α-D-Glucose as a non-radioactive MRS tracer for metabolic studies of the brain.

Changes in brain glucose metabolism occur in many neurological disorders as well as during aging. Most studies on the uptake of glucose in the brain use positron emission tomography, which requires injection of a radioactive tracer. Our study shows that ultra-high-field 1H-MRS can be used to measure α-D-glucose at 5.22 ppm in vivo, and the α-D-glucose can be used as a radiation-free tracer in the human brain

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.

Purpose Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method. Materials and Methods We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM). Results Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= -4.2, right = 4.2) than with FreeSurfer (left= -3.1, right = 3.7) and FSL (left= -2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods. Conclusion Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS

Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty

The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by separated training on low-grade gliomas (LGGs) and high-grade gliomas (HGGs), but in practice this information is not available at test time to decide which model to use. By contrast with HGGs, LGGs often present no sharp boundary between the tumor core and the surrounding edema, but rather a gradual reduction of tumor-cell density. Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, which ranked highly in the 2019 BraTS challenge and was trained using an uncertainty-aware loss, we separate cases into those with a confidently segmented core, and those with a vaguely segmented or missing core. Since by assumption every tumor has a core, we reduce the threshold for classification of core tissue in those cases where the core, as segmented by the classifier, is vaguely defined or missing. We then predict survival of high-grade glioma patients using a fusion of linear regression and random forest classification, based on age, number of distinct tumor components, and number of distinct tumor cores. We present results on the validation dataset of the Multimodal Brain Tumor Segmentation Challenge 2020 (segmentation and uncertainty challenge), and on the testing set, where the method achieved 4th place in Segmentation, 1st place in uncertainty estimation, and 1st place in Survival prediction.Comment: Presented (virtually) in the MICCAI Brainles workshop 2020. Accepted for publication in Brainles proceeding

Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters.

Volumetric assessment based on structural MRI is increasingly recognized as an auxiliary tool to visual reading, also in examinations acquired in the clinical routine. However, MRI acquisition parameters can significantly influence these measures, which must be considered when interpreting the results on an individual patient level. This Technical Note shall demonstrate the problem. Using data from a dedicated experiment, we show the influence of two crucial sequence parameters on the GM/WM contrast and their impact on the measured volumes. A simulated contrast derived from acquisition parameters TI/TR may serve as surrogate and is highly correlated (r=0.96) with the measured contrast

SLOW: A novel spectral editing method for whole-brain MRSI at ultra high magnetic field.

PURPOSE At ultra-high field (UHF), B1 + -inhomogeneities and high specific absorption rate (SAR) of adiabatic slice-selective RF-pulses make spatial resolved spectral-editing extremely challenging with the conventional MEGA-approach. The purpose of the study was to develop a whole-brain resolved spectral-editing MRSI at UHF (UHF, B0  ≥ 7T) within clinical acceptable measurement-time and minimal chemical-shift-displacement-artifacts (CSDA) allowing for simultaneous GABA/Glx-, 2HG-, and PE-editing on a clinical approved 7T-scanner. METHODS Slice-selective adiabatic refocusing RF-pulses (2π-SSAP) dominate the SAR to the patient in (semi)LASER based MEGA-editing sequences, causing large CSDA and long measurement times to fulfill SAR requirements, even using SAR-minimized GOIA-pulses. Therefore, a novel type of spectral-editing, called SLOW-editing, using two different pairs of phase-compensated chemical-shift selective adiabatic refocusing-pulses (2π-CSAP) with different refocusing bandwidths were investigated to overcome these problems. RESULTS Compared to conventional echo-planar spectroscopic imaging (EPSI) and MEGA-editing, SLOW-editing shows robust refocusing and editing performance despite to B1 + -inhomogeneity, and robustness to B0 -inhomogeneities (0.2 ppm ≥ ΔB0  ≥ -0.2 ppm). The narrow bandwidth (∼0.6-0.8 kHz) CSAP reduces the SAR by 92%, RF peak power by 84%, in-excitation slab CSDA by 77%, and has no in-plane CSDA. Furthermore, the CSAP implicitly dephases water, lipid and all the other signals outside of range (≥ 4.6 ppm and ≤1.4 ppm), resulting in additional water and lipid suppression (factors ≥ 1000s) at zero SAR-cost, and no spectral aliasing artifacts. CONCLUSION A new spectral-editing has been developed that is especially suitable for UHF, and was successfully applied for 2HG, GABA+, PE, and Glx-editing within 10 min clinical acceptable measurement time

Neural correlates of working memory and its association with metabolic parameters in early-treated adults with phenylketonuria.

BACKGROUND Phenylketonuria (PKU) is an inborn error of metabolism affecting the conversion of phenylalanine (Phe) into tyrosine. Previous research has found cognitive and functional brain alterations in individuals with PKU even if treated early. However, little is known about working memory processing and its association with task performance and metabolic parameters. The aim of the present study was to examine neural correlates of working memory and its association with metabolic parameters in early-treated adults with PKU. METHODS This cross-sectional study included 20 early-treated adults with PKU (mean age: 31.4 years ± 9.0) and 40 healthy controls with comparable age, sex, and education (mean age: 29.8 years ± 8.2). All participants underwent functional magnetic resonance imaging (fMRI) of working memory to evaluate the fronto-parietal working memory network. Fasting blood samples were collected from the individuals with PKU to acquire a concurrent plasma amino acid profile, and retrospective Phe concentrations were obtained to estimate an index of dietary control. RESULTS On a cognitive level, early-treated adults with PKU displayed significantly lower accuracy but comparable reaction time in the working memory task compared to the control group. Whole-brain analyses did not reveal differences in working memory-related neural activation between the groups. Exploratory region-of-interest (ROI) analyses indicated reduced neural activation in the left and right middle frontal gyri and the right superior frontal gyrus in the PKU group compared to the control group. However, none of the ROI analyses survived correction for multiple comparisons. Neural activation was related to concurrent Phe, tyrosine, and tryptophan concentrations but not to retrospective Phe concentrations. CONCLUSION In early-treated adults with PKU, cognitive performance and neural activation are slightly altered, a result that is partly related to metabolic parameters. This study offers a rare insight into the complex interplay between metabolic parameters, neural activation, and cognitive performance in a sample of individuals with PKU

