Morbidity and mortality in spontaneous intracerebral hemorrhage (ICH) are correlated with hematoma progression. We hypothesized that the presence of tiny, enhancing foci ("spot sign") within acute ...hematomas is associated with hematoma expansion.
We prospectively studied 39 consecutive patients with spontaneous ICH by computed tomography angiography within 3 hours of symptom onset. Scans were reviewed by 3 readers. Patients were dichotomized according to the presence or absence of the spot sign. Clinical and radiological outcomes were compared between groups. The predictive value of this sign was assessed in a multivariate analysis.
Thirteen patients (33%) demonstrated 31 enhancing foci. Baseline clinical variables were similar in both groups. Hematoma expansion occurred in 11 patients (28%) on follow-up. Seventy-seven percent of patients with and 4% without hematoma expansion demonstrated the spot sign (P<0.0001). Sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratio for expansion were 91%, 89%, 77%, 96%, and 8.5, respectively. Interobserver agreement was high (kappa=0.92 to 0.94). In patients with the spot sign, mean volume change was greater (P=0.008), extravasation more common (P=0.0005), and median hospital stay longer (P=0.04), and fewer patients achieved a good outcome (modified Rankin Scale score <2), although the latter was not significant (P=0.16). No differences in hydrocephalus (P=1.00), surgical intervention (P=1.00), or death (P=0.60) were noted between groups. In multiple regression, the spot sign independently predicted hematoma expansion (P=0.0003).
The computed tomography angiography spot sign is associated with the presence and extent of hematoma progression. Fewer patients achieve a good clinical outcome and hospital stay was longer. Further studies are warranted to validate the ability of this sign to predict clinical outcomes.
Introduction
The objective of this study was to determine the radiation dose delivered during comprehensive computed tomography (CT) imaging for acute stroke.
Methods
All CT examinations performed ...over 18 months using our acute stroke protocol were included. Protocol includes an unenhanced CT head, CT angiography from the arch to vertex, CT perfusion/permeability, and an enhanced CT head. All imaging was acquired with a 64-MDCT. Examinations where any element of the protocol was repeated or omitted due to mistimed injection or patient motion were excluded. Dose-length products (DLP) for all components of each examination were obtained from dose reports generated at the time of acquisition, separating neck, and head calculations. Effective doses for each examination were calculated using the DLP and normalized values of effective dose per DLP appropriate for the body regions imaged.
Results
Ninety-five examinations were included. Mean DLP was 6,790.0 mGy·cm. Effective doses ranged from 11.8 to 27.3 mSv, mean effective dose of 16.4 mSv. Mean effective dose for acquisition of the unenhanced head was 2.7 mSv. Largest contribution to effective dose was the CTA with a mean effective dose of 5.4 mSv. Mean effective dose for the CT perfusion was 4.9 mSv.
Conclusion
A comprehensive CT acute stroke protocol delivered a mean effective dose of 16.4 mSv, which is approximately six times the dose of an unenhanced CT head. These high-dose results must be balanced with the benefits of the detailed anatomic and physiologic data obtained. Centers should implement aggressive dose reduction strategies and freely use MR as a substitute.
Nine- and 24-point prediction scores have recently been published to predict hematoma expansion (HE) in acute intracerebral hemorrhage. We sought to validate these scores and perform an independent ...analysis of HE predictors.
We retrospectively studied 301 primary or anticoagulation-associated intracerebral hemorrhage patients presenting <6 hours post ictus prospectively enrolled in the Predicting Hematoma Growth and Outcome in Intracerebral Hemorrhage Using Contrast Bolus Computed Tomography (PREDICT) study. Patients underwent baseline computed tomography angiography and 24-hour noncontrast computed tomography follow-up for HE analysis. Discrimination and calibration of the 9- and 24-point scores was assessed. Independent predictors of HE were identified using multivariable regression and incorporated into the PREDICT A/B scores, which were then compared with existing scores.
The 9- and 24-point HE scores demonstrated acceptable discrimination for HE>6 mL or 33% and >6 mL, respectively (area under the curve of 0.706 and 0.755, respectively). The 24-point score demonstrated appropriate calibration in the PREDICT cohort (χ2 statistic, 11.5; P=0.175), whereas the 9-point score demonstrated poor calibration (χ2 statistic, 34.3; P<0.001). Independent HE predictors included spot sign number, time from onset, warfarin use or international normalized ratio>1.5, Glasgow Coma Scale, and National Institutes of Health Stroke Scale and were included in PREDICT A/B scores. PREDICT A showed improved discrimination compared with both existing scores, whereas performance of PREDICT B varied by definition of expansion.
