three-dimensional (3-D) convolutional neural network (CNN) designed for tissue classification in medical imaging and applied for discriminating between primary and metastatic liver tumors from diffusion weighted MRI (DW-MRI) data. The proposed network consists of four consecutive strided 3-D convolutional layers with 3 × 3 × 3 kernel size and rectified linear unit (ReLU) as activation function, followed by a fully connected layer with 2048 neurons and a Softmax layer for binary classification. A dataset comprising 130 DW-MRI scans was used for the training and validation of the network. To the best of our knowledge this is the first DL solution for the specific clinical problem and the first 3-D CNN for cancer classification operating directly on whole 3-D tomographic data without the need of any preprocessing step such as region cropping, annotating, or detecting regions of interest. The classification performance results, 83% (3-D) versus 69.6% and 65.2% (2-D), demonstrated significant tissue classification accuracy improvement compared to two 2-D CNNs of different architectures also designed for the specific clinical problem with the same dataset. These results suggest that the proposed 3-D CNN architecture can bring significant benefit in DW-MRI liver discrimination and potentially, in numerous other tissue classification problems based on tomographic data, especially in size-limited, disease-specific clinical datasets.

Value of 3 Tesla diffusion-weighted magnetic resonance imaging for assessing liver fibrosis.

BACKGROUND: Limited data are available regarding the role of magnetic resonance imaging (MRI), particularly the new generation 3 Tesla technology, and especially diffusion-weighted imaging (DWI) in predicting liver fibrosis. The aim of our pilot study was to assess the clinical performance of the apparent diffusion coefficient (ADC) of liver parenchyma for the assessment of liver fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). METHODS: 18 patients with biopsy-proven NAFLD underwent DWI with 3 Tesla MRI. DWI was performed with single-shot echo-planar technique at b values of 0-500 and 0-1000 s/mm2. ADC was measured in four locations in the liver and the mean ADC value was used for analysis. Staging of fibrosis was performed according to the METAVIR system. RESULTS: The median age of patients was 52 years (range 23-73). The distribution of patients in different fibrosis stages was: 0 (n=1), 1 (n=7), 2 (n=1), 3 (n=5), 4 (n=4). Fibrosis stage was poorly associated with ADC at b value of 0-500 s/mm2 (r= -0.30, P=0.27). However it was significantly associated with ADC at b value of 0-1000 s/mm2 (r= -0.57, P=0.01). For this b value (0-1000 s/mm2) the area under receiver-operating characteristic curve was 0.93 for fibrosis stage ≥3 and the optimal ADC cut-off value was 1.16 ×10-3 mm2/s. CONCLUSION: 3 Tesla DWI can possibly predict the presence of advanced fibrosis in patients with NAFLD.