RATIONALE AND OBJECTIVES:This study evaluates the accuracy of the nn-UNet TotalSegmentator (TS) by Wasserthal et al. (2023) in segmenting atypical livers with pathologies and variants in CT scans. MATERIALS AND METHODS:CT scans were retrospectively collected from our RIS and divided into two cohorts: a reference group (67 healthy livers) and a study group (55 scans across eleven pathology and variant subgroups). TS performed automatic segmentation for all groups. For reference, the images were then manually segmented, with corrections reviewed by two radiologists. Accuracy was assessed using Dice similarity score, Hausdorff distance (HD), mean surface distance (MSD), volume difference, and clinical ratings. RESULTS:Automatic segmentation underestimated liver volume by a mean of 48.11  ml (3.1%) in the reference group and overestimated it in 84% of study group cases by 79.09 ml (4%). The average Dice score was 0.980 ± 0.007 for the reference group and 0.933 ± 0.113 for the study group. Hepatomegaly achieved the highest score (0.979 ± 0.006), Polycystic liver disease (PLD) the lowest (0.656 ± 0.230). Cirrhosis with Ascites, Beavertail, and PLD had significantly lower Dice scores than the reference group. Clinical ratings were often lower than Dice scores suggested, especially in Beavertail, Cirrhosis with Ascites, Ablation defects, Metastases, and Hemihepatectomy. CONCLUSION:TS performs excellently on healthy and well on most pathological livers. Despite high Dice scores in many pathological cases, clinical ratings reveal limitations. Clinical evaluation remains essential. Inclusion of PLD and Beavertail cases in training data may reduce bias and improve performance.

Tenecteplase (TNK) offers promising efficacy and safety data for intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) and pharmacological advantages over alteplase (rt-PA), justifying its gradual adoption as primary thrombolytic agent. At our tertiary care center, we transitioned from rt-PA to TNK, providing valuable real-world insights into this process, including its use beyond the 4.5-hour time window. We retrospectively analyzed our stroke registry to compare clinical and procedural data from AIS patients treated with rt-PA (up to 6 months before transition) and those treated with TNK (up to 6 months after transition, starting June 2024). Primary endpoints included treatment metrics, such as door-to-needle (DTN), door-to-imaging (DTI), imaging-to-needle (ITN), door-to-groin and door-to-recanalization times. Safety outcomes comprised rate of any intracranial hemorrhage (ICH), symptomatic ICH (sICH), parenchymatous hematoma type 2 (PH 2) and post-thrombolysis angioedema. A semiquantitative questionnaire evaluated satisfaction with TNK and changes in lysis behavior among nurses and physicians 3 months post-implementation. During the twelve-month period (December 1, 2023 - November 30, 2024), 276 patients underwent IVT. Median DTN times were significantly shorter with TNK (n = 138) compared to rt-PA (n = 138) (TNK 27 min [IQR 19–39] vs. rt-PA 34 min [IQR 25–62]; p = 0.011). No significant differences were observed in safety outcomes, including any ICH (TNK 9

Distortions in brain MRI caused by gradient nonlinearities may reach several millimeters, thus distortion correction is strongly recommended for radiotherapy treatment planning. However, the significance of MRI distortion correction on actual clinical outcomes has not been described yet. Therefore, we investigated the impact of planning MRI distortion correction on subsequent local control in a historic series of 419 brain metastases in 189 patients treated with stereotactic radiotherapy between 01/2003 and 04/2015. Local control was evaluated using a volumetric extension of the RANO-BM criteria. The predictive significance of distortion correction was assessed using competing risk analysis. In this cohort, 2D distortion-corrected MRIs had been used for treatment planning in 52.5% (220/419) of lesions, while uncorrected MRIs had been employed in 47.5% (199/419) of metastases. 2D distortion correction was associated with improved local control (Cumulative incidence of local progression at 12 months: 14.3% vs. 21.2% and at 24 months: 18.7% vs. 28.6%, p = 0.038). In multivariate analysis, adjusting for histology, baseline tumor volume, interval between MRI and treatment delivery, year of planning MRI, biologically effective dose and adjuvant Whole-brain radiotherapy, use of distortion correction remained significantly associated with improved local control (HR 0.55, p = 0.020). This is the first study to clinically evaluate the impact of MRI gradient nonlinearity distortion correction on local control in stereotactic radiotherapy for brain metastases. In this historic series, we found significantly higher local control when using 2D corrected vs. uncorrected MRI studies for treatment planning. These results stress the importance of assuring that MR images used for radiotherapy treatment planning are properly distortion-corrected.

