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Background: Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment.Purpose: To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy.Study Type: Retrospective secondary analysis of prospectively acquired clinical research data.Population: A total of 391 adults (173 men, 218 women) with known or suspected NAFLD.Field Strength/sequence: Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique.Assessment: An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average.Statistical Testing: Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses.Results: The study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) <1.5%, including four-ROI strategies with two ROIs from each lobe, which all had LOA <1.5%. 14/36 (39%) of two-ROI strategies and 74/84 (88%) of three-ROI strategies had ICC >0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%.Data Conclusion: Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF.Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:988-994. [ABSTRACT FROM AUTHOR]

Objectives Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree ( GFT) design called the LITHium Intelligent Agent ( LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1H- MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. Methods We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1H- MRS scans at baseline pre-treatment. We trained LITHIA using 18 1H- MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. Results LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. Conclusions The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. [ABSTRACT FROM AUTHOR]

Functional neuroimaging studies have identified several 'core' brain regions that are preferentially activated by scene stimuli, namely posterior parahippocampal gyrus (PHG), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS). The hippocampus (HC), too, is thought to play a key role in scene processing, although no study has yet investigated scene-sensitivity in the HC relative to these other 'core' regions. Here, we characterised the frequency and consistency of individual scene-preferential responses within these regions by analysing a large dataset ( n = 51) in which participants performed a one-back working memory task for scenes, objects, and scrambled objects. An unbiased approach was adopted by applying independently-defined anatomical ROIs to individual-level functional data across different voxel-wise thresholds and spatial filters. It was found that the majority of subjects had preferential scene clusters in PHG (max = 100% of participants), RSC (max = 76%), and TOS (max = 94%). A comparable number of individuals also possessed significant scene-related clusters within their individually defined HC ROIs (max = 88%), evidencing a HC contribution to scene processing. While probabilistic overlap maps of individual clusters showed that overlap 'peaks' were close to those identified in group-level analyses (particularly for TOS and HC), inter-individual consistency varied across regions and statistical thresholds. The inter-regional and inter-individual variability revealed by these analyses has implications for how scene-sensitive cortex is localised and interrogated in functional neuroimaging studies, particularly in medial temporal lobe regions, such as the HC. Hum Brain Mapp 37:3779-3794, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: The region-of-interest (ROI) selection and evaluation is one of the key factors in the successful evaluation of radiological images. However, the presence of noise in images may lead to incorrect diagnosis. The aim of this study was to test the hypothesis that the weighting by error estimation in ROI assessment might significantly improve the validity of the results. Methods: As a model, the data maps of the transverse relaxation time constants (T2) from patients who underwent a matrix-associated chondrocyte transplantation procedure on the femoral condyle were analyzed. Artificial noise with a Rician density probability distribution was added to each TE image. ROIs were processed either as a regular arithmetic mean or as a weighted mean, in which weighted coefficients were calculated with regard to fitting error estimates [coefficient of determination (R2); root mean squared error (RMSE), mean absolute error (MSE), mean squared error (MAE), and chi-squared error (χ2)]. Results: The global T2 values in repair tissue (mean±standard deviation, 62±7 ms; range 51–70 ms) and in healthy cartilage (mean±SD, 49±6 ms; range 40–60 ms) were significantly different (p<0.001). With a 45% or greater decrease from the original SNR value (corresponding to a noise level of 35% of random value), the statistical significance was lost (P>0.05); however, the use of the coefficient of determination (R2) as a correction factor was able to maintain the p-value of <0.05 up to a 56% decrease from the original SNR value. Conclusions: The results of this study can prospectively be applied in a wide range of radiological imaging techniques in cases when error estimation is possible. Our analysis on MR images with artificially added noise showed that utilization of the correlation of determination (R2) as a weighting parameter in ROI evaluation may significantly improve the differentiation between native and transplanted cartilage tissue in noisy images. This could be an added benefit in the non-invasive monitoring of the post-operative status of patients with cartilage transplants if the MR images are not ideal (e.g., lower field strength or lower SNR). [ABSTRACT FROM AUTHOR]

