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Objectives: To prospectively evaluate the effect of hyoscine butylbromide (HBB) on visualisation of anatomical details and motion-related artefacts in mp-MRI of the prostate at 3.0 Tesla.Methods: One hundred and three consecutive patients (65 ± 10 years) were included in this trial, powered to demonstrate an improvement of image quality after HBB administration, assessed on a 5-point scale by two blinded readers. All patients received high-spatial resolution axial T2-weighted TSE sequences at 3.0 T without spasmolytic agent, repeated after application of 40 mg HBB and followed by routine mp-MRI. Secondary endpoints were (1) susceptibility to side effects, (2) dependence of spasmolytic effect on patients´ weight, and (3) prostate volume.Results: In 68% of patients, HBB significantly improved the anatomic score (mean 3.4 ± 0.9 before and 4.4 ± 0.7 after HBB for both readers, p = <0.001). In 67%, HBB significantly enhanced the artefact score (mean 3.2 ± 1 before and 4.2 ± 0.8 after HBB for reader 1, p = <0.001; 3.2 ± 1 and 4.1 ± 0.8 for reader 2, p = <0.001). Subgroup analysis revealed no statistically significant difference between patients with different bodyweight or prostate volume. Inter-reader agreement was excellent (k = 0.95-0.98).Conclusions: Hyoscine butylbromide significantly improves image quality and reduces motion-related artefacts in mp-MRI of the prostate independent of bodyweight or prostate volume. No side effects were reported.Key Points: • Hyoscine butylbromide (HBB) improved image quality in over 2/3 of patients. • Severe artefacts were reduced after HBB in more than 20%. • The number of non-diagnostic MRI was reduced to <1% after HBB. • HBB effect was independent of bodyweight and prostate volume. • No side effects of HBB were reported in this study population. [ABSTRACT FROM AUTHOR]

Objectives: This study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation., Methods: A pooled NC of 3945 subjects (NC pool ) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer's disease (AD) patients (55-82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NC pool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC., Results: The maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran's Q, p = 0.19)., Conclusion: At least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study's context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used., Clinical Relevance Statement: The influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation., Key Points: • Software-based brain atrophy estimation often relies on normal cohorts for comparisons. • At least 15 subjects must be included in an age- and sex-specific normal cohort. • Using different normal cohorts does not influence regional atrophy estimation., (© 2024. The Author(s).)

Objectives: An automated workflow for age- and sex-specific estimation of regional brain volume changes from structural MRI relative to a standard population is presented and evaluated for feasibility., Methods: T1w MRI scans are preprocessed in a standardized way comprising gray matter (GM) segmentation, normalization, modulation, and spatial smoothing. Resulting GM images are then compared to precomputed age- and sex-specific GM templates derived from the population-based Nathan Kline Institute Rockland Sample, and voxel-wise z-maps are compiled. z-maps are color-coded and fused with the subject's T1w images. The rate of technical success of the proposed workflow was evaluated in 1330 subjects of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Furthermore, medial temporal atrophy (MTA) was assessed using the color-coded maps and with the MTA visual rating scale in these subjects. Sensitivities and specificity of color-coded maps and MTA scale were compared using McNemar's test., Results: One test dataset was excluded due to severe motion artifacts. Out of the remaining 1329 datasets, atrophy map generation was successful in 1323 ADNI subjects (99.5%). Sensitivity for AD diagnosis (71.4 % vs. 53.3%, p < 0.0001 for left; 70.4% vs. 55.3%, p < 0.0001 for right hemisphere) and for MCI (45.4% vs. 17.4, p < 0.0001 for left; 43.5% vs. 14.6%, p < 0.0001 for right hemisphere) based on medial temporal atrophy assessment in color-coded maps was significantly higher than for MTA visual rating scale, while specificity was lower (78.4% vs. 93.8%, p < 0.0001 for left; 79.4% vs. 95.8%, p < 0.0001 for right hemisphere). The workflow is named veganbagel and is published as open-source software with an integrated PACS interface., Conclusions: Automated brain volume change estimation with the proposed workflow is feasible and technically dependable. It provides high potential for radiologic assessment of brain volume changes and neurodegenerative diseases., Key Points: • A workflow combining techniques from voxel-based morphometry and population-based neuroimaging data is feasible and technically highly dependable. • The workflow is provided as open-source software, named veganbagel. • Sensitivity of medial temporal atrophy assessment in atrophy maps from veganbagel exceeds the sensitivity of MTA visual rating scale for the diagnosis of Alzheimer's disease.