BackgroundDespite current guidelines recommending against intravenous thrombolysis (IVT) in acute ischemic stroke (AIS) patients with direct oral anticoagulants (DOAC) within prior 48 h, latest real-world data indicate no increased bleeding risk. However, these observations are based mainly on alteplase (rt-PA), whereas data for tenecteplase (TNK) are scarce.MethodsWe retrospectively compared data from our stroke registry of AIS-patients with DOAC (intake within the last 48 h), who received IVT either with rt-PA or TNK without prior antagonization. The primary outcome was the rate of symptomatic intracranial hemorrhage (sICH) per SITS-Most criteria. Secondary outcomes included the rate of any ICH or major bleeding, rate of mortality, neurological and functional outcome at discharge.Results82 AIS-patients were included, with 42 patients receiving TNK und 40 patients receiving rt-PA. Median age was 83 y for TNK patients and 82 y for rt-PA patients. Median NIHSS score at admission for TNK was 9 points for both groups (p = 0.61). Median drug-specific DOAC plasma level was 49 ng/mL for TNK versus 24 ng/mL for rt-PA (p = 0.04). We found no statistically significant increased risk for neither sICH (TNK 2.4% vs. rt-PA 2.5%; p = 1), nor for other safety outcomes for TNK-treated patients compared with rt-PA. The rate of excellent functional outcome (TNK 61.9% vs. rt-PA 52.5%) was similar among both groups. High drug-specific DOAC plasma levels were not related to an increased rate of hemorrhagic complications in our cohort.ConclusionWe report no increased rate of (s)ICH for TNK based IVT compared with rt-PA in AIS-patients with DOAC, indicating a similar safety profile. Moderate to high drug-specific DOAC levels were no surrogates for hemorrhagic complications, supporting the implementation of specific Standard Operating Procedures for IVT in DOAC-treated patients. Contrary to previous studies, we did not observe an increased rate of early recanalization of LVO in TNK-treated patients in this small single-center cohort.Trial registrationn/A.

Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma patients is crucial for optimal treatment planning. However, this task remains challenging due to the overlapping imaging characteristics of PsP and TP. This study therefore proposes a multimodal deep-learning approach utilizing complementary information from routine anatomical MR images, clinical parameters, and RT treatment planning information for improved predictive accuracy. The approach utilizes a self-supervised Vision Transformer (ViT) to encode multi-sequence MR brain volumes to effectively capture both global and local context from the high dimensional input. The encoder is trained in a self-supervised upstream task on unlabeled glioma MRI datasets from the open BraTS2021, UPenn-GBM, and UCSF-PDGM datasets (n = 2317 MRI studies) to generate compact, clinically relevant representations from FLAIR and T1 post-contrast sequences. These encoded MR inputs are then integrated with clinical data and RT treatment planning information through guided cross-modal attention, improving progression classification accuracy. This work was developed using two datasets from different centers: the Burdenko Glioblastoma Progression Dataset (n = 59) for training and validation, and the GlioCMV progression dataset from the University Hospital Erlangen (UKER) (n = 20) for testing. The proposed method achieved competitive performance, with an AUC of 75.3%, outperforming the current state-of-the-art data-driven approaches. Importantly, the proposed approach relies solely on readily available anatomical MRI sequences, clinical data, and RT treatment planning information, enhancing its clinical feasibility. The proposed approach addresses the challenge of limited data availability for PsP and TP differentiation and could allow for improved clinical decision-making and optimized treatment plans for glioblastoma patients.

