Multicenter Study of Long-Term Outcomes and Quality of Life in PHACE Syndrome after Age 10

OBJECTIVE

To characterize long-term outcomes of PHACE syndrome.

STUDY DESIGN

Multicenter study with cross-sectional interviews and chart review of individuals with definite PHACE syndrome ≥10 years of age. Data from charts were collected across multiple PHACE-related topics. Data not available in charts were collected from patients directly. Likert scales were used to assess the impact of specific findings. Patient-Reported Outcomes Measurement Information System (PROMIS) scales were used to assess quality of life domains.

RESULTS

A total of 104/153 (68%) individuals contacted participated in the study at a median of 14 years of age (range 10-77 years). There were infantile hemangioma (IH) residua in 94.1%. Approximately one-half had received laser treatment for residual IH, and the majority (89.5%) of participants were satisfied or very satisfied with the appearance. Neurocognitive manifestations were common including headaches/migraines (72.1%), participant-reported learning differences (45.1%), and need for individualized education plans (39.4%). Cerebrovascular arteriopathy was present in 91.3%, with progression identified in 20/68 (29.4%) of those with available follow-up imaging reports. Among these, 6/68 (8.8%) developed moyamoya vasculopathy or progressive stenoocclusion, leading to isolated circulation at or above the level of the circle of Willis. Despite the prevalence of cerebrovascular arteriopathy, the proportion of those with ischemic stroke was low (2/104; 1.9%). PROMIS global health scores were lower than population norms by at least 1 SD.

CONCLUSIONS

PHACE syndrome is associated with long-term, mild to severe morbidities including IH residua, headaches, learning differences, and progressive arteriopathy. Primary and specialty follow-up care is critical for PHACE patients into adulthood.

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset

Supplemental material is available for this article.

Altered Iron and Microstructure in Huntington's Disease Subcortical Nuclei: Insight From 7T MRI

BACKGROUND

Pathophysiological changes of Huntington's disease (HD) can precede symptom onset by decades. Robust imaging biomarkers are needed to monitor HD progression, especially before the clinical onset.

PURPOSE

To investigate iron dysregulation and microstructure alterations in subcortical regions as HD imaging biomarkers, and to associate such alterations with motor and cognitive impairments.

STUDY TYPE

Prospective.

POPULATION

Fourteen individuals with premanifest HD (38.0 ± 11.0 years, 9 females; far-from-onset N = 6, near-onset N = 8), 21 manifest HD patients (49.1 ± 12.1 years, 11 females), and 33 age-matched healthy controls (43.9 ± 12.2 years, 17 females).

FIELD STRENGTH/SEQUENCE

7 T, T1 -weighted imaging, quantitative susceptibility mapping, and diffusion tensor imaging.

ASSESSMENT

Volume, susceptibility, fractional anisotropy (FA), and mean diffusivity (MD) within subcortical brain structures were compared across groups, used to establish HD classification models, and correlated to clinical measures and cognitive assessments.

STATISTICAL TESTS

Generalized linear model, multivariate logistic regression, receiver operating characteristics with the area under the curve (AUC), and likelihood ratio test comparing a volumetric model to one that also includes susceptibility and diffusion metrics, Wilcoxon paired signed-rank test, and Pearson's correlation. A P-value <0.05 after Benjamini-Hochberg correction was considered statistically significant.

RESULTS

Significantly higher striatal susceptibility and FA were found in premanifest and manifest HD preceding atrophy, even in far-from-onset premanifest HD compared to controls (putamen susceptibility: 0.027 ± 0.022 vs. 0.018 ± 0.013 ppm; FA: 0.358 ± 0.048 vs. 0.313 ± 0.039). The model with additional susceptibility, FA, and MD features showed higher AUC compared to volume features alone when differentiating premanifest HD from HC (0.83 vs. 0.66), and manifest from premanifest HD (0.94 vs. 0.83). Higher striatal susceptibility significantly correlated with cognitive deterioration in HD (executive function: r = -0.600; socioemotional function: r = -0.486).

DATA CONCLUSION

7 T MRI revealed iron dysregulation and microstructure alterations with HD progression, which could precede volume loss, provide added value to HD differentiation, and might be associated with cognitive changes.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI

BACKGROUND

Although susceptibility-weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI contrast from T2*-weighted magnitude images is therefore advantageous.

PURPOSE

To create synthetic SWI images from clinical T2*-weighted magnitude images using deep learning and evaluate the resulting images in terms of similarity to conventional SWI images and ability to detect radiation-associated CMBs.

STUDY TYPE

Retrospective.

POPULATION

A total of 145 adults (87 males/58 females; 43.9 years old) with radiation-associated CMBs were used to train (16,093 patches/121 patients), validate (484 patches/4 patients), and test (2420 patches/20 patients) our networks.

FIELD STRENGTH/SEQUENCE

3D T2*-weighted, gradient-echo acquired at 3 T.

ASSESSMENT

Structural similarity index (SSIM), peak signal-to-noise-ratio (PSNR), normalized mean-squared-error (nMSE), CMB counts, and line profiles were compared among magnitude, original SWI, and synthetic SWI images. Three blinded raters (J.E.V.M., M.A.M., B.B. with 8-, 6-, and 4-years of experience, respectively) independently rated and classified test-set images.

STATISTICAL TESTS

Kruskall-Wallis and Wilcoxon signed-rank tests were used to compare SSIM, PSNR, nMSE, and CMB counts among magnitude, original SWI, and predicted synthetic SWI images. Intraclass correlation assessed interrater variability. P values <0.005 were considered statistically significant.

