Initial findings using high-resolution magnetic resonance imaging for visualisation of the sural nerve and surrounding anatomy in healthy volunteers at 7 Tesla.

BACKGROUND AND AIMS: Histopathological diagnosis is the gold standard in many acquired inflammatory, infiltrative and amyloid based peripheral nerve diseases and a sensory nerve biopsy of sural or superficial peroneal nerve is favoured where a biopsy is deemed necessary. The ability to determine nerve pathology by high-resolution imaging techniques resolving anatomy and imaging characteristics might improve diagnosis and obviate the need for biopsy in some. The sural nerve is anatomically variable and occasionally adjacent vessels can be sent for analysis in error. Knowing the exact position and relationships of the nerve prior to surgery could be clinically useful and thus reliably resolving nerve position has some utility. METHODS: 7T images of eight healthy volunteers' (HV) right ankle were acquired in a pilot study using a double-echo in steady-state sequence for high-resolution anatomy images. Magnetic Transfer Ratio images were acquired of the same area. Systematic scoring of the sural, tibial and deep peroneal nerve around the surgical landmark 7 cm from the lateral malleolus was performed (number of fascicles, area in voxels and mm2, diameter and location relative to nearby vessels and muscles). RESULTS: The sural and tibial nerves were visualised in the high-resolution double-echo in steady-state (DESS) image in all HV. The deep peroneal nerve was not always visualised at level of interest. The MTR values were tightly grouped except in the sural nerve where the nerve was not visualised in two HV. The sural nerve location was found to be variable (e.g., lateral or medial to, or crossing behind, or found positioned directly posterior to the saphenous vein). INTERPRETATION: High-resolution high-field images have excellent visualisation of the sural nerve and would give surgeons prior knowledge of the position before surgery. Basic imaging characteristics of the sural nerve can be acquired, but more detailed imaging characteristics are not easily evaluable in the very small sural and further developments and specific studies are required for any diagnostic utility at 7T.

Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method.

PURPOSE: Quantitative susceptibility mapping (QSM) is used increasingly for clinical research where oblique image acquisition is commonplace, but its effects on QSM accuracy are not well understood. THEORY AND METHODS: The QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic field B ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ with respect to the acquired image volume axes. The direction of B ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ is dependent on the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in vivo acquisitions in 5 healthy volunteers, we analyzed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt-correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the RMS error and QSM-tuned structural similarity index. RESULTS: Rotation of wrapped phase images gave severe artifacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment with B ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ . Laplacian boundary value and variable-kernel sophisticated harmonic artifact reduction for phase data background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularization, and weighted linear total variation regularization, all performed most accurately when local field maps were rotated into alignment with B ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ before susceptibility calculation. CONCLUSION: For accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment with B ^ 0 $$ {\hat{\boldsymbol{B}}}_{\mathbf{0}} $$ should be carried out after phase unwrapping and before background-field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.

The functional neuroanatomy of emotion processing in frontotemporal dementias.

Impaired processing of emotional signals is a core feature of frontotemporal dementia syndromes, but the underlying neural mechanisms have proved challenging to characterize and measure. Progress in this field may depend on detecting functional changes in the working brain, and disentangling components of emotion processing that include sensory decoding, emotion categorization and emotional contagion. We addressed this using functional MRI of naturalistic, dynamic facial emotion processing with concurrent indices of autonomic arousal, in a cohort of patients representing all major frontotemporal dementia syndromes relative to healthy age-matched individuals. Seventeen patients with behavioural variant frontotemporal dementia [four female; mean (standard deviation) age 64.8 (6.8) years], 12 with semantic variant primary progressive aphasia [four female; 66.9 (7.0) years], nine with non-fluent variant primary progressive aphasia [five female; 67.4 (8.1) years] and 22 healthy controls [12 female; 68.6 (6.8) years] passively viewed videos of universal facial expressions during functional MRI acquisition, with simultaneous heart rate and pupillometric recordings; emotion identification accuracy was assessed in a post-scan behavioural task. Relative to healthy controls, patient groups showed significant impairments (analysis of variance models, all P < 0.05) of facial emotion identification (all syndromes) and cardiac (all syndromes) and pupillary (non-fluent variant only) reactivity. Group-level functional neuroanatomical changes were assessed using statistical parametric mapping, thresholded at P < 0.05 after correction for multiple comparisons over the whole brain or within pre-specified regions of interest. In response to viewing facial expressions, all participant groups showed comparable activation of primary visual cortex while patient groups showed differential hypo-activation of fusiform and posterior temporo-occipital junctional cortices. Bi-hemispheric, syndrome-specific activations predicting facial emotion identification performance were identified (behavioural variant, anterior insula and caudate; semantic variant, anterior temporal cortex; non-fluent variant, frontal operculum). The semantic and non-fluent variant groups additionally showed complex profiles of central parasympathetic and sympathetic autonomic involvement that overlapped signatures of emotional visual and categorization processing and extended (in the non-fluent group) to brainstem effector pathways. These findings open a window on the functional cerebral mechanisms underpinning complex socio-emotional phenotypes of frontotemporal dementia, with implications for novel physiological biomarker development.

