Cerebellar, Not Nigrostriatal Degeneration Impairs Dexterity in Multiple System Atrophy.

BACKGROUND: Multiple system atrophy (MSA) clinically manifests with either predominant nigrostriatal or cerebellopontine degeneration. This corresponds to two different phenotypes, one with predominant Parkinson's symptoms (MSA-P [multiple system atrophy-parkinsonian subtype]) and one with predominant cerebellar deficits (MSA-C [multiple system atrophy-cerebellar subtype]). Both nigrostriatal and cerebellar degeneration can lead to impaired dexterity, which is a frequent cause of disability in MSA. OBJECTIVE: The aim was to disentangle the contribution of nigrostriatal and cerebellar degeneration to impaired dexterity in both subtypes of MSA. METHODS: We thus investigated nigrostriatal and cerebellopontine integrity using diffusion microstructure imaging in 47 patients with MSA-P and 17 patients with MSA-C compared to 31 healthy controls (HC). Dexterity was assessed using the 9-Hole Peg Board (9HPB) performance. RESULTS: Nigrostriatal degeneration, represented by the loss of cells and neurites, leading to a larger free-fluid compartment, was present in MSA-P and MSA-C when compared to HCs. Whereas no intergroup differences were observed between the MSAs in the substantia nigra, MSA-P showed more pronounced putaminal degeneration than MSA-C. In contrast, a cerebellopontine axonal degeneration was observed in MSA-P and MSA-C, with stronger effects in MSA-C. Interestingly, the degeneration of cerebellopontine fibers is associated with impaired dexterity in both subtypes, whereas no association was observed with nigrostriatal degeneration. CONCLUSION: Cerebellar dysfunction contributes to impaired dexterity not only in MSA-C but also in MSA-P and may be a promising biomarker for disease staging. In contrast, no significant association was observed with nigrostriatal dysfunction. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset.

PURPOSE: In cases of acute intracerebral hemorrhage (ICH) volume estimation is of prognostic and therapeutic value following minimally invasive surgery (MIS). The ABC/2 method is widely used, but suffers from inaccuracies and is time consuming. Supervised machine learning using convolutional neural networks (CNN), trained on large datasets, is suitable for segmentation tasks in medical imaging. Our objective was to develop a CNN based machine learning model for the segmentation of ICH and of the drain and volumetry of ICH following MIS of acute supratentorial ICH on a relatively small dataset. METHODS: Ninety two scans were assigned to training (n = 29 scans), validation (n = 4 scans) and testing (n = 59 scans) datasets. The mean age (SD) was 70 (± 13.56) years. Male patients were 36. A hierarchical, patch-based CNN for segmentation of ICH and drain was trained. Volume of ICH was calculated from the segmentation mask. RESULTS: The best performing model achieved a Dice similarity coefficient of 0.86 and 0.91 for the ICH and drain respectively. Automated ICH volumetry yielded high agreement with ground truth (Intraclass correlation coefficient = 0.94 [95% CI: 0.91, 0.97]). Average difference in the ICH volume was 1.33 mL. CONCLUSION: Using a relatively small dataset, originating from different CT-scanners and with heterogeneous voxel dimensions, we applied a patch-based CNN framework and successfully developed a machine learning model, which accurately segments the intracerebral hemorrhage (ICH) and the drains. This provides automated and accurate volumetry of the bleeding in acute ICH treated with minimally invasive surgery.

Impact of temporal resolution on perfusion metrics, therapy decision, and radiation dose reduction in brain CT perfusion in patients with suspected stroke.