The 9- and 24-point expansion scores demonstrate acceptable discrimination in an independent multicenter cohort; however, calibration was suboptimal for the 9-point score. The PREDICT A score showed improved discrimination for HE prediction but requires independent validation.
Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in ...large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.
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•Developed open-source benchmarking platform and metrics for robustness of DNNs.•Quantified sensitivity of DNNs to OOD data on three neuroimaging segmentation tasks.•Modern CNNs are highly susceptible to distribution shift, corruptions and artifacts.•Simple augmentation strategies improve robustness for anatomical segmentation tasks.•Vision transformers exhibit improved robustness over FCNs.
Magnetic Resonance Imaging Exposure During Pregnancy Symons, Sean P; Heyn, Chris; Maralani, Pejman Jabehdar
JAMA : the journal of the American Medical Association,
12/2016, Letnik:
316, Številka:
21
Journal Article
Integrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam ...radiotherapy (EBRT) in one of North America's largest radiotherapy programs.
An MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years.
From July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%).
Detailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program.
To determine whether admission computed tomography (CT) perfusion-derived permeability-surface area product (PS) maps differ between patients with hemorrhagic acute stroke and those with ...nonhemorrhagic acute stroke.
This prospective study was institutional review board approved, and all participants gave written informed consent. Forty-one patients who presented with acute stroke within 3 hours after stroke symptom onset underwent two-phase CT perfusion imaging, which enabled PS measurement. Patients were assigned to groups according to whether they had hemorrhage transformation (HT) at follow-up magnetic resonance (MR) imaging and CT and/or whether they received tissue plasminogen activator (TPA) treatment. Clinical, demographic, and CT perfusion variables were compared between the HT and non-HT patient groups. Associations between PS and HT were tested at univariate and multivariate logistic regression analyses and receiver operating characteristic (ROC) analysis.
HT developed in 23 (56%) patients. Patients with HT had higher National Institutes of Health Stroke Scale (NIHSS) scores (P = .005), poorer outcomes (P = .001), and a higher likelihood of having received TPA (P = .005) compared with patients without HT. Baseline blood flow (P = .17) and blood volume (P = .11) defects and extent of flow reduction (P = .27) were comparable between the two groups. The mean PS for the HT group, 0.49 mL x min(-1) x (100 g)(-1), was significantly higher than that for the non-HT group, 0.09 mL x min(-1) x (100 g)(-1) (P < .0001). PS (odds ratio, 3.5; 95% confidence interval CI: 1.69, 7.06; P = .0007) and size of hypoattenuating area at nonenhanced admission CT (odds ratio, 0.4; 95% CI: 0.2, 0.7; P = .002) were the only independent variables associated with HT at stepwise multivariate analysis. The mean area under the ROC curve was 0.918 (95% CI: 0.828, 1.00). The PS threshold of 0.23 mL x min(-1) x (100 g)(-1) had 77% sensitivity and 94% specificity for detection of HT.
Admission PS measurement appears promising for distinguishing patients with acute stroke who are likely from those who are not likely to develop HT.
http://radiology.rsnajnls.org/cgi/content/full/250/3/867/DC1.
The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the ...raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Cochlear implant (CI) insertion depth can affect residual hearing preservation, tonotopic range coverage, and Mapping. Therefore, determining insertion depth has the potential to maximize CI ...performance. A post-op skull X-RAY is commonly used to assess insertion depth, however its effectiveness has not been well established. Our primary objective was to assess the accuracy of post-op skull X-RAYs to determine insertion depth, compared to CT as the gold standard. Secondary objectives were to compare experience level of raters and different skull X-RAY views.
Thirteen patients with Advanced Bionic HiRes 90 K implants, and post-operative temporal bone CT scans were selected from the CI database at Sunnybrook Health Sciences Centre. Medical students, otology fellows, and CI surgeons evaluated insertion depths on post-op skull X-RAYs, while neuroradiologists evaluated CT scans. Descriptive statistics, regression analysis, and paired t-tests were used to compare the two types of imaging.
X-RAYs and CTs provided an equivalent mean insertion depth of 337 degrees (p = 0.93), a mean difference of - 0.9 degrees and a standard deviation of paired differences of 43 degrees. Although means were similar across rater groups, CI surgeons (45 degrees) had the lowest standard deviation of paired differences. Comparing X-RAY views, Caldwell (29 degrees) had less variation than Towne (59 degrees) for standard deviation of paired differences.
Skull X-RAYs provide accurate and reliable measurements for CI insertion depth. The Caldwell view alone may be sufficient for evaluations of insertion depth, and experience has a minor impact on the variability of estimates.
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