Improving fMRI in signal drop-out regions at 7 T by using tailored radio-frequency pulses: application to the ventral occipito-temporal cortex.

OBJECTIVE: Signal drop-off occurs in echo-planar imaging in inferior brain areas due to field gradients from susceptibility differences between air and tissue. Tailored-RF pulses based on a hyperbolic secant (HS) have been shown to partially recover signal at 3 T, but have not been tested at higher fields. MATERIALS AND METHODS: The aim of this study was to compare the performance of an optimized tailored-RF gradient-echo echo-planar imaging (TRF GRE-EPI) sequence with standard GRE-EPI at 7 T, in a passive viewing of faces or objects fMRI paradigm in healthy subjects. RESULTS: Increased temporal-SNR (tSNR) was observed in the middle and inferior temporal lobes and orbitofrontal cortex of all subjects scanned, but elsewhere tSNR decreased relative to the standard acquisition. In the TRF GRE-EPI, increased functional signal was observed in the fusiform, lateral occipital cortex, and occipital pole, regions known to be part of the visual pathway involved in face-object perception. CONCLUSION: This work highlights the potential of TRF approaches at 7 T. Paired with a reversed-gradient distortion correction to compensate for in-plane susceptibility gradients, it provides an improved acquisition strategy for future neurocognitive studies at ultra-high field imaging in areas suffering from static magnetic field inhomogeneities.

Neural mechanisms underlying song and speech perception can be differentiated using an illusory percept.

The issue of whether human perception of speech and song recruits integrated or dissociated neural systems is contentious. This issue is difficult to address directly since these stimulus classes differ in their physical attributes. We therefore used a compelling illusion (Deutsch et al. 2011) in which acoustically identical auditory stimuli are perceived as either speech or song. Deutsch's illusion was used in a functional MRI experiment to provide a direct, within-subject investigation of the brain regions involved in the perceptual transformation from speech into song, independent of the physical characteristics of the presented stimuli. An overall differential effect resulting from the perception of song compared with that of speech was revealed in right midposterior superior temporal sulcus/right middle temporal gyrus. A left frontotemporal network, previously implicated in higher-level cognitive analyses of music and speech, was found to co-vary with a behavioural measure of the subjective vividness of the illusion, and this effect was driven by the illusory transformation. These findings provide evidence that illusory song perception is instantiated by a network of brain regions that are predominantly shared with the speech perception network.

Designing hyperbolic secant excitation pulses to reduce signal dropout in gradient-echo echo-planar imaging.

PURPOSE: To design hyperbolic secant (HS) excitation pulses to reduce signal dropout in the orbitofrontal and inferior temporal regions in gradient-echo echo-planar imaging (GE-EPI) for functional MRI (fMRI) applications. METHODS: An algorithm based on Bloch simulations optimizes the HS pulse parameters needed to give the desired signal response across the range of susceptibility gradients observed in the human head (approximately ±250 µT·m(-1) ). The impact of the HS pulse on the signal, temporal signal-to-noise ratio, blood oxygen level-dependent (BOLD) sensitivity, and ability to detect resting state BOLD signal changes was assessed in six healthy male volunteers at 3T. RESULTS: The optimized HS pulse (µ = 4.25, ß = 3040 Hz, A0 = 12.3 µT, Δf = 4598 Hz) had a near uniform signal response for through-plane susceptibility gradients in the range ±250 µT·m(-1) . Signal, temporal signal-to-noise ratio, BOLD sensitivity, and the detectability of resting state networks were all partially recovered in the orbitofrontal and inferior temporal regions; however, there were signal losses of up to 50% in regions of homogeneous field (and signal loss from in-plane susceptibility gradients remained). CONCLUSION: The HS pulse reduced signal dropout and could be used to acquire task and resting state fMRI data without loss of spatial coverage or temporal resolution.