BACKGROUND:Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. OBJECTIVES:The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. METHODS:This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search. RESULTS:The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented. CONCLUSIONS:The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

Chemical exchange saturation transfer (CEST) is a magnetic resonance (MR) imaging method providing molecular image contrasts based on indirect detection of low concentrated solutes. Previous CEST studies focused predominantly on the imaging of single CEST exchange regimes (e.g., slow, intermediate or fast exchanging groups). In this work, we aim to establish a so-called comprehensive CEST protocol for 7 T, covering the different exchange regimes by three saturation B1 amplitude regimes: low, intermediate and high. We used the results of previous publications and our own simulations in pulseq-CEST to produce a 7 T CEST protocol that has sensitivity to these three B1 regimes. With postprocessing optimization (simultaneous mapping of water shift and B1, B0-fitting, multiple interleaved mode saturation B1 correction, neural network employment (deepCEST) and analytical input feature reduction), we are able to shorten our initially 40 min protocol to 15 min and generate six CEST contrast maps simultaneously. With this protocol, we measured four healthy subjects and one patient with a brain tumor. We established a comprehensive CEST protocol for clinical 7 T MRI, covering three different B1 amplitude regimes. We were able to reduce the acquisition time significantly by more than 50%, while still maintaining decent image quality and contrast in healthy subjects and one patient with a tumor. Our protocol paves the way to perform comprehensive CEST studies in clinical scan times for hypothesis generation regarding molecular properties of certain pathologies, for example, ischemic stroke or high-grade brain tumours.

Chemical exchange saturation transfer (CEST) is an MRI method that provides insights on the metabolic level. Several metabolite effects appear in the CEST spectrum. These effects are isolated by Lorentzian curve fitting. The separation of CEST effects suffers from the inhomogeneity of the saturation field B1. This leads to inhomogeneities in the associated metabolic maps. Current B1 correction methods require at least two sets of CEST-spectra. This at least doubles the acquisition time. In this study, we investigated the use of an unsupervised physics-informed conditional autoencoder (PICAE) to efficiently correct B1 inhomogeneity and isolate metabolic maps while using a single CEST scan. The proposed approach integrates conventional Lorentzian model into the conditional autoencoder and performs voxel-wise B1 correction and Lorentzian line fitting. The method provides clear interpretation of each step and is inherently generative. Thus, CEST-spectra and fitted metabolic maps can be created at arbitrary B1 levels. This is important because the B1 dispersion contains information about the exchange rates and concentration of metabolite protons, paving the way for their quantification. The isolated maps for tumor data showed a robust B1 correction and more than 25% increase in structural similarity index (SSIM) with gadolinium reference image compared to the standard interpolation-based method and subsequent Lorentzian curve fitting. This efficient correction method directly results in at least 50% reduction in scan time.

OBJECTIVE:Krabbe disease (KD) is a multisystem neurodegenerative disorder with severe disability and premature death, mostly with an infancy/childhood onset. In rare cases of late-onset phenotypes, symptoms are often milder and difficult to diagnose. We here present a translational approach combining diagnostic and biochemical analyses of a male patient with a progressive gait disorder starting at the age of 44 years, with a final diagnosis of late-onset KD (LOKD). METHODS:Additionally to cerebral MRI, protein structural analyses of the β-galactocerebrosidase protein (GALC) were performed. Moreover, expression, lysosomal localization, and activities of β-glucocerebrosidase (GCase), cathepsin B (CTSB), and cathepsin D (CTSD) were analyzed in leukocytes, fibroblasts, and lysosomes of fibroblasts. RESULTS:Exome sequencing revealed biallelic likely pathogenic variants: GALC exons 11-17: 33 kb deletion; exon 4: missense variant (c.334A>G, p.Thr112Ala). We detected a reduced GALC activity in leukocytes and fibroblasts. While histological KD phenotypes were absent in fibroblasts, they showed a significantly decreased activities of GCase, CTSB, and CTSD in lysosomal fractions, while expression levels were unaffected. INTERPRETATION:The presented LOKD case underlines the age-dependent appearance of a mildly pathogenic GALC variant and its interplay with other lysosomal proteins. As GALC malfunction results in reduced ceramide levels, we assume this to be causative for the here described decrease in CTSB and CTSD activity, potentially leading to diminished GCase activity. Hence, we emphasize the importance of a functional interplay between the lysosomal enzymes GALC, CTSB, CTSD, and GCase, as well as between their substrates, and propose their conjoined contribution in KD pathology.

Die Magnetresonanztomographie (MRT) der Skelettmuskulatur (kurz: Muskel-MRT) wird zunehmend routinemäßig zur Diagnose und Verlaufsbeurteilung von Muskelerkrankungen eingesetzt. Jedoch existierten bislang keine zentrumsübergreifenden Standards für Messprotokolle und die radiologische Befundung. In der vorliegenden Expertenempfehlung sollen Standards zur Anwendung und Befundinterpretation der Muskel-MRT bei angeborenen und entzündlichen Muskelerkrankungen vorgestellt werden. Diese Arbeit entstand in Zusammenarbeit von Neurologen, Neuroradiologen, Radiologen, Neuropädiatern, Neurowissenschaftlern sowie MR-Physikern verschiedener Universitätskliniken in Deutschland. Die Empfehlungen basieren auf Expertenwissen und einer gezielten Literaturrecherche. Es werden die Indikationen für eine Muskel-MRT erläutert. Diese beinhalten den Nachweis und die Verlaufskontrolle von strukturellen und ödematösen Veränderungen der Muskulatur sowie die Identifizierung einer geeigneten Biopsiestelle. Des Weiteren werden Empfehlungen zum Untersuchungsablauf und für geeignete MRT-Sequenzen gegeben. Zuletzt werden die Schritte für eine strukturierte radiologische Befunderhebung dargestellt. Die vorliegende Arbeit bietet konkrete Empfehlungen zur Indikationsstellung, Durchführung und Befundinterpretation der Muskel-MRT. Darüber hinaus stellt sie eine mögliche Grundlage zur Vereinheitlichung der Messprotokolle an allen klinischen Standorten in Deutschland dar.

Accurate Magnetic Resonance Imaging (MRI) simulation is fundamental for high-precision stereotactic radiosurgery and fractionated stereotactic radiotherapy, collectively referred to as stereotactic radiotherapy (SRT), to deliver doses of high biological effectiveness to well-defined cranial targets. Multiple MRI hardware related factors as well as scanner configuration and sequence protocol parameters can affect the imaging accuracy and need to be optimized for the special purpose of radiotherapy treatment planning. MRI simulation for SRT is possible for different organizational environments including patient referral for imaging as well as dedicated MRI simulation in the radiotherapy department but require radiotherapy-optimized MRI protocols and defined quality standards to ensure geometrically accurate images that form an impeccable foundation for treatment planning. For this guideline, an interdisciplinary panel including experts from the working group for radiosurgery and stereotactic radiotherapy of the German Society for Radiation Oncology (DEGRO), the working group for physics and technology in stereotactic radiotherapy of the German Society for Medical Physics (DGMP), the German Society of Neurosurgery (DGNC), the German Society of Neuroradiology (DGNR) and the German Chapter of the International Society for Magnetic Resonance in Medicine (DS-ISMRM) have defined minimum MRI quality requirements as well as advanced MRI simulation options for cranial SRT.

