Optimised tissue clearing minimises distortion and destruction during tissue delipidation.

AIMS: A variety of tissue clearing techniques have been developed to render intact tissue transparent. For thicker samples, additional partial tissue delipidation is required before immersion into the final refractive index (RI)-matching solution, which alone is often inadequate to achieve full tissue transparency. However, it is difficult to determine a sufficient degree of tissue delipidation, excess of which can result in tissue distortion and protein loss. Here, we aim to develop a clearing strategy that allows better monitoring and more precise determination of delipidation progress. METHODS: We combined the detergent sodium dodecyl sulphate (SDS) with OPTIClear, a RI-matching solution, to form a strategy termed Accurate delipidation with Optimal Clearing (Accu-OptiClearing). Accu-OptiClearing allows for a better preview of the final tissue transparency achieved when immersed in OPTIClear alone just before imaging. We assessed for the changes in clearing rate, protein loss, degree of tissue distortion, and preservation of antigens. RESULTS: Partial delipidation using Accu-OptiClearing accelerated tissue clearing and better preserved tissue structure and antigens than delipidation with SDS alone. Despite achieving similar transparency in the final OPTIClear solution, more lipids were retained in samples cleared with Accu-OptiClearing compared to SDS. CONCLUSIONS: Combining the RI-matching solution OPTIClear with detergents, Accu-OptiClearing, can avoid excessive delipidation, leading to accelerated tissue clearing, less tissue damage and better preserved antigens.

Relationship between intravoxel incoherent motion diffusion-weighted MRI and dynamic contrast-enhanced MRI in tissue perfusion of cervical cancers.

PURPOSE: To investigate the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) and dynamic contrast-enhanced MRI (DCE-MRI) in cervical cancer perfusion. MATERIALS AND METHODS: Prospective newly diagnosed cervical cancer patients underwent diffusion-weighted MRI (13 b-values: 1-1000 s/mm(2) ) and DCE-MRI. The IVIM perfusion parameters, perfusion fraction (f), pseudodiffusion coefficient (D*), and flow-related parameter (fD*), were derived from a biexponential decay model. DCE-MRI was analyzed with a pharmacokinetic model and signal-time curve to derive the amplitude factor (A), estimated volume transfer constant between blood plasma, and the extravascular extracellular space (est K(trans) ), maximum relative enhancement (MaxRE), and area under the signal-time curve (AUC). Spearman's rank correlation coefficient (r) evaluated the correlative relationships. RESULTS: The f = 13.51% ± 1.76%, D* = 71.72 ± 7.55 × 10(-3) mm(2) /s, fD* = 9.64 ± 1.28 × 10(-3) mm(2) /s, A = 1.41 ± 0.43, est K(trans) = 0.19 ± 0.06 s(-1) , MaxRE of 120.02 ± 21.07%, and AUC 212,393 ± 54,423 was found in 25 cervical cancer patients. Statistically significant positive correlations were found between fD* and est K(trans) (r = 0.42, P = 0.038), fD* and A (r = 0.50, P = 0.011), fD* and MaxRE (r = 0.52, P = 0.008), f and AUC (r = 0.58, P = 0.003). CONCLUSION: The IVIM perfusion parameters showed moderate to good correlations with quantitative and semiquantitative perfusion parameters derived from DCE-MRI in cervical cancer.

Cortical-striatal network functional connectivity markers in poststroke fatigue: a single-centre fMRI case-control study protocol.

INTRODUCTION: Structural and functional abnormalities in the cortical-striatal network (CSN) are hypothesised to play a key role in the pathogenesis of neurological disease-associated fatigue. Some small-scale functional MRI (fMRI) studies have suggested that poststroke fatigue (PSF) is related to focal functional connectivity (FC) changes. To date, there has been no published large-scale fMRI study on PSF. This planned study will examine the role of the CSN FC on PSF. METHODS AND ANALYSIS: The planned study will be a prospective cohort study conducted at the Neurology Unit of the Prince of Wales Hospital. We will recruit 738 participants. The project duration will be 36 months. A psychiatrist will administer the Fatigue Severity Scale (FSS) at 3 months (P1) following the index stroke. PSF is defined as an FSS Score≥4.0. PSF severity will be defined by the FSS total score at P1. Participants with PSF at P1 will undergo two follow-up assessments at 9 (P2) and 15 (P3) months post stroke. PSF remission at P2 or P3 will be defined as a 50% reduction in FSS. Participants will undergo MRI examinations within 2 weeks of the 3-month poststroke assessment. Structural MRI, resting-state fMRI and diffusion tensor imaging will be performed. FC, structural connectivity, infarcts, cerebral microbleeds and white matter hyperintensities will be analysed. For the primary analysis, the effect of PSF on the FC, structural connectivity and diffusion metrics of CSN of stroke survivors, voxel-wise two-sample t-tests will be performed with FDR correction for multiple comparison and significance level set at p<0.05. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Joint Chinese University of Hong Kong-New Territories East Cluster clinical research ethics committee. The study findings will be shared through peer-reviewed journal publications, national and international conferences and social media platforms.