PURPOSE: CT perfusion of the brain is a powerful tool in stroke imaging, though the radiation dose is rather high. Several strategies for dose reduction have been proposed, including increasing the intervals between the dynamic scans. We determined the impact of temporal resolution on perfusion metrics, therapy decision, and radiation dose reduction in brain CT perfusion from a large dataset of patients with suspected stroke. METHODS: We retrospectively included 3555 perfusion scans from our clinical routine dataset. All cases were processed using the perfusion software VEOcore with a standard sampling of 1.5 s, as well as simulated reduced temporal resolution of 3.0, 4.5, and 6.0 s by leaving out respective time points. The resulting perfusion maps and calculated volumes of infarct core and mismatch were compared quantitatively. Finally, hypothetical decisions for mechanical thrombectomy following the DEFUSE-3 criteria were compared. RESULTS: The agreement between calculated volumes for core (ICC = 0.99, 0.99, and 0.98) and hypoperfusion (ICC = 0.99, 0.99, and 0.97) was excellent for all temporal sampling schemes. Of the 1226 cases with vascular occlusion, 14 (1%) for 3.0 s sampling, 23 (2%) for 4.5 s sampling, and 63 (5%) for 6.0 s sampling would have been treated differently if the DEFUSE-3 criteria had been applied. Reduction of temporal resolution to 3.0 s, 4.5 s, and 6.0 s reduced the radiation dose by a factor of 2, 3, or 4. CONCLUSION: Reducing the temporal sampling of brain perfusion CT has only a minor impact on image quality and treatment decision, but significantly reduces the radiation dose to that of standard non-contrast CT.

Application of diffusion microstructure imaging in musculoskeletal radiology - translation from head to shoulders.

OBJECTIVES: Quantitative MRI techniques, such as diffusion microstructure imaging (DMI), are increasingly applied for advanced tissue characterization. We determined its value in rotator cuff (RC) muscle imaging by studying the association of DMI parameters to isometric strength and fat fraction (FF). METHODS: Healthy individuals prospectively underwent 3T-MRI of the shoulder using DMI and chemical shift encoding-based water-fat imaging. RC muscles were segmented and quantitative MRI metrics (V-ISO, free fluid; V-intra, compartment inside of muscle fibers; V-extra, compartment outside of muscle fibers, and FF) were extracted. Isometric shoulder strength was quantified using specific clinical tests. Sex-related differences were assessed with Student's t. Association of DMI-metrics, FF, and strength was tested. A factorial two-way ANOVA was performed to compare the main effects of sex and external/internal strength-ratio and their interaction effects on quantitative imaging parameters ratios of infraspinatus/subscapularis. RESULTS: Among 22 participants (mean age: 26.7 ± 3.1 years, 50% female, mean BMI: 22.6 ± 1.9 kg/m2), FF of the individual RC muscles did not correlate with strength or DMI parameters (all p > 0.05). Subjects with higher V-intra (r = 0.57 to 0.87, p < 0.01) and lower V-ISO (r = -0.6 to -0.88, p < 0.01) had higher internal and external rotation strength. Moreover, V-intra was higher and V-ISO was lower in all RC muscles in males compared to female subjects (all p < 0.01). There was a sex-independent association of external/internal strength-ratio with the ratio of V-extra of infraspinatus/subscapularis (p = 0.02). CONCLUSIONS: Quantitative DMI parameters may provide incremental information about muscular function and microstructure in young athletes and may serve as a potential biomarker. KEY POINTS: • Diffusion microstructure imaging was successfully applied to non-invasively assess the microstructure of rotator cuff muscles in healthy volunteers. • Sex-related differences in the microstructural composition of the rotator cuff were observed. • Muscular microstructural metrics correlated with rotator cuff strength and may serve as an imaging biomarker of muscular integrity and function.

Widespread white matter oedema in subacute COVID-19 patients with neurological symptoms.

