Improvement of the thalamocortical white matter network in people with stable treated relapsing-remitting multiple sclerosis over time.

Advanced imaging techniques (tractography) enable the mapping of white matter (WM) pathways and the understanding of brain connectivity patterns. We combined tractography with a network-based approach to examine WM microstructure on a network level in people with relapsing-remitting multiple sclerosis (pw-RRMS) and healthy controls (HCs) over 2 years. Seventy-six pw-RRMS matched with 43 HCs underwent clinical assessments and 3T MRI scans at baseline (BL) and 2-year follow-up (2-YFU). Probabilistic tractography was performed, accounting for the effect of lesions, producing connectomes of 25 million streamlines. Network differences in fibre density across pw-RRMS and HCs at BL and 2-YFU were quantified using network-based statistics (NBS). Longitudinal network differences in fibre density were quantified using NBS in pw-RRMS, and were tested for correlations with disability, cognition and fatigue scores. Widespread network reductions in fibre density were found in pw-RRMS compared with HCs at BL in cortical regions, with more reductions detected at 2-YFU. Pw-RRMS had reduced fibre density at BL in the thalamocortical network compared to 2-YFU. This effect appeared after correction for age, was robust across different thresholds, and did not correlate with lesion volume or disease duration. Pw-RRMS demonstrated a robust and long-distance improvement in the thalamocortical WM network, regardless of age, disease burden, duration or therapy, suggesting a potential locus of neuroplasticity in MS. This network's role over the disease's lifespan and its potential implications in prognosis and treatment warrants further investigation.

Non-contrast based approach for liver function quantification using Bayesian-based intravoxel incoherent motion diffusion weighted imaging: A pilot study.

PURPOSE: Liver cirrhosis disrupts liver function and tissue perfusion, detectable by magnetic resonance imaging (MRI). Assessing liver function at the voxel level with 13-b value intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) could aid in radiation therapy liver-sparing treatment for patients with early impairment. This study aimed to evaluate the feasibility of IVIM-DWI for liver function assessment and correlate it with other multiparametric (mp) MRI methods at the voxel level. METHOD: This study investigates the variability of apparent diffusion coefficient (ADC) derived from 13-b value IVIM-DWI and B1-corrected dual flip angle (DFA) T1 mapping. Experiments were conducted in-vitro with QIBA and NIST phantoms and in 10 healthy volunteers for IVIM-DWI. Additionally, 12 patients underwent an mp-MRI examination. The imaging protocol included a 13-b value IVIM-DWI sequence for generating IVIM parametric maps. B1-corrected DFA T1 pulse sequence was used for generating T1 maps, and Gadoxatate low temporal resolution dynamic contrast-enhanced (LTR-DCE) MRI was used for generating the Hepatic extraction fraction (HEF) map. The Mann-Whitney U test was employed to compare IVIM-DWI parameters (Pure Diffusion, Dslow ; Pseudo diffusion, Dfast ; and Perfusion Fraction, Fp ) between the healthy volunteer and patient groups. Furthermore, in the patient group, statistical correlations were assessed at a voxel level between LTR-DCE MRI-derived HEF, T1 post-Gadoxetate administration, ΔT1%, and various IVIM parameters using Pearson correlation. RESULTS: For-vitro measurements, the maximum coefficient of variation of the ADC and T1 parameters was 12.4% and 16.1%, respectively. The results also showed that Fp and Dfast were able to distinguish between healthy liver function and mild liver function impairment at the global level, with p = 0.002 for Fp and p < 0.001 for Dfast . Within the patient group, these parameters also exhibited a moderate correlation with HEF at the voxel level. CONCLUSION: Overall, the study highlighted the potential of Dfast and Fp for detecting liver function impairment at both global and pixel levels.

The emerging role of artificial intelligence in multiple sclerosis imaging.

BACKGROUND: Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial intelligence (AI) have led to the improvements in the classification, quantification and identification of diagnostic patterns in medical images for a range of diseases, in particular, for MS. Importantly, data generated using AI techniques are analyzed automatically, which compares favourably with labour-intensive and time-consuming manual methods. OBJECTIVE: The aim of this review is to assist MS researchers to understand current and future developments in the AI-based diagnosis and prognosis of MS. METHODS: We will investigate a variety of AI approaches and various classifiers and compare the current state-of-the-art techniques in relation to lesion segmentation/detection and prognosis of disease. After briefly describing the magnetic resonance imaging (MRI) techniques commonly used, we will describe AI techniques used for the detection of lesions and MS prognosis. RESULTS: We then evaluate the clinical maturity of these AI techniques in relation to MS. CONCLUSION: Finally, future research challenges are identified in a bid to encourage further improvements of the methods.

