Quantifizierung von Metaboliten des Gehirns mit Hilfe der Magnetresonanzspektroskopie unter Verwendung von LCModel

In dieser Dissertation wurden in zwei Teilen die Möglichkeiten der Quantifizierung von Metaboliten im Gehirn via 1H in-vivo MRS und der Auswertung mittels LCModel untersucht. Im ersten Teil der Arbeit wurde eine Methode etabliert, die auf eine verbesserte Akquirierung von Laktat in Hirntumoren und der Trennung des Laktatsignals von überlagernden Lipidsignalen abzielt. Laktat ist ein wichtiger Marker anaeroben Stoffwechsels und somit ein Tumormarker in der MRS. Dazu wurden 20 muli-Voxel- Tumorspektren bei einer Echozeit von 135ms von 5 Hirntumorpatienten mit einer modifizierten LCModel-Auswertung analysiert. Es konnte eine Vergrößerung der Laktat- Kreatin-Ratio von durchschnittlich 2,95 auf 3,44 und eine Verkleinerung der mittleren Cramér-Rao-Werte von 12 auf 8,8 erreicht werden. Die Anpassung anderer wichtiger Metaboliten wurde dabei nicht negativ beeinflusst. Im zweiten Projektteil wurden die Möglichkeiten der Quantifizierung von Glutamat und Glutamin im gesunden Gehirn von Probanden bei Variation der Echozeit beleuchtet. Dazu wurden mit den zwei verschiedenen CSI-Sequenzen PRESS und Semi-LASER bei fünf Echozeiten (40ms, 60ms, 80ms, 100ms, 135ms) Spektren akquiriert. Die Spektren des VOI jedes Datensatzes wurden gemittelt, um eine gute Spektrenqualität zu erreichen und dann mittels LCModel ausgewertet. Dabei wurden drei verschiedene LCModel- Basisdatensätze verwendet. Diese unterschieden sich in dem Einsatz von Glutamat- und Glutaminbasisspektren im Basisdatensatz. Der erste Basisdatensatz enthielt beide Metaboliten. Im zweiten Basisdatensatz wurde nur Glutamat verwendet, während im dritten Basisdatensatz Glutamat und Glutamin im Verhältnis 7:3 zu Glx zusammengefasst wurden. Diese drei Datensätze wurden angewandt um das Verhalten der Resonanzenanpassung durch LCModel zu eruieren. Im Rahmen der Auswertung wurde das Verhalten der Konzentrationswerte der Metaboliten mit steigender Echozeit untersucht. Es zeigte sich, dass das neurometabolische Profil der Probanden unabhängig von Geschlecht und Alter sehr ähnlich ist und dass diese Methode gut geeignet ist, um qualitativ hochwertige MR Spektren hervorzubringen. Eine adäquate Quantifizierung des Metaboliten Glutamat ist ebenfalls mit dieser Methodik möglich. Die Quantifizierung des deutlich schwieriger zu 77 messenden Metaboliten Glutamin war allerdings insuffizient. Dieses Ergebnis ergab sich anhand des Verlaufs der Glutaminkonzentrationswerte mit steigender Echozeit. Diese beschrieben unabhängig von der zur Akquirierung verwendeten Sequenz keinen konstanten T2-Abfall. Dadurch konnte gezeigt werden, dass das oftmals in der Forschung und Klinik genutzte Vorgehen der Bestimmung von Glutamat, Glutamin oder Glx mit LCModel kritisch hinterfragt werden sollte

Retrospective accuracy analysis of MRI based lesion size measurement in neuroblastic tumors: which sequence should we choose?