While neuropathological examinations in patients who died from COVID-19 revealed inflammatory changes in cerebral white matter, cerebral MRI frequently fails to detect abnormalities even in the presence of neurological symptoms. Application of multi-compartment diffusion microstructure imaging (DMI), that detects even small volume shifts between the compartments (intra-axonal, extra-axonal and free water/CSF) of a white matter model, is a promising approach to overcome this discrepancy. In this monocentric prospective study, a cohort of 20 COVID-19 inpatients (57.3 ± 17.1 years) with neurological symptoms (e.g. delirium, cranial nerve palsies) and cognitive impairments measured by the Montreal Cognitive Assessment (MoCA test; 22.4 ± 4.9; 70% below the cut-off value <26/30 points) underwent DMI in the subacute stage of the disease (29.3 ± 14.8 days after positive PCR). A comparison of whole-brain white matter DMI parameters with a matched healthy control group (n = 35) revealed a volume shift from the intra- and extra-axonal space into the free water fraction (V-CSF). This widespread COVID-related V-CSF increase affected the entire supratentorial white matter with maxima in frontal and parietal regions. Streamline-wise comparisons between COVID-19 patients and controls further revealed a network of most affected white matter fibres connecting widespread cortical regions in all cerebral lobes. The magnitude of these white matter changes (V-CSF) was associated with cognitive impairment measured by the MoCA test (r = -0.64, P = 0.006) but not with olfactory performance (r = 0.29, P = 0.12). Furthermore, a non-significant trend for an association between V-CSF and interleukin-6 emerged (r = 0.48, P = 0.068), a prominent marker of the COVID-19 related inflammatory response. In 14/20 patients who also received cerebral 18F-FDG PET, V-CSF increase was associated with the expression of the previously defined COVID-19-related metabolic spatial covariance pattern (r = 0.57; P = 0.039). In addition, the frontoparietal-dominant pattern of neocortical glucose hypometabolism matched well to the frontal and parietal focus of V-CSF increase. In summary, DMI in subacute COVID-19 patients revealed widespread volume shifts compatible with vasogenic oedema, affecting various supratentorial white matter tracts. These changes were associated with cognitive impairment and COVID-19 related changes in 18F-FDG PET imaging.

Diffusion microstructure imaging in progressive supranuclear palsy: reduced axonal volumes in the superior cerebellar peduncles, dentato-rubro-thalamic tracts, ventromedial thalami, and frontomesial white matter.

Differentiating between Parkinson's disease (PD) and atypical Parkinson syndromes such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal degeneration is challenging. Diffusion microstructure imaging (DMI) was analyzed in patients with clinically suspected atypical Parkinson syndromes and healthy controls. In an exploration cohort, the spatial distribution of PSP-related changes of DMI parameters were evaluated in a voxel-wise analysis and a region-of-interest (ROI)-based approach was established. The diagnostic performance was subsequently tested in an independent validation cohort. In the exploration cohort, 53 PSP patients were compared to a pooled comparison group of 19 patients with PD, 26 patients with MSA, 7 patients with corticobasal syndrome, and 25 healthy controls. PSP patients showed widespread axonal loss in the superior cerebellar peduncles, the dentato-rubro-thalamic tracts, the thalami and the frontal white matter (each P < 0.001). In the validation cohort consisting of 12 patients with PSP vs. 13 patients with other movement disorders, the accuracy of this ROI-based approach for identifying the PSP was highest in the thalamus and the frontal white matter (accuracy 0.96 each). This DMI approach can identify PSP patients on an individual level in a collective with suspected atypical Parkinson syndromes and allows further insight on microstructural alterations in vivo.

Where Position Matters-Deep-Learning-Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation.

INTRODUCTION: Recent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used imaging modality, but because of its idiosyncrasies (high spatial accuracy at low soft tissue resolution), it has not been sufficient for the parallel determination of electrode position and details of the surrounding brain anatomy (nuclei). The common solution is rigid fusion of CT images and magnetic resonance (MR) images, which have much better soft tissue contrast and allow accurate normalization into template spaces. Here, we explored a deep-learning approach to directly relate positions (usually the lead position) in postoperative CT images to the native anatomy of the midbrain and group space. MATERIALS AND METHODS: Deep learning is used to create derived tissue contrasts (white matter, gray matter, cerebrospinal fluid, brainstem nuclei) based on the CT image; that is, a convolution neural network (CNN) takes solely the raw CT image as input and outputs several tissue probability maps. The ground truth is based on coregistrations with MR contrasts. The tissue probability maps are then used to either rigidly coregister or normalize the CT image in a deformable way to group space. The CNN was trained in 220 patients and tested in a set of 80 patients. RESULTS: Rigorous validation of such an approach is difficult because of the lack of ground truth. We examined the agreements between the classical and proposed approaches and considered the spread of implantation locations across a group of identically implanted subjects, which serves as an indicator of the accuracy of the lead localization procedure. The proposed procedure agrees well with current magnetic resonance imaging-based techniques, and the spread is comparable or even lower. CONCLUSIONS: Postoperative CT imaging alone is sufficient for accurate localization of the midbrain nuclei and normalization to the group space. In the context of group analysis, it seems sufficient to have a single postoperative CT image of good quality for inclusion. The proposed approach will allow researchers and clinicians to include cases that were not previously suitable for analysis.