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection.

BACKGROUND: Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. METHODS: Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. RESULTS: The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). CONCLUSION: The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.

PURPOSE: The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp-MRI), comprised of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. MATERIALS AND METHODS: In this work, 191 radiomic features were extracted from mp-MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp-MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave-one-patient-out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. RESULTS: The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. CONCLUSIONS: Combination of noncontrast mp-MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

Diurnal variability of cerebral metabolites in healthy human brain with 2D localized correlation spectroscopy (2D L-COSY).

BACKGROUND: Two-dimensional localized correlation spectroscopy (2D L-COSY) is a research tool that has been applied to evaluate in vivo metabolic activity in many neurological and oncological disorders. Circadian mediators such as brain temperature, hydration, and osmotic regulation have been claimed to change metabolic profiles. PURPOSE: To evaluate the diurnal variability of neuro-metabolites with 2D L-COSY in healthy subjects using a 3 T scanner. STUDY TYPE: Crossover. POPULATION/PHANTOM: Ten healthy subjects and magnetic resonance spectroscopy-high definition (MRS-HD) sphere or "Braino." Field Strength/Sequence: 3 T/2D L-COSY MRS. ASSESSMENT: In vivo 2D L-COSY measurements were performed on ten healthy subjects (5 M/5F, mean age 36.1 ± 7.7 years) repeatedly at three timepoints (0700, 1200, and 1700) on the same day. in vitro evaluations were performed in a similar fashion as in vivo on Braino containing selected brain metabolites at physiological concentrations and pH. 2D L-COSY was acquired from a 27 cm3 voxel located in the posterior cingulate cortex. A total of 75 resonances were included in the analysis and spectral peak volumes were normalized to creatine. STATISTICAL TEST: One-way repeated measured analysis of variance with Bonferroni post-hoc adjustment using SPSS software. RESULTS: In vitro data showed no statistically significant differences between different scans (P > 0.12). in vivo results showed statistically significant diurnal variations (P ≤ 0.05, F > 3.88) for 22 resonances. Bonferroni post-hoc testing showed there was statistically significant increases in metabolite ratios between 0700 and 1700 and these include different moieties of N-acetylaspartate, creatine, choline, myo-inositol, lipids, fucose, glutathione, and homocarnosine. DATA CONCLUSION: 2D L-COSY can detect diurnal physiological variability in neuro-metabolite levels. Thus, time of the day should be considered when planning MRS studies to avoid confounding results. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:592-601.

Supervised risk predictor of central gland lesions in prostate cancer using 1 H MR spectroscopic imaging with gradient offset-independent adiabaticity pulses.

BACKGROUND: Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge. PURPOSE: To evaluate the efficacy of in vivo 3D 1 H MR spectroscopic imaging (3D 1 H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels. STUDY TYPE: Prospective. SUBJECTS: Thirty-six patients with biopsy-proven central gland prostate cancer. FIELD STRENGTH/SEQUENCE: 3T MRI / 3D 1 H MRSI using GOIA-sLASER. ASSESSMENT: Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1 H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing. STATISTICAL TESTS: Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset. RESULTS: High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%. DATA CONCLUSION: The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926-1936.

Reliability of neurometabolite detection with two-dimensional localized correlation spectroscopy at 3T.