Large-scale transient peri-ictal perfusion magnetic resonance imaging abnormalities detected by quantitative image analysis.

Epileptic seizures require a rapid and safe diagnosis to minimize the time from onset to adequate treatment. Some epileptic seizures can be diagnosed clinically with the respective expertise. For more subtle seizures, imaging is mandatory to rule out treatable structural lesions and potentially life-threatening conditions. MRI perfusion abnormalities associated with epileptic seizures have been reported in CT and MRI studies. However, the interpretation of transient peri-ictal MRI abnormalities is routinely based on qualitative visual analysis and therefore reader dependent. In this retrospective study, we investigated the diagnostic yield of visual analysis of perfusion MRI during ictal and postictal states based on comparative expert ratings in 51 patients. We further propose an automated semi-quantitative method for perfusion analysis to determine perfusion abnormalities observed during ictal and postictal MRI using dynamic susceptibility contrast MRI, which we validated on a subcohort of 27 patients. The semi-quantitative method provides a parcellation of 3D T1-weighted images into 32 standardized cortical regions of interests and subcortical grey matter structures based on a recently proposed method, direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation for brain anatomy segmentation. Standard perfusion maps from a Food and Drug Administration-approved image analysis tool (Olea Sphere 3.0) were co-registered and investigated for region-wise differences between ictal and postictal states. These results were compared against the visual analysis of two readers experienced in functional image analysis in epilepsy. In the ictal group, cortical hyperperfusion was present in 17/18 patients (94% sensitivity), whereas in the postictal cohort, cortical hypoperfusion was present only in 9/33 (27%) patients while 24/33 (73%) showed normal perfusion. The (semi-)quantitative dynamic susceptibility contrast MRI perfusion analysis indicated increased thalamic perfusion in the ictal cohort and hypoperfusion in the postictal cohort. Visual ratings between expert readers performed well on the patient level, but visual rating agreement was low for analysis of subregions of the brain. The asymmetry of the automated image analysis correlated significantly with the visual consensus ratings of both readers. We conclude that expert analysis of dynamic susceptibility contrast MRI effectively discriminates ictal versus postictal perfusion patterns. Automated perfusion evaluation revealed favourable interpretability and correlated well with the classification of the visual ratings. It may therefore be employed for high-throughput, large-scale perfusion analysis in extended cohorts, especially for research questions with limited expert rater capacity

Diagnosis of Small Unruptured Intracranial Aneurysms : Comparison of 7 T versus 3 T MRI.

PURPOSE Differentiating normal anatomical variants such as an infundibulum or a vascular loop from true intracranial aneurysms is crucial for patient management. We hypothesize that high-resolution 7 T magnetic resonance imaging (MRI) improves the detection and characterization of normal anatomical variants that may otherwise be misdiagnosed as small unruptured aneurysms. METHODS This is a retrospective, single-center study. All patients were scanned on a clinically approved 7 T MRI scanner and on a 3 T scanner. Image analysis was performed independently by three neuroradiologists blinded to clinical information. The presence of an unruptured intracranial aneurysm (UIA) and level of diagnostic certainty were assessed and the interrater agreement was calculated. If an aneurysm was present, the anatomic location and shape were recorded and compared. RESULTS In total, 53 patients with equivocal cerebrovascular findings on 1.5 T or 3 T MRI referred for a 7T MRI examination were included. Aneurysms were suspected in 42 patients examined at 3 T and in 23 patients at 7 T (rate difference 36%, 95% confidence interval, CI, 19-53%, p-value < 0.001). Major disagreement between the field strengths was observed in the A1 segment of anterior cerebral artery/anterior communicating artery (A1/ACOM) complex. The interrater agreement among the readers on the presence of an aneurysm on 7 T MRI was higher than that for 3 T MRI (0.925, 95% CI 0.866-0.983 vs. 0.786, 95% CI 0.700-0.873). CONCLUSION Our analysis demonstrates a significantly higher interrater agreement and improved diagnostic certainty when small intracranial aneurysms are visualized on 7 T MRI compared to 3 T. In a selected patient cohort, clinical implementation of 7 T MRI may help to establish the definitive diagnosis and thus have a beneficial impact on patient management

Diagnostics of micro- and nanostructure using the scanning probe microscopy, Journal of Telecommunications and Information Technology, 2005, nr 1

In this paper we summarize the results of our research concerning the diagnostics of micro- and nanostructure with scanning probe microscopy (SPM). We describe the experiments performed with one of the scanning probe microscopy techniques enabling also insulating surfaces to be investigated, i.e., atomic force microscopy (AFM). We present the results of topography measurements using both contact and non-contact AFM modes, investigations of the friction forces that appear between the microtip and the surface, and experiments connected with the thermal behaviour of integrated circuits, carried out with the local resolution of 20 nm