Association of MRI-based adrenal gland volume and impaired glucose metabolism in a population-based cohort study.

OBJECTIVES: The aim of this study was to assess adrenal gland volume by using magnetic resonance imaging (MRI) and to study its role as an indirect marker of impaired glucose metabolism and hypothalamic-pituitary-adrenal (HPA) axis activation in a population-based cohort. METHODS: Asymptomatic participants were enrolled in a nested case-control study and underwent a 3-T MRI, including T1w-VIBE-Dixon sequences. For the assessment of adrenal gland volume, adrenal glands were manually segmented in a blinded fashion. Impaired glucose metabolism was determined using fasting glucose and oral glucose tolerance test. Cardiometabolic risk factors were also obtained. Inter- and intrareader reliability as well as univariate and multivariate associations were derived. RESULTS: Among 375 subjects included in the analysis (58.5% male, 56.1 ± 9.1 years), 25.3% participants had prediabetes and 13.6% had type 2 diabetes (T2DM). Total adrenal gland volume was 11.2 ± 4.2 ml and differed significantly between impaired glucose metabolism and healthy controls with largest total adrenal gland volume in T2DM (healthy controls: 10.0 ± 3.9 ml, prediabetes: 12.5 ± 3.8 ml, T2DM: 13.9 ± 4.6 ml; p < 0.001). In the multivariate analysis, association of T2DM and increased adrenal gland volume was independent of age, sex, hypertension, triglycerides and body mass index (BMI), but was attenuated in subjects with prediabetes after adjustment for BMI. CONCLUSIONS: T2DM is significantly associated with increased adrenal gland volume by MRI in an asymptomatic cohort, independent of age, sex, dyslipidaemia, hypertension and BMI. Adrenal gland volume may represent an indirect marker of impaired glucose metabolism and HPA axis dysfunction.

Alzheimer resemblance atrophy index, BrainAGE, and normal pressure hydrocephalus score in the prediction of subtle cognitive decline: added value compared to existing MR imaging markers.

OBJECTIVES: Established visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)-based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls. METHODS: A prospective study including 80 individuals (46 females, mean age 73.4 ± 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers. RESULTS: AD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%. CONCLUSIONS: The AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs. KEY POINTS: • AD-RAI score at baseline was associated with Corsi score (visuospatial memory) decline. • BrainAGE score was associated with Trail Making B (executive functions) decrease. • AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

MRI of focal cortical dysplasia.

Focal cortical dysplasia (FCD) are histopathologically categorized in ILAE type I to III. Mild malformations of cortical development (mMCD) including those with oligodendroglial hyperplasia (MOGHE) are to be integrated into this classification yet. Only FCD type II have distinctive MRI and molecular genetics alterations so far. Subtle FCD including FCD type II located in the depth of a sulcus are often overlooked requiring the use of dedicated sequences (MP2RAGE, FLAWS, EDGE) and/or voxel (VBM)- or surface-based (SBM) postprocessing. The added value of 7 Tesla MRI has to be proven yet.

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.

BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. METHODS AND RESULTS: Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m2 with upper and lower limits of agreement of - 7.5 and 5.8 mL/m2, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%-from 105 to 34 s, in our in-house clinical setting. CONCLUSIONS: Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.

Surgery for IDH1/2 wild-type glioma invading the corpus callosum.