BACKGROUND: Two-dimensional localized correlational spectroscopy (2D L-COSY) has been applied in vivo to investigate metabolic profiles in many disorders due to its ability to detect several metabolites simultaneously. Successful application of this technique depends on the reliability of the detection and understanding of the variability result from test-retest measurements. PURPOSE: To evaluate the test-retest repeatability/reliability of 2D L-COSY in detecting brain metabolites in a phantom and healthy subjects in a 3T scanner. STUDY TYPE: Test-retest. POPULATION/PHANTOM: Six healthy subjects and magnetic resonance spectroscopy-high definition (MRS-HD) sphere or "Braino". FIELD STRENGTH/SEQUENCE: 3T/2D L-COSY MRS. ASSESSMENT: Healthy subjects underwent eight weekly experiments over a period of 3 months with an intersession delay of 1 month after the first four measurements. Twenty-nine neurometabolite resonances (8 diagonal, 14 cross, and 7 composite resonances) were studied using a 27 cm3 voxel from the posterior cingulate cortex. In vitro evaluations were performed in a similar manner as in vivo on a Braino phantom containing brain metabolites at physiological concentrations and pH. STATISTICAL TESTS: Intra- and intersubject variability were measured. Test-retest repeatability was calculated using coefficient of variation (CV), and reliability was assessed with standard error measurement (SEM) and intraclass correlation coefficient (ICC), using SPSS software. RESULTS: The intra/inter CV for in vitro and in vivo data ranged from 0.01-0.23%/0.02-0.29% and 0.03-0.23%/0.04-0.39%, respectively. The major diagonal peaks showed ICC ranging from 0.31 to 0.93, while the ICC for cross peaks were 0.09-0.87. The SEM for in vivo data ranged from 0.0016 to 0.08. The interweek interval may have a positive effect on metabolite ratios (P = 0.08; F = 1.78). DATA CONCLUSION: The low variability in metabolite concentration in this study shows a high level of reliability of 2D L-COSY in the human brain. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1559-1569.

Fast magnetic resonance spectroscopic imaging techniques in human brain- applications in multiple sclerosis.

Multi voxel magnetic resonance spectroscopic imaging (MRSI) is an important imaging tool that combines imaging and spectroscopic techniques. MRSI of the human brain has been beneficially applied to different clinical applications in neurology, particularly in neurooncology but also in multiple sclerosis, stroke and epilepsy. However, a major challenge in conventional MRSI is the longer acquisition time required for adequate signal to be collected. Fast MRSI of the brain in vivo is an alternative approach to reduce scanning time and make MRSI more clinically suitable.Fast MRSI can be categorised into spiral, echo-planar, parallel and turbo imaging techniques, each with its own strengths. After a brief introduction on the basics of non-invasive examination (1H-MRS) and localization techniques principles, different fast MRSI techniques will be discussed from their initial development to the recent innovations with particular emphasis on their capacity to record neurochemical changes in the brain in a variety of pathologies.The clinical applications of whole brain fast spectroscopic techniques, can assist in the assessment of neurochemical changes in the human brain and help in understanding the roles they play in disease. To give a good example of the utilities of these techniques in clinical context, MRSI application in multiple sclerosis was chosen. The available up to date and relevant literature is discussed and an outline of future research is presented.

Lipid and Metabolite Deregulation in the Breast Tissue of Women Carrying BRCA1 and BRCA2 Genetic Mutations.

PURPOSE: To use localized correlated spectroscopy (COSY) to assess for an altered biochemical state or states in breast tissue of women with BRCA gene mutations that potentially constitute preinvasive conditions. MATERIALS AND METHODS: Institutional review board approval was obtained. Participants provided written informed consent. In vivo localized COSY images were recorded at 3 T in the breast tissue of women carrying BRCA1 (n = 9) or BRCA2 (n = 14) gene mutations and were compared with images in healthy control subjects with no family history of breast cancer (n = 10). All participants underwent contrast material-enhanced MR imaging and ultrasonography (US). Statistical significance was calculated with the Mann-Whitney two-sided nonparametric test. RESULTS: No abnormality was recorded with MR imaging or US. Metabolite levels in the BRCA1 cohort were reduced by 79% (P = .014) when compared with triglycerides level, and there was a 19% increase in lipid unsaturation and triglyceride levels (P = .027 and P = .086, respectively) when compared with cellular cholesterol level. Cholesterol level was reduced by 47% (P = .027) when compared with diallylic lipid level. Metabolite levels in the BRCA2 cohort showed increased unsaturation of 21% (P = .030) relative to triglycerides level. Comparison of the BRCA1 and BRCA2 cohorts showed a 47% (P = .002) increase in cholesterol level in the BRCA2 cohort when compared with diallylic lipid level and a 52% (P = .003) increase when compared with triglycerides level. Levels of diallylic lipid, unsaturated lipid, triglycerides, and terminal methyl on the acyl chain are reduced by 46% (P = .002), 57% (P = .003), 66% (P = .003), and 29% (P = .010), respectively, when compared with cholesterol level. CONCLUSION: Localized COSY recorded significant changes in women with BRCA1 and BRCA2 gene mutations when compared with control subjects. If these changes are ultimately proven to be a premalignant stage, this method may prove useful in screening.

