Mitigating susceptibility-induced distortions in high-resolution 3DEPI fMRI at 7T.

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI.

Quantitative Assessment of Resting-State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late-Onset Alzheimer's Disease From Normal Subjects.

BACKGROUND: Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting-state functional MRI (rs-fMRI) can provide valuable information about the brain network pattern in early AD diagnosis. PURPOSE: To quantitatively assess FC patterns of resting-state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late-onset AD from normal. STUDY TYPE: Prospective. SUBJECTS: A total of 14 normal, 16 aMCI, and 13 late-onset AD. FIELD STRENGTH/SEQUENCE: A 3.0 T; rs-fMRI: single-shot 2D-EPI and T1-weighted structure: MPRAGE. ASSESSMENT: By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI-to-ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs. STATISTICAL TESTS: Region of interest (ROI)-based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)-corrected P < 0.05 cluster-level threshold together with posthoc uncorrected P < 0.05 connection-level threshold. Graph-theory analysis (GTA): P-FDR-corrected < 0.05. One-way ANOVA and Chi-square tests were used to compare clinical characteristics. RESULTS: PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global-efficiency (28.05 < 45), local-efficiency (22.98 < 24.05), and betweenness-centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local-efficiency (33.46 > 24.05) and clustering-coefficient (25 > 20.18) were found in aMCI compared to normal. DATA CONCLUSION: This study demonstrated resting-state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study.

(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.

SSFP fMRI at 3 tesla: Efficiency of polar acquisition-reconstruction technique.

SSFP-based fMRI techniques, known for their high specificity and low geometrical distortion, look promising for high-resolution brain mapping. Nevertheless, they suffer from lack of speed and sensitivity, leading them to be exploited mostly in high-field scanners. Radial acquisition can help with these inefficiencies through better tSNR and more effective coverage of the spatial frequencies. Here, we present a SSFP-fMRI approach and experimentally investigate it at 3 T scanners using radial readout for acquisition. In particular, the visual activity is mapped through three bSSFP techniques: 1- Cartesian, 2- Radial with re-gridding reconstruction, 3- Radial with Polar Fourier Transform (PFT) reconstruction. In the PFT technique streaking artifacts, generated at high acceleration rates by re-gridding reconstruction, are avoided and pixel size in the final framework is retrospectively selectable. General agreement, but better tSNR of Radial reading, was first confirmed for these techniques in detection of neural activities at 2 × 2 mm2 in-plane resolution for all 28 subjects,. Next the outcome of the PFT algorithm with 1 × 1 mm2 pixel size was compared to images reconstructed by re-gridding (from the same raw data) with the identical pixel size through interpolation. The localization of the activity showed improvement in PFT over interpolation both qualitatively (i.e., well-fitting in gray-matter) and quantitatively (i.e., higher z-scores and tSNR). The proposed technique can therefore be considered as a remedy for lack of speed and sensitivity in SSFP-based fMRI, in conventional field strengths. The proposed approach is particularly useful in task-based studies when we concentrate on a ROI considerably smaller than FOV, without sacrificing spatial resolution.

Efficient de-noising of high-resolution fMRI using local and sub-band information.

BACKGROUND: High-resolution fMRI, useful for accurate brain mapping, suffers from low functional sensitivity at a reasonable acquisition time. Conventional smoothing techniques although reduce the noise and boost the sensitivity, but degrade the spatial resolution of fMRI. NEW METHODS: We propose a novel spatial de-noising technique to increase sensitivity while preserving the boundaries of active regions in the high-resolution fMRI. A modified version of PCA that utilizes adjacent voxels information (LPCA) is first suggested for de-noising. This technique is then further empowered by its application to wavelet sub-bands (WLPCA). RESULTS: Proposed techniques were assessed on both simulated and experimental data. Identifiablity index was calculated for evaluation of the denoising on the simulated data. Maximum and mean z-scores along with LAE and SSIM were reported on experimental data for two presented techniques as well as Guassian smoothing. WLPCA outperformed other techniques in Identifiablity index, for simulation, and in preserving maximum z-score, for experimental study. COMPARISON WITH EXISTING METHODS: The presented technique was developed to simultaneously suppress the noise and preserve the boundaries of active areas against leakage. For first aim, its achievable mean z-score was compared to conventional Gaussian. For second aim, its maximum z-score was compared to that of no-smoothing. While Gaussian and no-smoothing can work fine with only one measure, WLPCA was able to improve both measures concurrently. CONCLUSIONS: The local PCA based methods, and in particular WLPCA, is an effective noise reduction step that preserves the spatial resolution by preventing activity leakage of high-resolution fMRI data.