BACKGROUND: Glioblastoma of the corpus callosum (ccGBM) are rare tumors, with a dismal prognosis marked by a rapid clinical deterioration. For a long time, surgical treatment was not considered beneficial for most patients with such tumors. Recent studies claimed an improved survival for patients undergoing extensive resection, albeit without integration of the molecular profile of the lesions. The purpose of this study was to investigate the effect of biopsy and surgical resection on oncological and functional outcomes in patients with IDH wild-type ccGBM. METHODS: We performed a retrospective analysis of our institution's database of patients having been treated for high-grade glioma between 2005 and 2017. Inclusion criteria were defined as follows: patients older than 18 years, histopathological, and molecularly defined IDH wild-type glioma, major tumor mass (at least 2/3) invading the corpus callosum in the sagittal plane with a uni- or bilateral infiltration of the adjacent lobules. Surgical therapy (resection vs. biopsy), extent of resection according to the remaining tumor volume and adjuvant treatment as well as overall survival and functional outcome using the Karnofsky Performance Score (KPS) were analyzed. RESULTS: Fifty-five patients were included in the study, from which the mean age was 64 years and men (n = 34, 61.8%) were more often affected than women (n = 21, 38.2%). Thirty (54.5%) patients were treated with stereotactic biopsy alone, while 25 patients received tumor resection resulting in 14.5% (n = 8) gross-total resections and 30.9% (n = 17) partial resections. The 2-year survival rate after resection was 30% compared to 7% after biopsy (p = 0.047). The major benefit was achieved in the group with gross-total resection, while partial resection failed to improve survival. Neurological outcome measured by KPS did not differ between both groups either pre- or postoperatively. CONCLUSIONS: Our study suggests that in patients with corpus callosum glioblastoma, gross-total resection prolongs survival without negatively impacting neurological outcome as compared to biopsy.

Characterization of longitudinal transformation of T2-hyperintensity in oligodendroglioma.

BACKGROUND: Oligodendroglioma (ODG) are CNS resistant tumors characterized by their unique molecular signature, namely a combined deletion of 1p and 19q simultaneously to an IDH1/2 mutation. These tumors have a more favorable clinical outcome compared to other gliomas and a long-time survival that ranges between 10 and 20 years. However, during the course of the disease, multiple recurrences occur and the optimal treatment at each stage of the disease remains unclear. Here we report a retrospective longitudinal observation study of 836 MRI examinations in 44 ODG patients. METHODS: We quantified the volume of T2-hyperintensity to compute growth behavior in dependence of different treatment modalities, using various computational models. RESULTS: The identified growth pattern revealed dynamic changes, which were found to be patient-specific an did not correlate with clinical parameter or therapeutic interventions. Further, we showed that, surgical resection is beneficial for overall survival regardless the WHO grad or timepoint of surgery. To improve overall survival, an extent of resection above 50% is required. Multiple resections do not generally improve overall survival, except a greater extent of resection than in previous surgeries was achieved. CONCLUSIONS: Our data aids to improve the interpretation of MRI images in clinical practice.

Intra-axonal diffusivity in brain white matter.

Biophysical modeling lies at the core of evaluating tissue cellular structure using diffusion-weighted MRI, albeit with shortcomings. The challenges lie not only in the complexity of the diffusion phenomenon, but also in the need to know the diffusion-specific properties of diverse cellular compartments in vivo. The likelihood function obtained from the commonly acquired Stejskal-Tanner diffusion-weighted MRI data is degenerate with different parameter constellations explaining the signal equally well, thereby hindering an unambiguous parameter estimation. The aim of this study is to measure the intra-axonal water diffusivity which is one of the central parameters of white matter models. Estimating intra-axonal diffusivity is complicated by (i) the presence of other compartments, and (ii) the orientation dispersion of axons. Our measurement involves an efficient signal suppression of water in extra-axonal space and all cellular processes oriented outside a narrow cone around the principal fiber direction. This is achieved using a planar water mobility filter that suppresses signal from all molecules that are mobile in the plane transverse to the fiber bundle. After the planar filter, the diffusivity of the remaining intra-axonal signal is measured using linear and spherical diffusion encoding. We find the average intra-axonal diffusivity D0=2.25±0.03µm2/ms for the timing of the applied gradients, which gives D0(∞)≈2.0µm2/ms when extrapolated to infinite diffusion time. The result imposes a strong limitation on the parameter selection for biophysical modeling of diffusion-weighted MRI.

The absence of restricted water pool in brain white matter.