Six fucose-α(1-2) sugars and α-fucose assigned in the human brain using in vivo two-dimensional MRS.

A growing body of literature has indicated that fucose-α(1-2)-galactose sugars are implicated in the molecular mechanisms that underlie neuronal development, learning and memory in the human brain. An understanding of the in vivo roles played by these terminal fucose residues has been hampered by the lack of technology to non-invasively monitor their levels in the human brain. We have implemented in vivo two-dimensional MRS technology to examine the human brain in a 3-T clinical MR scanner, and report that six fucose-α(1-2)-galactose residues and free α-fucose are available for inspection. Fucose-α(1-3)-galactose residues cannot yet be assigned using this technology as they resonate under the water resonance. This new application offers an unprecedented insight into the molecular mechanisms by which fucosylated sugars contribute to neuronal processes and how they alter during development, ageing and disease.

Diffusion tensor imaging changes of the cortico-thalamic-striatal tracts correlate with fatigue and disability in people with relapsing-remitting MS.

PURPOSE: To investigate how the microstructural neural integrity of cortico-thalamic-striatal (CTS) tracts correlate with fatigue and disability over time. The primary outcome was diffusion tensor imaging (DTI) metrics change over time, and the secondary outcome was correlations with fatigue and disability in people with RRMS (pw-RRMS). METHODS: 76 clinically stable pw-RRMS and 43 matched healthy controls (HCs). The pw-RRMS cohort consisted of three different treatment subgroups. All participants underwent disability, cognitive, fatigue and mental health assessments. Structural and diffusion scans were performed at baseline (BL) and 2-year follow-up (2-YFU) for all participants. Fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD, AD) of normal-appearing white matter (NAWM) and white matter lesion (WML) in nine tracts-of-interests (TOIs) were estimated using our MRtrix3 in-house pipeline. RESULTS: We found significant BL and 2-YFU differences in most diffusion metrics in TOIs in pw-RRMS compared to HCs (pFDR ≤ 0.001; false-detection-rate (FDR)-corrected). There was a significant decrease in WML diffusivities and an increase in FA over the follow-up period in most TOIs (pFDR ≤ 0.001). Additionally, there were no differences in DTI parameters across treatment groups. AD and MD were positively correlated with fatigue scores (r ≤ 0.33, p ≤ 0.01) in NAWM-TOIs, while disability (EDSS) was negatively correlated with FA in most NAWM-TOIs (|r|≤0.31, p ≤ 0.01) at both time points. Disability scores correlated with all diffusivity parameters (r ≤ 0.29, p ≤ 0.01) in most WML-TOIs at both time points. CONCLUSION: Statistically significant changes in diffusion metrics in WML might be indicative of integrity improvement over two years in CTS tracts in clinically stable pw-RRMS. This finding represents structural changes within lesioned tracts. Measuring diffusivity in pw-RRMS affected tracts might be a relevant measure for future remyelination clinical trials.

Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study.

