Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images.

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.

Geometric Distortion Correction of Renal Diffusion Tensor Imaging Using the Reversed Gradient Method.

ABSTRACT: Renal echo planar diffusion tensor imaging (DTI) has clinical potential but suffers from geometric distortion. We evaluated feasibility of reversed gradient distortion correction in 10 diabetic patients and 6 volunteers. Renal area, apparent diffusion coefficient, fractional anisotropy, and tensor eigenvalues were measured on uncorrected and distortion-corrected DTI. Corrected DTI correlated better than uncorrected DTI (r = 0.904 vs 0.840, P = 0.002) with reference anatomic T2-weighted imaging, with no significant difference in DTI metrics.

Ultra-high-field MRI using composite RF (STEP) pulses.

Ultra-high field MRI offers many opportunities to expand the applications of MRI. In order for this to be realized, the technical problems associated with MRI at field strengths of 7 T and greater need to be solved or mitigated. This paper explores the use of new variations of composite RF pulses, named serial transmit excitation pulses (STEP), in contrast to parallel pulse techniques, in order to remove and/or mitigate the effects of non-uniform B1 excitation fields associated with the subject (eg the human brain). Several techniques based on STEP sequences are introduced and their application to human brain imaging is presented and evaluated.

Review: Using diffusion-weighted magnetic resonance imaging techniques to explore the microstructure and connectivity of subcortical white matter tracts in the human auditory system.

Since its inception 30 years ago, diffusion-weighted magnetic resonance imaging (dMRI) has advanced to become a common component of routine clinical MRI examinations. Diffusion-weighted magnetic resonance offers a way to measure anisotropic diffusion in-vivo, which has led to the development of techniques capable of characterising the orientation of diffusion within living tissue. These modelling techniques can be used to investigate the microstructure and connectivity of white matter tracts within the human brain. Such techniques have been used to study many neural networks within the human body. There is, however, a notable paucity of research utilising dMRI techniques to investigate the white matter tracts of the auditory brainstem. In this review we provide a brief introduction to the basic principles of dMRI analysis and consider some of the difficulties associated with applying dMRI techniques to study the auditory pathways of the brainstem. We also consider aspects of current dMRI methodologies relevant to the auditory brainstem to inform future research in this area.

Does fMRI repetition suppression reveal mirror neuron activity in the human brain? Insights from univariate and multivariate analysis.

Mirror neurons (MN) have been proposed as the neural substrate for a wide range of clinical, social and cognitive phenomena. Over the last decade, a commonly used tool for investigating MN activity in the human brain has been functional magnetic resonance (fMRI) repetition suppression (RS) paradigms. However, the available evidence is mixed, largely owing to inconsistent application of the methodological criteria necessary to infer MN properties. This raises concerns about the degree to which one can infer the presence (or absence) of MN activity from earlier accounts that adopted RS paradigms. We aimed to clarify this issue using a well-validated fMRI RS paradigm and tested for mirror properties by rigorously applying the widely accepted criteria necessary to demonstrate MN activity using traditional univariate techniques and Multivariate Pattern Analysis (MVPA). While univariate whole brain analysis in healthy adults showed uni-modal RS effects within the supplementary motor area, no evidence for cross-modal RS effects consistent with mirror neuron activity was found. MVPA on the other hand revealed a region along the anterior intraparietal sulcus that met the criteria for MN activity. Taken together, these results clarify disparate evidence from earlier RS studies, highlighting that traditional univariate analysis of RS data may not be sensitive for detecting MN activity when rigorously applying the requisite criteria. In light of these findings, we recommend that short of increasing sample sizes substantially, future studies using RS paradigms to investigate MNs across the human brain consider the use of MVPA.

White matter organization in developmental coordination disorder: A pilot study exploring the added value of constrained spherical deconvolution.