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Optimal MRI methods for direct stereotactic targeting of the subthalamic nucleus and globus pallidus.

OBJECTIVE: Reliable identification of the subthalamic nucleus (STN) and globus pallidus interna (GPi) is critical for deep brain stimulation (DBS) of these structures. The purpose of this study was to compare the visibility of the STN and GPi with various MRI techniques and to assess the suitability of each technique for direct stereotactic targeting. METHODS: MR images were acquired from nine volunteers with T2- and proton density-weighted (PD-W) fast spin echo, susceptibility-weighted imaging (SWI), phase-sensitive inversion recovery and quantitative T1, T2 and T2* mapping sequences. Contrast-to-noise ratios (CNR) for the STN and GPi were calculated for all sequences. Targeting errors on SWI were evaluated on magnetic susceptibility maps. The sequences demonstrating the best conspicuity of DBS target structures (SWI and T2*) were then applied to ten patients with movement disorders, and the CNRs for these techniques were assessed. RESULTS: SWI offers the highest CNR for the STN, but standard PD-W images provide the best CNR for the pallidum. Susceptibility maps indicated that the GPi margins may be shifted slightly on SWI, although no shifts were seen for the STN. CONCLUSION: SWI may improve the visibility of the STN on pre-operative MRI, potentially improving the accuracy of direct stereotactic targeting.

Clinical quantitative MRI and the need for metrology.

MRI has been an essential diagnostic tool in healthcare for several decades. It offers unique insights into most tissues without the need for ionising radiation. Historically, MRI has been predominantly used qualitatively, images are formed to allow visual discrimination of tissues types and pathologies, rather than providing quantitative measurements. Increasingly, quantitative MRI (qMRI) is also finding clinical application, where images provide the basis for physical measurements of, e.g. tissue volume measures and represent aspects of tissue composition and microstructure. This article reviews some common current research and clinical applications of qMRI from the perspective of measurement science. qMRI not only offers additional information for radiologists, but also the opportunity for improved harmonisation and calibration between scanners and as such it is well-suited to large-scale investigations such as clinical trials and longitudinal studies. Realising these benefits, however, presents a new kind of technical challenge to MRI practioners. When measuring a parameter quantitatively, it is crucial that the reliability and reproducibility of the technique are well understood. Strictly speaking, a numerical result of a measurement is meaningless unless it is accompanied by a description of the associated measurement uncertainty. It is therefore necessary to produce not just estimates of physical properties in a quantitative image, but also their associated uncertainties. As the process of determining a physical property from the raw MR signal is complicated and multistep, estimation of uncertainty is challenging and there are many aspects of the MRI process that require validation. With the clinical implementation of qMRI techniques and its continued expansion, there is a clear and urgent need for metrology in this field.

Microstructural imaging in temporal lobe epilepsy: Diffusion imaging changes relate to reduced neurite density.

PURPOSE: Previous imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics. METHODS: 20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured. RESULTS: In intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF. CONCLUSIONS: Diffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.

Skeletal muscle MRI differentiates SBMA and ALS and correlates with disease severity.

OBJECTIVE: To investigate the use of muscle MRI for the differential diagnosis and as a disease progression biomarker for 2 major forms of motor neuron disorders: spinal bulbar muscular atrophy (SBMA) and amyotrophic lateral sclerosis (ALS). METHODS: We applied quantitative 3-point Dixon and semiquantitative T1-weighted and short tau inversion recovery (STIR) imaging to bulbar and lower limb muscles and performed clinical and functional assessments in ALS (n = 21) and SBMA (n = 21), alongside healthy controls (n = 16). Acquired images were analyzed for the presence of fat infiltration or edema as well as specific patterns of muscle involvement. Quantitative MRI measurements were correlated with clinical measures of disease severity in ALS and SBMA. RESULTS: Quantitative imaging revealed significant fat infiltration in bulbar (p < 0.001) and limb muscles in SBMA compared to controls (thigh: p < 0.001; calf: p = 0.001), identifying a characteristic pattern of muscle involvement. In ALS, semiquantitative STIR imaging detected marked hyperintensities in lower limb muscles, distinguishing ALS from SBMA and controls. Finally, MRI measurements correlated significantly with clinical scales of disease severity in both ALS and SBMA. CONCLUSIONS: Our findings show that muscle MRI differentiates between SBMA and ALS and correlates with disease severity, supporting its use as a diagnostic tool and biomarker for disease progression. This highlights the clinical utility of muscle MRI in motor neuron disorders and contributes to establish objective outcome measures, which is crucial for the development of new drugs.