Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

Impact of Eating a Carbohydrate-Restricted Diet on Cortical Atrophy in a Cross-Section of Amyloid Positive Patients with Alzheimer's Disease: A Small Sample Study.

BACKGROUND: A carbohydrate-restricted diet aimed at lowering insulin levels has the potential to slow Alzheimer's disease (AD). Restricting carbohydrate consumption reduces insulin resistance, which could improve glucose uptake and neural health. A hallmark feature of AD is widespread cortical thinning; however, no study has demonstrated that lower net carbohydrate (nCHO) intake is linked to attenuated cortical atrophy in patients with AD and confirmed amyloidosis. OBJECTIVE: We tested the hypothesis that individuals with AD and confirmed amyloid burden eating a carbohydrate-restricted diet have thicker cortex than those eating a moderate-to-high carbohydrate diet. METHODS: A total of 31 patients (mean age 71.4±7.0 years) with AD and confirmed amyloid burden were divided into two groups based on a 130 g/day nCHO cutoff. Cortical thickness was estimated from T1-weighted MRI using FreeSurfer. Cortical surface analyses were corrected for multiple comparisons using cluster-wise probability. We assessed group differences using a two-tailed two-independent sample t-test. Linear regression analyses using nCHO as a continuous variable, accounting for confounders, were also conducted. RESULTS: The lower nCHO group had significantly thicker cortex within somatomotor and visual networks. Linear regression analysis revealed that lower nCHO intake levels had a significant association with cortical thickness within the frontoparietal, cingulo-opercular, and visual networks. CONCLUSIONS: Restricting carbohydrates may be associated with reduced atrophy in patients with AD. Lowering nCHO to under 130 g/day would allow patients to follow the well-validated MIND diet while benefiting from lower insulin levels.

Omega-3 Fatty Acids, Cognition, and Brain Volume in Older Adults.

The elderly population is growing at increased rates and is expected to double in size by 2050 in the United States and worldwide. The consumption of healthy foods and enriched diets have been associated with improved cognition and brain health. The key nutrients common to many healthy foods and diets are the omega-3 polyunsaturated fatty acids (omega-3 FAs), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). We explored whether omega-3 FA levels are associated with brain volume and cognition. Forty healthy, cognitively normal, Seventh-day Adventist older adults (mean age 76.3 years at MRI scan, 22 females) completed neurocognitive testing, a blood draw, and structural neuroimaging from 2016 to 2018. EPA and an overall omega-3 index were associated with individual measures of delayed recall (RAVLT-DR) and processing speed (Stroop Color) as well as entorhinal cortex thickness. EPA, DHA, and the omega-3 index were significantly correlated with the total white matter volume. The entorhinal cortex, frontal pole, and total white matter were associated with higher scores on delayed memory recall. This exploratory study found that among healthy, cognitively older adults, increased levels of omega-3 FAs are associated with better memory, processing speed, and structural brain measures.

3D Neural Networks for Visceral and Subcutaneous Adipose Tissue Segmentation using Volumetric Multi-Contrast MRI.

Individuals with obesity have larger amounts of visceral (VAT) and subcutaneous adipose tissue (SAT) in their body, increasing the risk for cardiometabolic diseases. The reference standard to quantify SAT and VAT uses manual annotations of magnetic resonance images (MRI), which requires expert knowledge and is time-consuming. Although there have been studies investigating deep learning-based methods for automated SAT and VAT segmentation, the performance for VAT remains suboptimal (Dice scores of 0.43 to 0.89). Previous work had key limitations of not fully considering the multi-contrast information from MRI and the 3D anatomical context, which are critical for addressing the complex spatially varying structure of VAT. An additional challenge is the imbalance between the number and distribution of pixels representing SAT/VAT. This work proposes a network based on 3D U-Net that utilizes the full field-of-view volumetric T1-weighted, water, and fat images from dual-echo Dixon MRI as the multi-channel input to automatically segment SAT and VAT in adults with overweight/obesity. In addition, this work extends the 3D U-Net to a new Attention-based Competitive Dense 3D U-Net (ACD 3D U-Net) trained with a class frequency-balancing Dice loss (FBDL). In an initial testing dataset, the proposed 3D U-Net and ACD 3D U-Net with FBDL achieved 3D Dice scores of (mean ± standard deviation) 0.99 ±0.01 and 0.99±0.01 for SAT, and 0.95±0.04 and 0.96 ±0.04 for VAT, respectively, compared to manual annotations. The proposed 3D networks had rapid inference time (<60 ms/slice) and can enable automated segmentation of SAT and VAT.Clinical relevance- This work developed 3D neural networks to automatically, accurately, and rapidly segment visceral and subcutaneous adipose tissue on MRI, which can help to characterize the risk for cardiometabolic diseases such as diabetes, elevated glucose levels, and hypertension.