BACKGROUND: Cognitive impairment is a hallmark of multiple sclerosis (MS) but is usually an under-recorded symptom of disease progression. Identifying the predictive signatures of cognitive decline in people with MS (pwMS) over time is important to ensure effective preventative treatment strategies. Structural and functional brain characteristics as measured by various magnetic resonance (MR) methods have been correlated with variation in cognitive function in MS, but typically these studies are limited to a single MR modality and/or are cross-sectional designs. Here we assess the predictive value of multiple different MR modalities in relation to cognitive decline in pwMS over 5 years. METHODS: A cohort of 43 pwMS was assessed at baseline and 5 years follow-up. Baseline (input) data consisted of 70 multi-modal MRI measures for different brain regions including magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and standard volumetrics. Age, sex, disease duration and treatment were included as clinical inputs. Cognitive function was assessed using the Audio Recorded Cognitive Screen (ARCS) and the Symbol Digit Modalities Test (SDMT). Prediction modelling was performed using the machine learning package - GLMnet, where a penalised regression was applied to identify multi-modal signatures with the most predictive value (and the least error) for each outcome. RESULTS: The multi-modal approach to neuroimaging was able to accurately predict cognitive decline in pwMS. The best performing model for change in total ARCS (tARCS) included 16 features from across the various MR modalities and explained 54 % of the variation in change over time (R2=0.54, 95 % CI=0.48-0.51). The features included nine MRS, four volumetric and two DTI parameters. The model also selected disease duration, but not treatment, as a predictive feature. By comparison, the best model for SDMT included several of the same above features and explained 39 % of the change over time (R2=0.39, 95 % CI=0.48-0.51). Conventional volumetric measures were about half as good at predicting change in tARCS score compared to the best multi-modal model (R2=0.26 95 % CI:0.22-0.29). The clinical interpretation of the best predictive model for change in tARCS showed that cognitive decline could be predicted with >90 % accuracy in this cohort (AUC=0.92, SE=0.86 - 0.94). CONCLUSION: Multi-modal MRI signatures can predict cognitive decline in a cohort of pwMS over 5 years with high accuracy. Future studies will benefit from the inclusion of even more MR modalities e.g., functional MRI, quantitative susceptibility mapping, magnetisation transfer imaging, as well as other potential predictors e.g., genetic and environmental factors. With further validation, this signature could be used in future trials with high-risk patients to personalise the management of cognitive decline in pwMS, even in the absence of relapses.

Glutamate and glutamine: a review of in vivo MRS in the human brain.

Our understanding of the roles that the amino acids glutamate (Glu) and glutamine (Gln) play in the mammalian central nervous system has increased rapidly in recent times. Many conditions are known to exhibit a disturbance in Glu-Gln equilibrium, and the exact relationships between these changed conditions and these amino acids are not fully understood. This has led to increased interest in Glu/Gln quantitation in the human brain in an array of conditions (e.g. mental illness, tumor, neuro-degeneration) as well as in normal brain function. Accordingly, this review has been undertaken to describe the increasing number of in vivo techniques available to study Glu and Gln separately, or pooled as 'Glx'. The present MRS methods used to assess Glu and Gln vary in approach, complexity, and outcome, thus the focus of this review is on a description of MRS acquisition approaches, and an indication of relative utility of each technique rather than brain pathologies associated with Glu and/or Gln perturbation. Consequently, this review focuses particularly on (1) one-dimensional (1)H MRS, (2) two-dimensional (1)H MRS, and (3) one-dimensional (13)C MRS techniques.

MR myelin imaging in multiple sclerosis: A scoping review.

The inability of disease-modifying therapies to stop the progression of multiple sclerosis (MS), has led to the development of a new therapeutic strategy focussing on myelin repair. While conventional MRI lacks sensitivity for quantifying myelin damage, advanced MRI techniques are proving effective. The development of targeted therapeutics requires histological validation of myelin imaging results, alongside the crucial task of establishing correlations between myelin imaging results and clinical assessments, so that the effectiveness of therapeutic interventions can be evaluated. The aims of this scoping review were to identify myelin imaging methods - some of which have been histologically validated, and to determine how these approaches correlate with clinical assessments of people with MS (pwMS), thus allowing for effective therapeutic evaluation. A search of two databases was undertaken for publications relating to studies on adults MS using either MRI/MR-histology of the MS brain in the range 1990-to-2022. The myelin imaging methods specified were relaxometry, magnetization transfer, and quantitative susceptibility. Relaxometry was used most frequently, with myelin water fraction (MWF) being the primary metric. Studies conducted on tissue from various regions of the brain showed that MWF was significantly lower in pwMS than in healthy controls. Magnetization transfer ratio indicated that the macromolecular content of lesions was lower than that of normal-appearing tissue. Higher magnetic susceptibility of lesions were indicative of myelin breakdown and iron accumulation. Several myelin imaging metrics were correlated with disability, disease severity and duration. Many studies showed a good correlation between myelin measured histologically and by MR myelin imaging techniques.

Stability of longitudinal DTI metrics in MS with treatment of injectables, fingolimod and dimethyl fumarate.