Background!#!MR imaging of neuroblastic tumors is widely used for assessing the effect of chemotherapy on tumor size. However, there are some concerns that MRI might falsely estimate lesion diameters due to calcification and fibrosis. Therefore, the aim of our study was to compare neuroblastic tumor size based on MRI measurements to histopathology measurements of the resected specimens as standard of reference.!##!Methods!#!Inclusion criteria were diagnosis of a neuroblastic tumor, MR imaging within 100 days to surgery and gross total resection without fragmentation of the tumor between 2008 and 2019. Lesion diameters were measured by two radiologists according to RECIST 1.1 in axial plane in T2w turbo spin echo (TSE), diffusion-weighted imaging (DWI), and in T1w pre- and postcontrast sequences. Furthermore, the largest lesion size in three-dimensions was noted. The largest diameter of histopathology measurements of each specimen was used for comparison with MRI.!##!Results!#!Thirty-seven patients (mean age: 5 ± 4 years) with 38 lesions (neuroblastoma: n = 17; ganglioneuroblastoma: n = 11; ganglioneuroma: n = 10) were included in this retrospective study. There was excellent intra-class correlation coefficient between both readers for all sequences (> 0.9) Tumor dimensions of reader 1 based on axial MRI measurements were significantly smaller with the following median differences (cm): T1w precontrast - 1.4 (interquartile range (IQR): 1.8), T1w postcontrast - 1.0 (IQR: 1.9), T2w TSE: -1.0 (IQR: 1.6), and DWI -1.3 (IQR: 2.2) (p < 0.001 for all sequences). However, the evaluation revealed no significant differences between the three-dimensional measurements and histopathology measurements of the resected specimens regardless of the applied MRI sequence.!##!Conclusions!#!Axial MRI based lesion size measurements are significantly smaller than histopathological measurements. However, there was no significant difference between three-dimensional measurements and histopathology measurements of the resected specimens. T2w TSE and T1w postcontrast images provided the lowest deviation and might consequently be preferred for measurements

Combined Metabolic and Functional Tumor Volumes on [¹⁸F]FDG-PET/MRI in Neuroblastoma Using Voxel-Wise Analysis

Purpose: The purpose of our study was to evaluate the association between the [18F]FDG standard uptake value (SUV) and the apparent diffusion coefficient (ADC) in neuroblastoma (NB) by voxel-wise analysis. Methods: From our prospective observational PET/MRI study, a subcohort of patients diagnosed with NB with both baseline imaging and post-chemotherapy imaging was further investigated. After registration and tumor segmentation, metabolic and functional tumor volumes were calculated from the ADC and SUV values using dedicated software allowing for voxel-wise analysis. Under the mean of thresholds, each voxel was assigned to one of three virtual tissue groups: highly vital (v) (low ADC and high SUV), possibly low vital (lv) (high ADC and low SUV), and equivocal (e) with high ADC and high SUV or low ADC and low SUV. Moreover, three clusters were generated from the total tumor volumes using the method of multiple Gaussian distributions. The Pearson’s correlation coefficient between the ADC and the SUV was calculated for each group. Results: Out of 43 PET/MRIs in 21 patients with NB, 16 MRIs in 8 patients met the inclusion criteria (PET/MRIs before and after chemotherapy). The proportion of tumor volumes were 26%, 36%, and 38% (v, lv, e) at baseline, 0.03%, 66%, and 34% after treatment in patients with response, and 42%, 25%, and 33% with progressive disease, respectively. In all clusters, the ADC and the SUV correlated negatively. In the cluster that corresponded to highly vital tissue, the ADC and the SUV showed a moderate negative correlation before treatment (R = −0.18; p p n = 2) under therapy had a relevant part in this cluster post-treatment. Conclusion: Our results indicate that voxel-wise analysis of the ADC and the SUV is feasible and can quantify the different quality of tissue in neuroblastic tumors. Monitoring ADCs as well as SUV levels can quantify tumor dynamics during therapy

AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans

(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman’s correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 ± 9.62 vs. 43.4 ± 4.45 vs. 34.8 ± 3.27 HU; each p < 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4–5) vs. 3 (4–5) vs. 3 (2–4), each p < 0.001) with good inter-rater agreement (r ≥ 0.790; p ≤ 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r ≥ 0.764; p < 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 ± 1.56 vs. 2.45 ± 1.90 vs. 2.66 ± 2.31 min; F (1.000; 99.00) = 268.1; p < 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially