13 research outputs found
Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics
No full textMore Space, Less Noise—New-generation Low-Field Magnetic Resonance Imaging Systems Can Improve Patient Comfort: A Prospective 0.55T–1.5T-Scanner Comparison
No full textObjectives: The objectives of this study were to assess patient comfort when imaged on a newly introduced 0.55T low-field magnetic resonance (MR) scanner system with a wider bore opening compared to a conventional 1.5T MR scanner system. Materials and Methods: In this prospective study, fifty patients (mean age: 66.2 ± 17.0 years, 22 females, 28 males) underwent subsequent magnetic resonance imaging (MRI) examinations with matched imaging protocols at 0.55T (MAGNETOM FreeMax, Siemens Healthineers; Erlangen, Germany) and 1.5T (MAGNETOM Avanto Fit, Siemens Healthineers; Erlangen, Germany) on the same day. MRI performed between 05/2021 and 07/2021 was included for analysis. The 0.55T MRI system had a bore opening of 80 cm, while the bore diameter of the 1.5T scanner system was 60 cm. Four patient groups were defined by imaged body regions: (1) cranial or cervical spine MRI using a head/neck coil (n = 27), (2) lumbar or thoracic spine MRI using only the in-table spine coils (n = 10), (3) hip MRI using a large flex coil (n = 8) and (4) upper- or lower-extremity MRI using small flex coils (n = 5). Following the MRI examinations, patients evaluated (1) sense of space, (2) noise level, (3) comfort, (4) coil comfort and (5) overall examination impression on a 5-point Likert-scale (range: 1= “much worse” to 5 = “much better”) using a questionnaire. Maximum noise levels of all performed imaging studies were measured in decibels (dB) by a sound level meter placed in the bore center. Results: Sense of space was perceived to be “better” or “much better” by 84% of patients for imaging examinations performed on the 0.55T MRI scanner system (mean score: 4.34 ± 0.75). Additionally, 84% of patients rated noise levels as “better” or “much better” when imaged on the low-field scanner system (mean score: 3.90 ± 0.61). Overall sensation during the imaging examination at 0.55T was rated as “better” or “much better” by 78% of patients (mean score: 3.96 ± 0.70). Quantitative assessment showed significantly reduced maximum noise levels for all 0.55T MRI studies, regardless of body region compared to 1.5T, i.e., brain MRI (83.8 ± 3.6 dB vs. 89.3 ± 5.4 dB; p = 0.04), spine MRI (83.7 ± 3.7 dB vs. 89.4 ± 2.6 dB; p = 0.004) and hip MRI (86.3 ± 5.0 dB vs. 89.1 ± 1.4 dB; p = 0.04). Conclusions: Patients perceived 0.55T new-generation low-field MRI to be more comfortable than conventional 1.5T MRI, given its larger bore opening and reduced noise levels during image acquisition. Therefore, new concepts regarding bore design and noise level reduction of MR scanner systems may help to reduce patient anxiety and improve well-being when undergoing MR imaging
Disentangling the impact of cerebrospinal fluid formation and neuronal activity on solute clearance from the brain
No full textBackground: Despite recent attention, pathways and mechanisms of fluid transposition in the brain are still a matter of intense discussion and driving forces underlying waste clearance in the brain remain elusive. Consensus exists that net solute transport is a prerequisite for efficient clearance. The individual impact of neuronal activity and cerebrospinal fluid (CSF) formation, which both vary with brain state and anesthesia, remain unclear. Methods: To separate conditions with high and low neuronal activity and high and low CSF formation, different anesthetic regimens in naive rat were established, using Isoflurane (ISO), Medetomidine (MED), acetazolamide or combinations thereof. With dynamic contrast-enhanced MRI, after application of low molecular weight contrast agent (CA) Gadobutrol to cisterna magna, tracer distribution was monitored as surrogate for solute clearance. Simultaneous fiber-based Ca2+-recordings informed about the state of neuronal activity under different anesthetic regimen. T2-weighted MRI and diffusion-weighted MRI (DWI) provided size of subarachnoidal space and aqueductal flow as surrogates for CSF formation. Finally, a pathway and mechanism-independent two-compartment model was introduced to provide a measure of efficiency for solute clearance from the brain. Results: Anatomical imaging, DWI and Ca2+-recordings confirmed that conditions with distinct levels of neuronal activity and CSF formation were achieved. A sleep-resembling condition, with reduced neuronal activity and enhanced CSF formation was achieved using ISO+MED and an awake-like condition with high neuronal activity using MED alone. CA distribution in the brain correlated with the rate of CSF formation. The cortical brain state had major influence on tracer diffusion. Under conditions with low neuronal activity, higher diffusivity suggested enlargement of extracellular space, facilitating a deeper permeation of solutes into brain parenchyma. Under conditions with high neuronal activity, diffusion of solutes into parenchyma was hindered and clearance along paravascular pathways facilitated. Exclusively based on the measured time signal curves, the two-compartment model provided net exchange ratios, which were significantly larger for the sleep-resembling condition than for the awake-like condition. Conclusions: Efficiency of solute clearance in brain changes with alterations in both state of neuronal activity and CSF formation. Our clearance pathway and mechanism agnostic kinetic model informs about net solute transport, solely based on the measured time signal curves. This rather simplifying approach largely accords with preclinical and clinical findings