Previous studies of white matter organization in sensorimotor tracts in developmental coordination disorder (DCD) have adopted diffusion tensor imaging (DTI), a method unable to reconcile pathways with 'crossing fibres'. In response to limitations of the commonly adopted DTI approach, the present study employed a framework that can reconcile the 'crossing fibre' problem (i.e., constrained spherical deconvolution- CSD) to characterize white matter tissue organization of sensorimotor tracts in young adults with DCD. Participants were 19 healthy adults aged 18-46: 7 met diagnostic criteria for DCD (4 females) and 12 were controls (3 females). All underwent high angular diffusion MRI. After preprocessing, the left and right corticospinal tracts (CST) and superior longitudinal fasciculi (SLF) were delineated and all tracts were then generated using both CSD and DTI tractography respectively. Based on the CSD model, individuals with DCD demonstrated significantly decreased mean apparent fibre density (AFD) in the left SLF relative to controls (with large effect size, Cohen's d = 1.32) and a trend for decreased tract volume of the right SLF (with medium-large effect size, Cohen's d = 0.73). No differences in SLF microstructure were found between groups using DTI, nor were differences in CST microstructure observed across groups regardless of hemisphere or diffusion model. Our data are consistent with the view that motor impairment characteristic of DCD may be subserved by white matter abnormalities in sensorimotor tracts, specifically the left and right SLF. Our data further highlight the benefits of higher order diffusion MRI (e.g. CSD) relative to DTI for clarifying earlier inconsistencies in reports speaking to white matter organization in DCD, and its contribution to poor motor skill in DCD.

Magnetic resonance imaging of the upper airway in patients with quadriplegia and obstructive sleep apnea.

The aim of this study was to investigate upper airway anatomy in quadriplegics with obstructive sleep apnea. Fifty subjects were recruited from three hospitals in Australia: people with quadriplegia due to spinal cord injury and obstructive sleep apnea (n = 11), able-bodied people with obstructive sleep apnea (n = 18), and healthy, able-bodied controls (n = 19). All underwent 3-Tesla magnetic resonance imaging of their upper airway. A subgroup (n = 34) received a topical vasoconstrictor, phenylephrine and post-phenylephrine magnetic resonance imaging. Mixed-model analysis indicated no significant differences in total airway lumen volume between the three groups (P = 0.086). Spinal cord injury-obstructive sleep apnea subjects had a significantly larger volume of soft palate (P = 0.020) and retroglossal lateral pharyngeal walls (P = 0.043) than able-bodied controls. Able-bodied-obstructive sleep apnea subjects had a smaller mandible volume than spinal cord injury-obstructive sleep apnea subjects and able-bodied control subjects (P = 0.036). No differences were seen in airway length between groups when controlling for height (P = 0.055). There was a marginal increase in velopharyngeal volume across groups post-phenylephrine (P = 0.050), and post hoc testing indicated the difference was confined to the able-bodied-obstructive sleep apnea group (P < 0.001). No other upper airway structures showed significant changes with phenylephrine administration. In conclusion, people with obstructive sleep apnea and quadriplegia do not have a structurally smaller airway than able-bodied subjects. They did, however, have greater volumes of soft palate and lateral pharyngeal walls, possibly due to greater neck fat deposition. The acute response to upper airway topical vasoconstriction was not enhanced in those with obstructive sleep apnea and quadriplegia. Changes in upper airway anatomy likely contribute to the high incidence in obstructive sleep apnea in quadriplegic subjects.

7T-fMRI: Faster temporal resolution yields optimal BOLD sensitivity for functional network imaging specifically at high spatial resolution.