Understanding diffusion-weighted MR signal in brain white matter (WM) has been a long-sought-after goal. Modern research pursues this goal by focusing on the biological compartments that contributes essentially to the signal. In this study, we experimentally address the apparent presence of a compartment in which water motion is restricted in all spatial directions. Using isotropic diffusion encoding, we establish an upper bound on the fraction of such a compartment, which is shown to be about 2% of the unweighted signal for moderate diffusion times. This helps to eliminate such a compartment that have been assumed in literature on biophysical modeling. We also used the diffusion decay curve obtained from the isotropic encoding to establish a lower limit on the mean diffusivities of either of intra- or extra-axonal compartment as a function of their relative water fraction.

Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline.

This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods.

Modelfree global tractography.

Tractography based on diffusion-weighted MRI investigates the large scale arrangement of the neurite fibers in brain white matter. It is usually assumed that the signal is a convolution of a fiber specific response function (FRF) with a fiber orientation distribution (FOD). The FOD is the focus of tractography. While in the past the FRF was estimated beforehand and was usually assumed to be fix, more recent approaches estimate the response function during tractography. This work proposes a novel objective function independent of the FRF, just aiming for FOD reconstruction. The objective is integrated into global tractography showing promising results.

Arterial input function in a dedicated slice for cerebral perfusion measurements in humans.

OBJECT: We aimed to modify our previously published method for arterial input function measurements for evaluation of cerebral perfusion (dynamic susceptibility contrast MRI) such that it can be applied in humans in a clinical setting. MATERIALS AND METHODS: Similarly to our previous work, a conventional measurement sequence for dynamic susceptibility contrast MRI is extended with an additional measurement slice at the neck. Measurement parameters at this slice were optimized for the blood signal (short echo time, background suppression, magnitude and phase images). Phase-based evaluation of the signal in the carotid arteries is used to obtain quantitative arterial input functions. RESULTS: In all pilot measurements, quantitative arterial input functions were obtained. The resulting absolute perfusion parameters agree well with literature values (gray and white matter mean values of 46 and 24 mL/100 g/min, respectively, for cerebral blood flow and 3.0% and 1.6%, respectively, for cerebral blood volume). CONCLUSIONS: The proposed method has the potential to quantify arterial input functions in the carotid arteries from a direct measurement without any additional normalization.

Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach.

Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications.

Arterial spin labeling-based Z-maps have high specificity and positive predictive value for neurodegenerative dementia compared to FDG-PET.

OBJECTIVE: Cerebral perfusion analysis based on arterial spin labeling (ASL) MRI has been proposed as an alternative to FDG-PET in patients with neurodegenerative disease. Z-maps show normal distribution values relating an image to a database of controls. They are routinely used for FDG-PET to demonstrate disease-specific patterns of hypometabolism at the individual level. This study aimed to compare the performance of Z-maps based on ASL to FDG-PET. METHODS: Data were combined from two separate sites, each cohort consisting of patients with Alzheimer's disease (n = 18 + 7), frontotemporal dementia (n = 12 + 8) and controls (n = 9 + 29). Subjects underwent pseudocontinuous ASL and FDG-PET. Z-maps were created for each subject and modality. Four experienced physicians visually assessed the 166 Z-maps in random order, blinded to modality and diagnosis. RESULTS: Discrimination of patients versus controls using ASL-based Z-maps yielded high specificity (84%) and positive predictive value (80%), but significantly lower sensitivity compared to FDG-PET-based Z-maps (53% vs. 96%, p < 0.001). Among true-positive cases, correct diagnoses were made in 76% (ASL) and 84% (FDG-PET) (p = 0.168). CONCLUSION: ASL-based Z-maps can be used for visual assessment of neurodegenerative dementia with high specificity and positive predictive value, but with inferior sensitivity compared to FDG-PET. KEY POINTS: • ASL-based Z-maps yielded high specificity and positive predictive value in neurodegenerative dementia. • ASL-based Z-maps had significantly lower sensitivity compared to FDG-PET-based Z-maps. • FDG-PET might be reserved for ASL-negative cases where clinical suspicion persists. • Findings were similar at two study sites.