Emergency Presentations for Dizziness—Radiological Findings, Final Diagnoses, and Mortality
No full textBackground. Dizziness is a frequent presentation in patients presenting to emergency departments (EDs), often triggering extensive work-up, including neuroimaging. Therefore, gathering knowledge on final diagnoses and outcomes is important. We aimed to describe the incidence of dizziness as primary or secondary complaint, to list final diagnoses, and to determine the use and yield of neuroimaging and outcomes in these patients. Methods. Secondary analysis of two observational cohort studies, including all patients presenting to the ED of the University Hospital of Basel from 30th January 2017–19th February 2017 and from 18th March 2019–20th May 2019. Baseline demographics, Emergency Severity Index (ESI), hospitalization, admission to Intensive Care Units (ICUs), and mortality were extracted from the electronic health record database. At presentation, patients underwent a structured interview about their symptoms, defining their primary and secondary complaints. Neuroimaging results were obtained from the picture archiving and communication system (PACS). Patients were categorized into three non-overlapping groups: dizziness as primary complaint, dizziness as secondary complaint, and absence of dizziness. Results. Of 10076 presentations, 232 (2.3%) indicated dizziness as their primary and 984 (9.8%) as their secondary complaint. In dizziness as primary complaint, the three (out of 73 main conditions defined) main diagnoses were nonspecific dizziness (47, 20.3%), dysfunction of the peripheral vestibular system (37, 15.9%), as well as somatization, depression, and anxiety (20, 8.6%). 104 of 232 patients (44.8%) underwent neuroimaging, with relevant findings in 5 (4.8%). In dizziness as primary complaint 30-day mortality was 0%. Conclusion. Work-up for dizziness in emergency presentations has to consider a broad differential diagnosis, but due to the low yield, it should include neuroimaging only in few and selected cases, particularly with additional neurological abnormalities. Presentation with primary dizziness carries a generally favorable prognosis lacking short-term mortality.
TotalSegmentator: robust segmentation of 104 anatomical structures in CT images
No full textWe present a deep learning segmentation model that can automatically and
robustly segment all major anatomical structures in body CT images. In this
retrospective study, 1204 CT examinations (from the years 2012, 2016, and 2020)
were used to segment 104 anatomical structures (27 organs, 59 bones, 10
muscles, 8 vessels) relevant for use cases such as organ volumetry, disease
characterization, and surgical or radiotherapy planning. The CT images were
randomly sampled from routine clinical studies and thus represent a real-world
dataset (different ages, pathologies, scanners, body parts, sequences, and
sites). The authors trained an nnU-Net segmentation algorithm on this dataset
and calculated Dice similarity coefficients (Dice) to evaluate the model's
performance. The trained algorithm was applied to a second dataset of 4004
whole-body CT examinations to investigate age dependent volume and attenuation
changes. The proposed model showed a high Dice score (0.943) on the test set,
which included a wide range of clinical data with major pathologies. The model
significantly outperformed another publicly available segmentation model on a
separate dataset (Dice score, 0.932 versus 0.871, respectively). The aging
study demonstrated significant correlations between age and volume and mean
attenuation for a variety of organ groups (e.g., age and aortic volume; age and
mean attenuation of the autochthonous dorsal musculature). The developed model
enables robust and accurate segmentation of 104 anatomical structures. The
annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit
(https://www.github.com/wasserth/TotalSegmentator) are publicly available.Comment: Accepted at Radiology: Artificial Intelligenc
Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI
No full textPurpose: To develop an open-source nnU-Net-based AI model for combined detection and segmentation of unruptured intracranial aneurysms (UICA) in 3D TOF-MRI, and compare models trained on datasets with aneurysm-like differential diagnoses. Methods: This retrospective study (2020-2023) included 385 anonymized 3D TOF-MRI images from 364 patients (mean age 59 years, 60% female) at multiple centers plus 113 subjects from the ADAM challenge. Images featured untreated or possible UICAs and differential diagnoses. Four distinct training datasets were created, and the nnU-Net framework was used for model development. Performance was assessed on a separate test set using sensitivity and False Positive (FP)/case rate for detection, and DICE score and NSD (Normalized Surface Distance) with a 0.5mm threshold for segmentation. Statistical analysis included chi-square, Mann-Whitney-U, and Kruskal-Wallis tests, with significance set at p < 0.05. Results: Models achieved overall sensitivity between 82% and 85% and a FP/case rate of 0.20 to 0.31, with no significant differences (p = 0.90 and p = 0.16). The primary model showed 85% sensitivity and 0.23 FP/case rate, outperforming the ADAM-challenge winner (61%) and a nnU-Net trained on ADAM data (51%) in sensitivity (p < 0.05). It achieved a mean DICE score of 0.73 and an NSD of 0.84 for correctly detected UICA. Conclusions: Our open-source, nnU-Net-based AI model (available at 10.5281/zenodo.13386859) demonstrates high sensitivity, low false positive rates, and consistent segmentation accuracy for UICA detection and segmentation in 3D TOF-MRI, suggesting its potential to improve clinical diagnosis and for monitoring of UICA.14 pages, 5 figures, 3 tables, 2 supplementary table