OBJECTIVES:This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. MATERIALS AND METHODS:A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. RESULTS:For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. CONCLUSION:Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

Background:This research aims to improve glioblastoma survival prediction by integrating MR images, clinical, and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Methods:We propose and evaluate a transformer-based nonlinear and nonproportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with nonimaging data using cross-attention. To demonstrate model generalizability, the model is assessed with the time-dependent concordance index (Cdt) in 2 training setups using 3 independent public test sets: UPenn-GBM, UCSF-PDGM, and Rio Hortega University Hospital (RHUH)-GBM, each comprising 378, 366, and 36 cases, respectively. Results:The proposed transformer model achieved a promising performance for imaging as well as nonimaging data, effectively integrating both modalities for enhanced performance (UCSF-PDGM test-set, imaging Cdt 0.578, multimodal Cdt 0.672) while outperforming state-of-the-art late-fusion 3D-CNN-based models. Consistent performance was observed across the 3 independent multicenter test sets with Cdt values of 0.707 (UPenn-GBM, internal test set), 0.672 (UCSF-PDGM, first external test set), and 0.618 (RHUH-GBM, second external test set). The model achieved significant discrimination between patients with favorable and unfavorable survival for all 3 datasets (log-rank P 1.9 × 10-8, 9.7 × 10-3, and 1.2 × 10-2). Comparable results were obtained in the second setup using UCSF-PDGM for training/internal testing and UPenn-GBM and RHUH-GBM for external testing (Cdt 0.670, 0.638, and 0.621). Conclusions:The proposed transformer-based survival prediction model integrates complementary information from diverse input modalities, contributing to improved glioblastoma survival prediction compared to state-of-the-art methods. Consistent performance was observed across institutions supporting model generalizability.

Fatty acid hydroxylase-associated neurodegeneration (FAHN/SPG35) is caused by pathogenic variants in FA2H and has been linked to a continuum of specific motor and non-motor neurological symptoms, leading to progressive disability. As an ultra-rare disease, its mutational spectrum has not been fully elucidated. Here, we present the prototypical workup of a novel FA2H variant, including clinical and in silico validation. An 18-year-old male patient presented with a history of childhood-onset progressive cognitive impairment, as well as progressive gait disturbance and lower extremity muscle cramps from the age of 15. Additional symptoms included exotropia, dystonia, and limb ataxia. Trio exome sequencing revealed a novel homozygous c.75C>G (p.Cys25Trp) missense variant in the FA2H gene, which was located in the cytochrome b5 heme-binding domain. Evolutionary conservation, prediction models, and structural protein modeling indicated a pathogenic loss of function. Brain imaging showed characteristic features, thus fulfilling the complete multisystem neurodegenerative phenotype of FAHN/SPG35. In summary, we here present a novel FA2H variant and provide prototypical clinical findings and structural analyses underpinning its pathogenicity.

BACKGROUND:Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. OBJECTIVES:The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. METHODS:This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search. RESULTS:The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented. CONCLUSIONS:The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

PurposeIn this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated.Theory and MethodsThe joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss-Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values.ResultsThe proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities.ConclusionThe proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.

The amide and rNOE CEST amplitudes are negatively correlated with age. Most prominently, the mean gray matter amide CEST amplitude decreases on average by 0.22 % per year of age in the cohort of our study.

APTw CEST MRI suffers from long preparation times and consequently long acquisition times (~5 min). Recently, a consensus on the preparation module for clinical APTw CEST at 3 T was found in the community, and we present a fast whole-brain APTw CEST MRI sequence following this consensus preparation of pulsed RF irradiation of 2 s duration at 90% RF duty-cycle and a B1,rms of 2 μT. After optimization of the snapshot CEST approach for APTw imaging regarding flip angle, voxel size and frequency offset sampling, we extend it by undersampled GRE acquisition and compressed sensing reconstruction. This allows 2 mm isotropic whole-brain APTw imaging for clinical research at 3 T below 2 min. With this sequence, a fast snapshot APTw imaging method is now available for larger clinical studies of brain tumors.