BACKGROUND AND PURPOSE: Diffusion MRI (dMRI) is sensitive to microstructural changes in white matter of people with relapse-remitting multiple sclerosis (pw-RRMS) that lead to progressive disability. The role of diffusion in assessing the efficacy of different therapies requires more investigation. This study aimed to evaluate selected dMRI metrics in normal-appearing white matter and white matter-lesion in pw-RRMS and healthy controls longitudinally and compare the effect of therapies given. MATERIAL AND METHODS: Structural and dMRI scans were acquired from 78 pw-RRMS (29 injectables, 36 fingolimod, 13 dimethyl fumarate) and 43 HCs at baseline and 2-years follow-up. Changes in dMRI metrics and correlation with clinical parameters were evaluated. RESULTS: Differences were observed in most clinical parameters between pw-RRMS and HCs at both timepoints (p ≤ 0.01). No significant differences in average changes over time were observed for any dMRI metric between treatment groups in either tissue type. Diffusion metrics in NAWM and WML correlated negatively with most cognitive domains, while FA correlated positively at baseline but only for NAWM at follow-up (p ≤ 0.05). FA correlated negatively with disability in NAWM and WML over time, while MD and RD correlated positively only in NAWM. CONCLUSIONS: This is the first DTI study comparing the effect of different treatments on dMRI parameters over time in a stable cohort of pw-RRMS. The results suggest that brain microstructural changes in a stable MS cohort are similar to HCs independent of the therapies used.

CEST 2022 - Differences in APT-weighted signal in T1 weighted isointense lesions, black holes and normal-appearing white matter in people with relapsing-remitting multiple sclerosis.

PURPOSE: To evaluate amide proton transfer weighted (APTw) signal differences between multiple sclerosis (MS) lesions and contralateral normal-appearing white matter (cNAWM). Cellular changes during the demyelination process were also assessed by comparing APTw signal intensity in T1weighted isointense (ISO) and hypointense (black hole -BH) MS lesions in relation to cNAWM. METHODS: Twenty-four people with relapsing-remitting MS (pw-RRMS) on stable therapy were recruited. MRI/APTw acquisitions were undertaken on a 3 T MRI scanner. The pre and post-processing, analysis, co-registration with structural MRI maps, and identification of regions of interest (ROIs) were all performed with Olea Sphere 3.0 software. Generalized linear model (GLM) univariate ANOVA was undertaken to test the hypotheses that differences in mean APTw were entered as dependent variables. ROIs were entered as random effect variables, which allowed all data to be included. Regions (lesions and cNAWM) and/or structure (ISO and BH) were the main factor variables. The models also included age, sex, disease duration, EDSS, and ROI volumes as covariates. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performance of these comparisons. RESULTS: A total of 502 MS lesions manually identified on T2-FLAIR from twenty-four pw-RRMS were subcategorized as 359 ISO and 143 BH with reference to the T1-MPRAGE cerebral cortex signal. Also, 490 ROIs of cNAWM were manually delineated to match the MS lesion positions. A two-tailed t-test showed that mean APTw values were higher in females than in males (t = 3.52, p < 0.001). Additionally, the mean APTw values of MS lesions were higher than those of cNAWM after accounting for covariates (mean lesion = 0.44, mean cNAWM = 0.13, F = 44.12, p < 0.001).The mean APTw values of ISO lesions were higher than those of cNAWM after accounting for covariates (mean ISO lesions = 0.42, mean cNAWM = 0.21, F = 12.12, p < 0.001). The mean APTw values of BH were also higher than those of cNAWM (mean BH lesions = 0.47, mean cNAWM = 0.033, F = 40.3, p < 0.001). The effect size (i.e., difference between lesion and cNAWM) for BH was found to be higher than for ISO (14 vs. 2). Diagnostic performance showed that APT was able to discriminate between all lesions and cNAWM with an accuracy of >75% (AUC = 0.79, SE = 0.014). Discrimination between ISO lesions and cNAWM was accomplished with an accuracy of >69% (AUC = 0.74, SE = 0.018), while discrimination between BH lesions and cNAWM was achieved at an accuracy of >80% (AUC = 0.87, SE = 0.021). CONCLUSIONS: Our results highlight the potential of APTw imaging for use as a non-invasive technique that is able to provide essential molecular information to clinicians and researchers so that the stages of inflammation and degeneration in MS lesions can be better characterized.

Determination of hepatic extraction fraction with gadoxetate low-temporal resolution DCE-MRI-based deconvolution analysis: validation with ALBI score and Child-Pugh class.