Recent developments in accelerated imaging methods allow faster acquisition of high spatial resolution images. This could improve the applications of functional magnetic resonance imaging at 7 Tesla (7T-fMRI), such as neurosurgical planning and Brain Computer Interfaces (BCIs). However, increasing the spatial and temporal resolution will both lead to signal-to-noise ratio (SNR) losses due to decreased net magnetization per voxel and T1-relaxation effect, respectively. This could potentially offset the SNR efficiency gains made with increasing temporal resolution. We investigated the effects of varying spatial and temporal resolution on fMRI sensitivity measures and their implications on fMRI-based BCI simulations. We compared temporal signal-to-noise ratio (tSNR), observed percent signal change (%∆S), volumes of significant activation, Z-scores and decoding performance of linear classifiers commonly used in BCIs across a range of spatial and temporal resolution images acquired during an ankle-tapping task. Our results revealed an average increase of 22% in %∆S (p=0.006) and 9% in decoding performance (p=0.015) with temporal resolution only at the highest spatial resolution of 1.5×1.5×1.5mm3, despite a 29% decrease in tSNR (p<0.001) and plateaued Z-scores. Further, the volume of significant activation was indifferent (p>0.05) across spatial resolution specifically at the highest temporal resolution of 500ms. These results demonstrate that the overall BOLD sensitivity can be increased significantly with temporal resolution, granted an adequately high spatial resolution with minimal physiological noise level. This shows the feasibility of diffuse motor-network imaging at high spatial and temporal resolution with robust BOLD sensitivity with 7T-fMRI. Importantly, we show that this sensitivity improvement could be extended to an fMRI application such as BCIs.

Cerebral quantitative susceptibility mapping predicts amyloid-ß-related cognitive decline.

See Derry and Kent (doi:10.1093/awx167) for a scientific commentary on this article.The large variance in cognitive deterioration in subjects who test positive for amyloid-ß by positron emission tomography indicates that convergent pathologies, such as iron accumulation, might combine with amyloid-ß to accelerate Alzheimer's disease progression. Here, we applied quantitative susceptibility mapping, a relatively new magnetic resonance imaging method sensitive to tissue iron, to assess the relationship between iron, amyloid-ß load, and cognitive decline in 117 subjects who underwent baseline magnetic resonance imaging and amyloid-ß positron emission tomography from the Australian Imaging, Biomarkers and Lifestyle study (AIBL). Cognitive function data were collected every 18 months for up to 6 years from 100 volunteers who were either cognitively normal (n = 64) or diagnosed with mild cognitive impairment (n = 17) or Alzheimer's disease (n = 19). Among participants with amyloid pathology (n = 45), higher hippocampal quantitative susceptibility mapping levels predicted accelerated deterioration in composite cognition tests for episodic memory [ß(standard error) = -0.169 (0.034), P = 9.2 × 10-7], executive function [ß(standard error) = -0.139 (0.048), P = 0.004), and attention [ß(standard error) = -0.074 (0.029), P = 0.012]. Deteriorating performance in a composite of language tests was predicted by higher quantitative susceptibility mapping levels in temporal lobe [ß(standard error) = -0.104 (0.05), P = 0.036] and frontal lobe [ß(standard error) = -0.154 (0.055), P = 0.006]. These findings indicate that brain iron might combine with amyloid-ß to accelerate clinical progression and that quantitative susceptibility mapping could be used in combination with amyloid-ß positron emission tomography to stratify individuals at risk of decline.

Periventricular Nodular Heterotopia: Detection of Abnormal Microanatomic Fiber Structures with Whole-Brain Diffusion MR Imaging Tractography.

