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Background Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI. Methods Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups. Results The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control. Conclusions The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.

Liver vessel density can be evaluated by an imaging biomarker diffusion-derived vessel density (DDVD): DDVD/area(b0b2) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the liver signal when b is 0 or 2 (s/mm2); ROIarea0 and ROIarea2 refer to the region of interest (ROI) on b = 0 or 2 images; and Sb2 may be replaced by Sb15 (b = 15). This concept was validated in this study. Liver diffusion images were acquired at 1.5 T. For a scan-rescan repeatability study of six subjects, b values of 0 and 2 were used. The validation study was composed of 26 healthy volunteers and 19 consecutive suspected chronic viral hepatitis B patients, and diffusion images with b values of 0, 2, 15, 20, 30, 45, 50, 60, 80, 100, 200, 300, 600, and 800 were acquired. Four patients did not have liver fibrosis, and the rest were four stage 1, three stage 2, four stage 3, and one stage 4 patients, respectively. The intraclass correlation coefficient for repeatability was 0.994 for DDVD/area(Sb0Sb2) and 0.978 for DDVD/area(Sb0Sb15). In the validation study, DDVD/area(Sb0Sb2) and area(Sb0Sb15) were 14.80 ± 3.06 and 26.58 ± 3.97 for healthy volunteers, 10.51 ± 1.51 and 20.15 ± 2.21 for stage 1-2 fibrosis patients, and 9.42 ± 0.87 and 19.42 ± 1.89 for stage 3-4 fibrosis patients. For 16 patients where IVIM analysis was performed, a combination of DDVD/area, PF, and Dfast achieved the best differentiation for nonfibrotic livers and fibrotic livers. DDVD/area were weakly correlated with PF or Dfast. Both DDVD/area(Sb0Sb2) and area(Sb0Sb15) are useful imaging biomarkers to separate fibrotic and nonfibrotic livers, with fibrotic livers having lower measurements.

Background: Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer’s disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. Methods: In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans. Results: The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction. Conclusions: The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI.

Background: Recently a small cohort study demonstrated that intravoxel incoherent motion (IVIM) diffusion MRI can detect early stage liver fibrosis. Using modified IVIM data acquisition parameters, the current study aims to confirm this finding. Methods: Twenty-six healthy volunteers, three patients of chronic viral hepatitis-b but without fibrosis and one mild liver steatosis subject, and 12 viral hepatitis-b patients with fibrosis (stage 1–2=7, stage 3–4=5) were included in this study. With a 1.5-T MR scanner and respiration-gating, IVIM diffusion imaging was acquired using a single-shot echo-planar sequence with a b -value series of 2, 0, 1, 15, 20, 30, 45, 50, 60, 80, 100, 200, 300, 600, 800 s/mm 2 . Signal measurement was performed on right liver parenchyma. The first three very low b -values were excluded to improve the curve fitting stability, and bi-exponential segmented fitting was performed using the 12 b -values of 15~800 s/mm 2 . Both threshold b -values of 60 s/mm 2 and 200 s/mm 2 were tested. With a 3-dimensional tool, Dslow ( D ), PF ( f ) and Dfast ( D* ) values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate healthy volunteers from liver fibrosis patients. Results: Threshold b -value of 60 s/mm 2 was preferred over 200 s/mm 2 for separating healthy volunteers and liver fibrosis patients. The IVIM measures of the four patients without fibrosis resembled those of healthy volunteers. When threshold b -value =60 s/mm 2 was applied, PF (PF