Objectives: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study., Methods: Three thousand one hundred ninety-nine CT chest examinations were used for training and testing of the feed-forward, single hidden layer neural network (January 2016-December 2017, 60% male, 62 ± 15 years, 80/20 split). The model was optimized and trained to predict the volumetric computed tomography dose index (CTDI vol ) based on scan patient metrics (scanner, study description, protocol, patient age, sex, and water-equivalent diameter (D W )). The root mean-squared error (RMSE) was calculated as performance measurement. One hundred separate, consecutive chest CTs were used for validation (January 2018, 60% male, 63 ± 16 years), independently reviewed by two blinded radiologists with regard to dose optimization, and used to define an optimal cutoff for the model., Results: RMSE was 1.71, 1.45, and 1.52 for the training, test, and validation dataset, respectively. The scanner and D W were the most important features. The radiologists found dose optimization potential in 7/100 of the validation cases. A percentage deviation of 18.3% between predicted and actual CTDI vol was found to be the optimal cutoff: 8/100 cases were flagged as suboptimal by the model (range 18.3-53.2%). All of the cases found by the radiologists were identified. One examination was flagged only by the model., Conclusions: ML can comprehensively detect CT examinations with dose optimization potential. It may be a helpful tool to simplify CT quality assurance. CT scanner and D W were most important. Final human review remains necessary. A threshold of 18.3% between the predicted and actual CTDI vol seems adequate for CT quality assurance., Key Points: • Machine learning can be integrated into CT quality assurance to improve retrospective analysis of CT dose data. • Machine learning may help to comprehensively detect dose optimization potential in chest CT, but an individual review of the results by an experienced radiologist or radiation physicist is required to exclude false-positive findings.

Objectives: The pathogenesis leading to poor functional outcome after aneurysmal subarachnoid haemorrhage (aSAH) is multifactorial and not fully understood. We evaluated a machine learning approach based on easily determinable clinical and CT perfusion (CTP) features in the course of patient admission to predict the functional outcome 6 months after ictus., Methods: Out of 630 consecutive subarachnoid haemorrhage patients (2008-2015), 147 (mean age 54.3, 66.7% women) were retrospectively included (Inclusion: aSAH, admission within 24 h of ictus, CTP within 24 h of admission, documented modified Rankin scale (mRS) grades after 6 months. Exclusion: occlusive therapy before first CTP, previous aSAH, CTP not evaluable). A random forests model with conditional inference trees was optimised and trained on sex, age, World Federation of Neurosurgical Societies (WFNS) and modified Fisher grades, aneurysm in anterior vs. posterior circulation, early external ventricular drainage (EVD), as well as MTT and T max maximum, mean, standard deviation (SD), range, 75th quartile and interquartile range to predict dichotomised mRS (≤ 2; > 2). Performance was assessed using the balanced accuracy over the training and validation folds using 20 repeats of 10-fold cross-validation., Results: In the final model, using 200 trees and the synthetic minority oversampling technique, median balanced accuracy was 84.4% (SD 0.7) over the training folds and 70.9% (SD 1.2) over the validation folds. The five most important features were the modified Fisher grade, age, MTT range, WFNS and early EVD., Conclusions: A random forests model trained on easily determinable features in the course of patient admission can predict the functional outcome 6 months after aSAH with considerable accuracy., Key Points: • Features determinable in the course of admission of a patient with aneurysmal subarachnoid haemorrhage (aSAH) can predict the functional outcome 6 months after the occurrence of aSAH. • The top five predictive features were the modified Fisher grade, age, the mean transit time (MTT) range from computed tomography perfusion (CTP), the WFNS grade and the early necessity for an external ventricular drainage (EVD). • The range between the minimum and the maximum MTT may prove to be a valuable biomarker for detrimental functional outcome.