Reconstruction of multi-phase parametric maps in 4D-magnetic resonance fingerprinting (4D-MRF) by optimization of local T1 and T2 sensitivities.

BACKGROUND: Time-resolved magnetic resonance fingerprinting (MRF), or 4D-MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D-MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues' properties of each phase. PURPOSE: To develop a technique for the reconstruction of multi-phase parametric maps in four-dimensional magnetic resonance fingerprinting (4D-MRF) through the optimization of local T1 and T2 sensitivities. METHODS: The proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter-phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi-phase signal evolution curves were modified by sliding window reconstruction and inter-phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi-parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac-torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner. RESULTS: In simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight-phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in-vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively. CONCLUSIONS: Both simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi-phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.

A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).

PURPOSE: This study developed a data-driven optimization to improve the accuracy of deep learning QSM quantification. METHODS: The proposed deep learning QSM pipeline consisted of two projections onto convex set (POCS) models designed to decouple trainable network components with the spherical mean value (SMV) filters and dipole kernel in the data-driven optimization. They were a background field removal network (named POCSnet1) and a dipole inversion network (named POCSnet2). Both POCSnet1 and POCSnet2 were the unrolled V-Net with iterative data-driven optimization to enforce the data fidelity. For training POCSnet1, we simulated phantom data with random geometric shapes as the background susceptibility sources. For training POCSnet2, we used geometric shapes to mimic the QSM. The evaluation was performed on synthetic data, a public COSMOS (N = 1), and clinical data from a Parkinson's disease cohort (N = 71) and small-vessel disease cohort (N = 26). For comparison, DLL2, FINE, and autoQSM, were implemented and tested under the same experimental setting. RESULTS: On COSMOS, results from POCSnet1 were more similar to that of the V-SHARP method with NRMSE = 23.7% and SSIM = 0.995, compared with the NRMSE = 62.7% and SSIM = 0.975 for SHARQnet, a naïve V-Net model. On COSMOS, the NRMSE and HFEN for POCSnet2 were 58.1% and 56.7%; while for DLL2, FINE, and autoQSM, they were 62.0% and 61.2%, 69.8% and 67.5%, and 87.5% and 85.3%, respectively. On the Parkinson's disease cohort, our results were consistent with those obtained from VSHARP+STAR-QSM with biases <3% and outperformed the SHARQnet+DeepQSM that had biases of 7% to 10%. The sensitivity of cerebral microbleed detection using our pipeline was 100%, compared with 92% by SHARQnet+DeepQSM. CONCLUSION: Data-driven optimization improved the accuracy of QSM quantification compared with that of naïve V-Net models.

Brain Microstructural Changes Associated With Neurocognitive Outcome in Intracranial Germ Cell Tumor Survivors.

BACKGROUND: Childhood intracranial germ cell tumor (GCT) survivors are prone to radiotherapy-related neurotoxicity, which can lead to neurocognitive dysfunctions. Diffusion kurtosis imaging (DKI) is a diffusion MRI technique that is sensitive to brain microstructural changes. This study aimed to investigate the association between DKI metrics versus cognitive and functional outcomes of childhood intracranial GCT survivors. METHODS: DKI was performed on childhood intracranial GCT survivors (n = 20) who had received cranial radiotherapy, and age and gender-matched healthy control subjects (n = 14). Neurocognitive assessment was performed using the Hong Kong Wechsler Intelligence Scales, and functional assessment was performed using the Lansky/Karnofsky performance scales (KPS). Survivors and healthy controls were compared using mixed effects model. Multiple regression analyses were performed to determine the effects of microstructural brain changes of the whole brain as well as the association between IQ and Karnofsky scores and the thereof. RESULTS: The mean Intelligence Quotient (IQ) of GCT survivors was 91.7 (95% CI 84.5 - 98.8), which was below the age-specific normative expected mean IQ (P = 0.013). The mean KPS score of GCT survivors was 85.5, which was significantly lower than that of controls (P < 0.001). Cognitive impairments were significantly associated with the presence of microstructural changes in white and grey matter, whereas functional impairments were mostly associated with microstructural changes in white matter. There were significant correlations between IQ versus the mean diffusivity (MD) and mean kurtosis (MK) of specific white matter regions. The IQ scores were negatively correlated with the MD of extensive grey matter regions. CONCLUSION: Our study identified vulnerable brain regions whose microstructural changes in white and grey matter were significantly associated with impaired cognitive and physical functioning in survivors of pediatric intracranial GCT.