Purpose To investigate whether it is possible in patients with periventricular nodular heterotopia (PVNH) to detect abnormal fiber projections that have only previously been reported in the histopathology literature. Materials and Methods Whole-brain diffusion-weighted (DW) imaging data from 14 patients with bilateral PVNH and 14 age- and sex-matched healthy control subjects were prospectively acquired by using 3.0-T magnetic resonance (MR) imaging between August 1, 2008, and December 5, 2012. All participants provided written informed consent. The DW imaging data were processed to generate whole-brain constrained spherical deconvolution (CSD)-based tractography data and super-resolution track-density imaging (TDI) maps. The tractography data were overlaid on coregistered three-dimensional T1-weighted images to visually assess regions of heterotopia. A panel of MR imaging researchers independently assessed each case and indicated numerically (no = 1, yes = 2) as to the presence of abnormal fiber tracks in nodular tissue. The Fleiss κ statistical measure was applied to assess the reader agreement. Results Abnormal fiber tracks emanating from one or more regions of heterotopia were reported by all four readers in all 14 patients with PVNH (Fleiss κ = 1). These abnormal structures were not visible on the tractography data from any of the control subjects and were not discernable on the conventional T1-weighted images of the patients with PVNH. Conclusion Whole-brain CSD-based fiber tractography and super-resolution TDI mapping reveals abnormal fiber projections in nodular tissue suggestive of abnormal organization of white matter (with abnormal fibers both within nodules and projecting to the surrounding white matter) in patients with bilateral PVNH. © RSNA, 2016.

Pooling Morphometric Estimates: A Statistical Equivalence Approach.

Changes in hardware or image-processing settings are a common issue for large multicenter studies. To pool MRI data acquired under these changed conditions, it is necessary to demonstrate that the changes do not affect MRI-based measurements. In these circumstances, classical inference testing is inappropriate because it is designed to detect differences, not prove similarity. We used a method known as statistical equivalence testing to address this limitation. Equivalence testing was carried out on 3 datasets: (1) cortical thickness and automated hippocampal volume estimates obtained from healthy individuals imaged using different multichannel head coils; (2) manual hippocampal volumetry obtained using two readers; and (3) corpus callosum area estimates obtained using an automated method with manual cleanup carried out by two readers. Equivalence testing was carried out using the "two one-sided tests" (TOST) approach. Power analyses of the TOST were used to estimate sample sizes required for well-powered equivalence testing analyses. Mean and standard deviation estimates from the automated hippocampal volume dataset were used to carry out an example power analysis. Cortical thickness values were found to be equivalent over 61% of the cortex when different head coils were used (q < .05, false discovery rate correction). Automated hippocampal volume estimates obtained using the same two coils were statistically equivalent (TOST P = 4.28 × 10(-15) ). Manual hippocampal volume estimates obtained using two readers were not statistically equivalent (TOST P = .97). The use of different readers to carry out limited correction of automated corpus callosum segmentations yielded equivalent area estimates (TOST P = 1.28 × 10(-14) ). Power analysis of simulated and automated hippocampal volume data demonstrated that the equivalence margin affects the number of subjects required for well-powered equivalence tests. We have presented a statistical method for determining if morphometric measures obtained under variable conditions can be pooled. The equivalence testing technique is applicable for analyses in which experimental conditions vary over the course of the study.

A semi-automated "blanket" method for renal segmentation from non-contrast T1-weighted MR images.

OBJECTIVE: To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI. MATERIALS AND METHODS: The user draws approximate kidney contours on every tenth slice, focusing on separating adjacent organs from the kidney. The program then performs a sequence of fully automatic steps: contour filling, interpolation, non-uniformity correction, sampling of representative parenchyma signal, and 3D binary morphology. Three independent observers applied the method to images of 40 kidneys ranging in volume from 94.6 to 254.5 cm(3). Manually constructed reference masks were used to assess accuracy. RESULTS: The volume errors for the three readers were: 4.4% ± 3.0%, 2.9% ± 2.3%, and 3.1% ± 2.7%. The relative discrepancy across readers was 2.5% ± 2.1%. The interactive processing time on average was 1.5 min per kidney. CONCLUSIONS: Pending further validation, the semi-automated method could be applied for monitoring of renal status using non-contrast MRI.

White matter fiber tractography: why we need to move beyond DTI.

OBJECT: Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe. METHODS: Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution-based tractography methodologies were applied to both patients and controls. RESULTS: Diffusion tensor imaging-based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution-based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution-based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex. CONCLUSIONS: This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging-based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.