Redefining MRI-Based Skull Segmentation Through AI-Driven Multimodal Integration
No full textSkull segmentation in magnetic resonance imaging (MRI) is essential for cranio-maxillofacial (CMF) surgery planning, yet manual approaches are time-consuming and error-prone. Computed tomography (CT) provides superior bone contrast but exposes patients to ionizing radiation, which is particularly concerning in pediatric care. This study presents an AI-based workflow that enables skull segmentation directly from routine MRI. Using 186 paired CT–MRI datasets, CT-based segmentations were transferred to MRI via multimodal registration to train dedicated deep learning models. Performance was evaluated against manually segmented CT ground truth using Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD), and Hausdorff Distance (HD). AI achieved higher performance on CT (DSC 0.981) than MRI (DSC 0.864), with MSD and HD also favoring CT. Despite lower absolute accuracy on MRI, the approach substantially improved segmentation quality compared with manual MRI methods, particularly in clinically relevant regions. This automated method enables accurate skull modeling from standard MRI without radiation exposure or specialized sequences. While CT remains more precise, the presented framework enhances MRI utility in surgical planning, reduces manual workload, and supports safer, patient-specific treatment, especially for pediatric and trauma cases
Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
No full textPericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016–01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48–99.38%) and 100.00% (95% CI 96.38–100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904–0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available
Deep Learning Reconstructed New-Generation 0.55 T MRI of the Knee-A Prospective Comparison With Conventional 3 T MRI
No full textOBJECTIVES
The aim of this study was to compare deep learning reconstructed (DLR) 0.55 T magnetic resonance imaging (MRI) quality, identification, and grading of structural anomalies and reader confidence levels with conventional 3 T knee MRI in patients with knee pain following trauma.
MATERIALS AND METHODS
This prospective study of 26 symptomatic patients (5 women) includes 52 paired DLR 0.55 T and conventional 3 T MRI examinations obtained in 1 setting. A novel, commercially available DLR algorithm was employed for 0.55 T image reconstruction. Four board-certified radiologists reviewed all images independently and graded image quality, noted structural anomalies and their respective reporting confidence levels for the presence or absence, as well as grading of bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05, significant), and MRI findings were correlated between 0.55 T and 3 T MRI using Cohen kappa (kappa).
RESULTS
In reader's consensus, good image quality was found for DLR 0.55 T MRI and 3 T MRI (3.8 vs 4.1/5 points, P = 0.06). There was near-perfect agreement between 0.55 T DLR and 3 T MRI regarding the identification of structural anomalies for all readers (each kappa >/= 0.80). Substantial to near-perfection agreement between 0.55 T and 3 T MRI was reported for grading of cartilage (kappa = 0.65-0.86) and meniscus lesions (kappa = 0.71-1.0). High confidence levels were found for all readers for DLR 0.55 T and 3 T MRI, with 3 readers showing higher confidence levels for reporting cartilage lesions on 3 T MRI.
CONCLUSIONS
In conclusion, new-generation 0.55 T DLR MRI provides good image quality, comparable to conventional 3 T MRI, and allows for reliable identification of internal derangement of the knee with high reader confidence
TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images
No full textPurpose: To develop an open-source and easy-to-use segmentation model that
can automatically and robustly segment most major anatomical structures in MR
images independently of the MR sequence.
Materials and Methods: In this study we extended the capabilities of
TotalSegmentator to MR images. 298 MR scans and 227 CT scans were used to
segment 59 anatomical structures (20 organs, 18 bones, 11 muscles, 7 vessels, 3
tissue types) relevant for use cases such as organ volumetry, disease
characterization, and surgical planning. The MR and CT images were randomly
sampled from routine clinical studies and thus represent a real-world dataset
(different ages, pathologies, scanners, body parts, sequences, contrasts, echo
times, repetition times, field strengths, slice thicknesses and sites). We
trained an nnU-Net segmentation algorithm on this dataset and calculated Dice
similarity coefficients (Dice) to evaluate the model's performance.
Results: The model showed a Dice score of 0.824 (CI: 0.801, 0.842) on the
test set, which included a wide range of clinical data with major pathologies.
The model significantly outperformed two other publicly available segmentation
models (Dice score, 0.824 versus 0.762; p<0.001 and 0.762 versus 0.542;
p<0.001). On the CT image test set of the original TotalSegmentator paper it
almost matches the performance of the original TotalSegmentator (Dice score,
0.960 versus 0.970; p<0.001).
Conclusion: Our proposed model extends the capabilities of TotalSegmentator
to MR images. The annotated dataset
(https://zenodo.org/doi/10.5281/zenodo.11367004) and open-source toolkit
(https://www.github.com/wasserth/TotalSegmentator) are publicly available