INTRODUCTION: In this study, we aimed to investigate the feasibility of gadoxetate low-temporal resolution (LTR) DCE-MRI for voxel-based hepatic extraction fraction (HEF) quantification for liver sparing radiotherapy using a deconvolution analysis (DA) method. METHODS: The accuracy and consistency of the deconvolution implementation in estimating liver function was first assessed using simulation data. Then, the method was applied to DCE-MRI data collected retrospectively from 64 patients (25 normal liver function and 39 cirrhotic patients) to generate HEF maps. The normal liver function patient data were used to measure the variability of liver function quantification. Next, a correlation between HEF and ALBI score (a new model for assessing the severity of liver dysfunction) was assessed using Pearson's correlation. Differences in HEF between Child-Pugh score classifications were assessed for significance using the Kruskal-Wallis test for all patient groups and Mann-Whitney U-test for inter-groups. A statistical significance was considered at a P-value <0.05 in all tests. RESULTS: The results showed that the implemented method accurately reproduced simulated liver function; root-mean-square error between estimated and simulated liver response functions was 0.003, and the coefficient-of-variance of HEF was <20%. HEF correlation with ALBI score was r = -0.517, P < 0.0001, and HEF was significantly decreased in the cirrhotic patients compared to normal patients (P < 0.0001). Also, HEF in Child-Pugh B/C was significantly lower than in Child-Pugh A (P = 0.024). CONCLUSION: The study demonstrated the feasibility of gadoxetate LTR-DCE MRI for voxel-based liver function quantification using DA. HEF could distinguish between different grades of liver function impairment and could potentially be used for functional guidance in radiotherapy.

Fast WASABI post-processing: Access to rapid B0 and B1 correction in clinical routine for CEST MRI.

CEST MRI methods, such as APT and NOE imaging reveal biomarkers with significant diagnostic potential due to their ability to access molecular tissue information. Regardless of the technique used, CEST MRI data are affected by static magnetic B0 and radiofrequency B1 field inhomogeneities that degrade their contrast. For this reason, the correction of B0 field-induced artefacts is essential, whereas accounting for B1 field inhomogeneities have shown significant improvements in image readability. In a previous work, an MRI protocol called WASABI was presented, which can map simultaneously B0 and B1 field inhomogeneities, while maintaining the same sequence and readout types as used for CEST MRI. Despite the highly satisfactory quality of B0 and B1 maps computed from the WASABI data, the post-processing method is based on an exhaustive search of a four-parameter space and an additional four-parameter non-linear model fitting step. This leads to long post-processing times that are prohibitive in clinical practice. This work provides a new method for fast post-processing of WASABI data with outstanding acceleration of the parameter estimation procedure and without compromising its stability. The resulting computational acceleration makes the WASABI technique suitable for clinical use. The stability of the method is demonstrated on phantom data and clinical 3 Tesla in vivo data.

Quantified hemodynamic parameters of the venous system in multiple sclerosis: A systematic review.

BACKGROUND: Multiple Sclerosis (MS) is a complex neurodegenerative condition that is influenced by a combination of genetic and environmental factors. Included in these factors is the venous system, however, the extent to which it influences the etiology of MS has yet to be fully characterised. The aim of this review is to critically summarize the literature available concerning the venous system in MS, primarily concerning specific data on the venous pressure and blood flow in this system. METHODS: A systematic review was conducted with the application of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The advanced search functions of both the Scopus and PubMed databases were used to conduct the literature search, resulting in 136 unique articles initially identified. Applying relevant exclusion criteria, 22 of the studies were chosen for this review. RESULTS: The selected studies were analysed for venous pressure and blood flow related findings, with 14 studies contributing data on the internal jugular vein (IJV) flow rate, 5 on blood flows of the intracranial venous sinuses, 2 on blood flow pulsatility and 6 supplying information relevant to the venous pressure (3 studies contributed to multiple areas). The general findings of the review included that the IJV flow was not significantly different between MS patients and controls, however, there were variances between stenotic (S) and non-stenotic (NS) MS patients. Due to the limited data in the other two areas defined in this review, further research is required to establish if any variances in MS are present. CONCLUSION: It remains unclear if there are significant differences in many flow variables between MS patients and controls considered in this review. It would be advantageous if future work in this area focused on understanding the hemodynamics of this system, primarily concerning how the flow rate, venous pressure and vascular resistance are related, and any impact that these factors have on the etiology of MS.