Objectives: Functional and postmortem studies suggest that the orbitofrontal cortex (OFC) is involved in the pathophysiology of bipolar disorder (BD). This anatomical magnetic resonance imaging (MRI) study examined whether BD patients have smaller OFC gray matter volumes compared to healthy comparison subjects (HC). Methods: Twenty-eight BD patients were compared to 28 age- and gender-matched HC. Subjects underwent a 1.5T MRI with 3D spoiled gradient recalled acquisition. Total OFC and medial and lateral subdivisions were manually traced by a blinded examiner. Images were segmented and gray matter volumes were calculated using an automated method. Results: Analysis of covariance, with intracranial volume as covariate, showed that BD patients and HC did not differ in gray matter volumes of total OFC or its subdivisions. However, total OFC gray matter volume was significantly smaller in depressed patients (n = 10) compared to euthymic patients (n = 18). Moreover, total OFC gray matter volumes were inversely correlated with depressive symptom intensity, as assessed by the Hamilton Depression Rating Scale. OFC gray matter volumes were not related to lithium treatment, age at disease onset, number of episodes, or family history of mood disorders. Conclusions: Our results suggest that abnormal OFC gray matter volumes are not a pervasive characteristic of BD, but may be associated with specific clinical features of the disorder. [ABSTRACT FROM AUTHOR]

The region-of-interest (ROI) in computer-assisted densitometric image analysis (CADIA) is defined by tis sized and location. This study evaluated the effects of these variables on two quantitative parameters, the change in density and the area with change in density. Pairs of radiographs for these analyses were obtained from sites with "no change" or "known loss" in Cynomolgus monkeys during a study of ligature-induced periodontitis. The size and location of the ROI had clear effects on the two CADIA parameters. To reduce these effects, a corrective procedure hypothesizing a linear relationship between the ROI size and the area with change in density was tested, while this procedure was sufficient to correct for ROI size-dependent differences among "no change" images, it was not effective in images with "known loss". In such images additional adjustments by increasing threshold levels for the registered change in density were found to reduce but not to eliminate differences between ROIs of different size and location. when densitometric radiographic analysis is used for quantitative comparison of different sites, it is primarily recommended to select ROIs of similar size and location. Application of corrective procedures should follow only after careful evaluation of their effects on sensitivity and specificity. [ABSTRACT FROM AUTHOR]

In high‐magnetic magnetic resonance imaging (MRI) systems (7 T and above), the radio frequency (RF) field shows greater inhomogeneity compared with clinical MRI systems (1.5 and 3.0 T). In multi‐channel RF coils, driving each coil element with convex optimisation can reduce non‐uniformity of the RF field in a region of interest. In this Letter, a periodic compressed method (PCM) and a PCM after averaging are proposed in order to reduce RF coil control time at high fields. Simulation results were obtained using a human head model in 7 and 9.4 T systems, and the results are discussed in detail. [ABSTRACT FROM AUTHOR]

The raster scan method in visual information processing is the rectangular pattern of image capture and reconstruction which systematically covers the area progressively, one line at a time starting from the top left corner. This traditional scanning method might work well for still image processing but for moving images, a scanning method such as diagonal snake scan can provide a better object correlation in a sequence of image frames. A novel adaptive scanning method for object‐oriented visual information processing is proposed. Here, image pixels are collected starting from the centre of the image area and progressively extended outwards similar to the water drop ripple effect. Simulation results have shown that the proposed work uses up to 33% less processing time to cover the region‐of‐interest in an image compared to the raster scan method. Preliminary simulation results have also shown that with the proposed adaptive scanning methodology, there is a significant improvement in the performance of the object‐oriented visual compression. [ABSTRACT FROM AUTHOR]