A robust SSFP technique for fMRI at ultra-high field strengths.

A non-balanced (nb) SSFP-based fMRI method based on CE-FAST is presented to alleviate some shortcomings of high spatial-specificity techniques commonly used in high static magnetic fields. The proposed sequence does not suffer from the banding artifacts inherent to balanced (b) SSFP, has low geometrical distortions and SAR compared to spin-echo EPI, and in contrast to previous nbSSFP implementations, is applied at a TR, theoretically prescribed for the optimum contrast. Its non-balanced gradient was chosen to just dephase the unwanted signal component (2π dephasing per TR per voxel). 3D data were acquired from nine healthy subjects, who performed a visual-motor task on a 7 Tesla scanner. For comparison, experiments were accompanied by similar bSSFP and spin-echo acquisitions. Consistent activation was achieved in all subjects with theoretically optimal TR, in contrast to previous nbSSFP techniques. The signal stability as well as relative and absolute functional signal changes, were found to be comparable with bSSFP and spin-echo techniques. The results suggest that with suitable modifications, CE-FAST can be regarded as a robust SSFP-based method for high spatial specificity fMRI techniques.

Computer aided measurement of melanoma depth of invasion in microscopic images.

This paper presents a novel computer aided technique for measurement of melanoma depth of invasion. Melanoma is the deadliest form of skin cancer with worldwide increasing incidences. For a conclusive diagnosis of melanoma, skin biopsies should be examined under a microscope. Visual inspection of microscopic samples is often subjective, time-consuming, cumbersome and prone to human errors. This fact demonstrates the necessity of developing an automated method which assists pathologists in evaluating histopathological samples more accurately in the busy clinical environment. To the best of our knowledge, this is the first time that a computer-assisted diagnosis algorithm has been applied in measurement of melanoma invasion depth. The proposed method uses a clustering algorithm for granular layer extraction and a pre-trained SVM classifier for detection of malignant melanocytes. The experimental results with average error of 3.9µm demonstrate that the proposed method is reliable and effective.

Computer aided measurement of sub-epithelial collagen band in colon biopsies for collagenous colitis diagnosis.

This paper presents a novel computer aided technique for screening of Collagenous Colitis (CC). CC is a type of microscopic colitis mostly characterized by chronic watery diarrhea which is a common feature with a range of other etiologies. Routine paraclinical tests from CC patients such as endoscopic and radiographic studies are usually normal, and diagnosis must be made by biopsy. The gold standard for a confirmative diagnosis of CC is to measure the thickness of the sub-epithelial collagen (SEC) in colon tissue samples. Visual inspection of microscopic samples is often time-consuming, cumbersome and subject to human errors. This fact demonstrates the necessity of developing an automated method which assists pathologists in evaluating histopathological samples more accurately in the busy clinical environment. To the best of our knowledge, this is the first time that a computer-assisted diagnosis algorithm has been applied to CC detection. The proposed method uses a pre-trained Multi-Layer Perceptron neural network to segment SEC band in colon tissue images. We compared a variety of different color and texture descriptors and explore the best set of features for this task. The investigation of the proposed method shows 94.5% specificity and 95.6% sensitivity rate.