Echo-planar imaging (EPI) generates considerable acoustic noise by rapidly oscillating gradients. In functional magnetic resonance imaging (FMRI), unshielded EPI sounds activate the auditory system inasmuch as it is responsive. Instead of attenuating EPI noise, our goal was to utilize it for auditory FMRI by omitting read-outs from the pulse sequence's gradient train. Read-out gradient pulses are the primary noise determinant of EPI introducing its peak sound level and fundamental frequency peak which inversely relates to twice the echo spacing. Using model-driven analyses, we demonstrate that withholding read-outs from EPI is suited to reliably evoke hemodynamic blood oxygenation level-dependent (BOLD) signal modulations bilaterally in the auditory cortex of normal hearing subjects (n=60). To investigate the utility of EPI read-out omissions for auditory FMRI at an individual subject's level, we compare traditional Family-Wise-Error-Rate (FWER)-corrected maximum height thresholding to spatial mixture modeling (SMM). With the latter, appropriate bilateral auditory activations were confirmed in 95% of the individuals, whereas FWER-based voxel thresholding detected such activations in up to 72%. We illustrate the applicability of this novel EPI modification for clinical diagnostic purposes and report on a patient with bilateral large vestibular aqueducts (LVAs) and severe binaural sensorineural hearing loss (SNHL). In this particular case, read-out omissions from EPI were used to assert residual audition prior to cochlear implantation (CI). Requiring no specific task compliance or sophisticated stimulation equipment other than the scanner on its own, FMRI by read-out omissions lends itself to auditory investigations and to quickly probe audition.

To develop an in-vivo imaging method for the measurement of dorsal-root-ganglia-(DRG) perfusion, to establish its normal values in patients without known peripheral nerve disorders or radicular pain syndromes and to determine the physiological spatial perfusion pattern within the DRG.

Functional disturbances within cortico-striatal control systems have been implicated in the psychobiology (i.e. impaired cognitive-behavioral flexibility, perfectionist personality) of anorexia nervosa. The aim of the present study was to investigate the morphometry of brain regions within cortico-striatal networks in acute anorexia nervosa (AN) as well as long-term weight-restored anorexia nervosa (AN-WR) patients. A total of 39 participants: 12 AN, 13 AN-WR patients, and 14 healthy controls (HC) underwent high-resolution, T1-weighted magnetic resonance imaging (MRI), a cognitive-behavioral flexibility task, and a psychometric assessment. Group differences in local grey matter volume (GMV) were analyzed using whole brain voxel-based morphometry (VBM) and brain-atlas based automatic volumetry computation (IBASPM). Individual differences in total GMV were considered as a covariate in all analyses. In the regional brain morphometry, AN patients, as compared to HC, showed decreased GMVs (VBM and volumetry) in the anterior cingulate cortex (ACC), the supplementary motor area (SMA), and in subcortical regions (amygdala, putamen: VBM only). AN-WR compared to HC showed decreased GMV (VBM and volumetry) in the ACC and SMA, whereas GMV of the subcortical region showed no differences. The findings of the study suggest that structural abnormalities of the ACC and SMA were independent of the disease stage, whereas subcortical limbic-striatal changes were state dependent.

Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N=53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N=135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials.

Accurate segmentation of cerebral venous vasculature from gradient echo data is of central importance in several areas of neuroimaging such as for the susceptibility-based assessment of brain oxygenation or planning of electrode placement in deep brain stimulation. In this study, a vein segmentation algorithm for single- and multi-echo gradient echo data is proposed. First, susceptibility maps, true susceptibility-weighted images, and, in the multi-echo case, R2* maps were generated from the gradient echo data. These maps were filtered with an inverted Hamming filter to suppress background contrast as well as artifacts from field inhomogeneities at the brain boundaries. A shearlet-based scale-wise representation was generated to calculate a vesselness function and to generate segmentations based on local thresholding. The accuracy of the proposed algorithm was evaluated for different echo times and image resolutions using a manually generated reference segmentation and two vein segmentation algorithms (Frangi vesselness-based, recursive vesselness filter) as a reference with the Dice and Cohen's coefficients as well as the modified Hausdorff distance. The Frangi-based and recursive vesselness filter methods were significantly outperformed with regard to all error metrics. Applying the algorithm, susceptibility differences likely related to differences in blood oxygenation between superficial and deep venous territories could be demonstrated.