Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estima-tion for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmen-tations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL-and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray mat-ter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.Radiolog

White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis

T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006–0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.</p

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling:Acquisition, quantification, and clinical applications

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.</p

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling:Acquisition, quantification, and clinical applications

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.</p

SARS-CoV-2 seroprevalence in healthcare workers of a teaching hospital in a highly endemic region in the Netherlands after the first wave: a cross-sectional study

Objective To study the SARS-CoV-2 infection rate among hospital healthcare workers after the first wave of the COVID-19 pandemic, and provide more knowledge in the understanding of the relationship between infection, symptomatology and source of infection. Design A cross-sectional study in healthcare workers. Setting Northern Limburg, the Netherlands. Participants All employees of VieCuri Medical Center (n=3300) were invited to enrol in current study. In total 2507 healthcare workers participated. Intervention Between 22 June 2020 and 3 July 2020, participants provided venous blood samples voluntarily, which were tested for SARS-CoV-2 antibodies with the Wantai SARS-CoV-2 Ig total ELISA test. Work characteristics, exposure risks and prior symptoms consistent with COVID-19 were gathered through a survey. Main outcome measure Proportion of healthcare workers with positive SARS-CoV-2 serology. Results The overall seroprevalence was 21.1% (n=530/2507). Healthcare workers between 17 and 30 years were more likely to have SARS-CoV-2 antibodies compared with participants >30 years. The probability of having SARS-CoV-2 antibodies was comparable for healthcare workers with and without direct patient (OR 1.42, 95% CI 0.86 to 2.34) and COVID-19 patient contact (OR 1.62, 95% CI 0.80 to 3.33). On the contrary, exposure to COVID-19 positive coworkers (OR 1.83, 95% CI 1.15 to 2.93) and household members (OR 6.09, 95% CI 2.23 to 16.64) was associated with seropositivity. Of those healthcare workers with SARS-CoV-2 antibodies, 16% (n=85/530) had not experienced any prior COVID-19-related symptoms. Only fever and anosmia were associated with seropositivity (OR 1.90, 95% CI 1.42 to 2.55 and OR 10.51, 95% CI 7.86 to 14.07). Conclusions Healthcare workers caring for hospitalised COVID-19 patients were not at an increased risk of infection, most likely as a result of taking standard infection control measures into consideration. These data show that compliance with infection control measures is essential to control secondary transmission and constrain the spread of the virus

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measured (Formula presented.) -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC–AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC–AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE–AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE–AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.ImPhys/Vos groupImPhys/Computational Imagin

Subarachnoid CSF hyperintensities at 7 tesla FLAIR MRI: A novel marker in cerebral amyloid angiopathy

Background: We observed subarachnoid cerebrospinal fluid (CSF) hyperintensities at non-contrast 7-tesla (T) fluid-attenuated inversion recovery (FLAIR) MRI, frequently topographically associated with cortical superficial siderosis (cSS), in participants with cerebral amyloid angiopathy (CAA). To systemically evaluate these CSF hyperintensities we investigated their frequency and anatomical and temporal relationship with cSS on 7T and 3T MRI in hereditary Dutch-type CAA (D-CAA), sporadic CAA (sCAA), and non-CAA controls. Methods: CAA participants were included from two prospective natural history studies and non-CAA controls from a 7T study in healthy females and females with ischemic stroke. CSF hyperintensities were scored by two independent observers. Results: We included 38 sCAA participants (mean age 72y), 50 D-CAA participants (mean age 50y) and 44 non-CAA controls (mean age 53y, 15 with stroke). In total 27/38 (71 %, 95 %CI 56–84) sCAA and 23/50 (46 %, 95 %CI 33–60) D-CAA participants had subarachnoid CSF hyperintensities at baseline 7T. Most (96 %) of those had cSS, in 54 % there was complete topographical overlap with cSS. The remaining 46 % had ≥1 sulcus with CSF hyperintensities without co-localizing cSS. None of the healthy controls and 2/15 (13 %, 95 %CI 2–41, 100 % cSS overlap) of the stroke controls had CSF hyperintensities. In 85 % of the CAA participants CSF hyperintensities could retrospectively be identified at 3T. Of the 35 CAA participants with follow-up 7T after two years, 17/35 (49 %) showed increase and 6/35 (17 %) decrease of regional CSF hyperintensities. In 2/11 (18 %) of participants with follow-up who had baseline CSF hyperintensities without overlapping cSS, new cSS developed at those locations. Conclusions: Subarachnoid CSF hyperintensities at 7T FLAIR MRI occur frequently in CAA and are associated with cSS, although without complete overlap. We hypothesize that the phenomenon could be a sign of subtle plasma protein or blood product leakage into the CSF, resulting in CSF T1-shortening

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.ImPhys/Computational ImagingImPhys/Medical Imagin