RESULTS

SSIM values of the predicted vs. original SWI (0.972, 0.995, 0.9864) were statistically significantly higher than that of the magnitude vs. original SWI (0.970, 0.994, 0.9861) for whole brain, vascular structures, and brain tissue regions, respectively; 67% (19/28) CMBs detected on original SWI images were also detected on the predicted SWI, whereas only 10 (36%) were detected on magnitude images. Overall image quality was similar between the synthetic and original SWI images, with less artifacts on the former.

CONCLUSIONS

This study demonstrated that deep learning can increase the susceptibility contrast present in neurovasculature and CMBs on T2*-weighted magnitude images, without residual susceptibility-induced artifacts. This may be useful for more accurately estimating CMB burden from magnitude images alone.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Correlating Quantitative MRI-based Apparent Diffusion Coefficient Metrics with 24-month Neurodevelopmental Outcomes in Neonates from the HEAL Trial

Background Multiple qualitative scoring systems have been created to capture the imaging severity of hypoxic ischemic brain injury. Purpose To evaluate quantitative volumes of acute brain injury at MRI in neonates with hypoxic ischemic brain injury and correlate these findings with 24-month neurodevelopmental outcomes and qualitative brain injury scoring by radiologists. Materials and Methods In this secondary analysis, brain diffusion-weighted MRI data from neonates in the High-dose Erythropoietin for Asphyxia and Encephalopathy trial, which recruited participants between January 2017 and October 2019, were analyzed. Volume of acute brain injury, defined as brain with apparent diffusion coefficient (ADC) less than 800 × 10-6 mm2/sec, was automatically computed across the whole brain and within the thalami and white matter. Outcomes of death and neurodevelopmental impairment (NDI) were recorded at 24-month follow-up. Associations between the presence and volume (in milliliters) of acute brain injury with 24-month outcomes were evaluated using multiple logistic regression. The correlation between quantitative acute brain injury volume and qualitative MRI scores was assessed using the Kendall tau-b test. Results A total of 416 neonates had available MRI data (mean gestational age, 39.1 weeks ± 1.4 [SD]; 235 male) and 113 (27%) showed evidence of acute brain injury at MRI. Of the 387 participants with 24-month follow-up data, 185 (48%) died or had any NDI. Volume of acute injury greater than 1 mL (odds ratio [OR], 13.9 [95% CI: 5.93, 32.45]; P < .001) and presence of any acute injury in the brain (OR, 4.5 [95% CI: 2.6, 7.8]; P < .001) were associated with increased odds of death or any NDI. Quantitative whole-brain acute injury volume was strongly associated with radiologists' qualitative scoring of diffusion-weighted images (Kendall tau-b = 0.56; P < .001). Conclusion Automated quantitative volume of brain injury is associated with death, moderate to severe NDI, and cerebral palsy in neonates with hypoxic ischemic encephalopathy and correlated well with qualitative MRI scoring of acute brain injury. Clinical trial registration no. NCT02811263 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Huisman in this issue.

Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF. Engineering and usability challenges and results from 3 use cases are presented. A generalized validation system based on free, open-source software (OSS) was implemented, connecting clinical imaging modalities, the Picture Archiving and Communication System (PACS), and an ML inference server. ML pipelines were implemented in NVIDIA's Clara Deploy framework with results and clinician feedback stored in a customized XNAT instance, separate from the clinical record but linked from within PACS. Prospective clinical validation studies of 3 ML models were conducted, with data routed from multiple clinical imaging modalities and PACS. Completed validation studies provided expert clinical feedback on model performance and usability, plus system reliability and performance metrics. Clinical validation of ML models entails assessing model performance, impact on clinical infrastructure, robustness, and usability. Study results must be easily accessible to participating clinicians but remain outside the clinical record. Building a system that generalizes and scales across multiple ML models takes the concerted effort of software engineers, clinicians, data scientists, and system administrators, and benefits from the use of modular OSS. The present work provides a template for institutions looking to translate and clinically validate ML models in the clinic, together with required resources and expected challenges.

What referring clinicians value most: Accuracy of radiology results and personal interactions with radiologists

PURPOSE

We sought to identify which aspects of the referring clinician experience are most strongly correlated with overall satisfaction, and hence of greatest relevant importance to referring clinicians.

METHODS

A survey instrument assessing referring clinician satisfaction throughout 11 domains of the radiology process map was distributed 2720 clinicians. The survey contained sections assessing each process map domain, with each section including a question about satisfaction overall in that domain and multiple more granular questions. The final question on the survey was overall satisfaction with the department. Univariate logistic regression and multivariate logistic regression were performed to assess the association between individual survey questions and overall satisfaction with the department.

RESULTS

729 referring clinicians (27%) completed the survey. Using univariate logistic regression nearly every question was associated with overall satisfaction. Amongst the 11 domains of the radiology process map multivariate logistic regression identified the following as mostly strongly associated with overall satisfaction: results/reporting overall (odds ratio 4.71; 95% confidence interval 2.15-10.23), section with which work most closely overall (3.39; 1.28-8.64), and inpatient radiology overall (2.39; 1.08-5.08). Other survey questions associated with overall satisfaction on multivariate logistic regression were attending radiologist interactions (odds ratio 3.71; 95% confidence interval 1.54-8.69), timeliness of inpatient radiology results (2.91; 1.01-8.09), technologist interactions (2.15; 0.99-4.40), appointment availability for urgent outpatient studies (2.01; 1.08-3.64), and guidance for selecting correct imaging study (1.88; 1.04-3.34).

CONCLUSION

Referring clinicians value most the accuracy of the radiology report and their interactions with attending radiologists, particularly within the section they work most closely.

Ecodesign and Operational Strategies to Reduce the Carbon Footprint of MRI for Energy Cost Savings

Background Radiology is a major contributor to health care's climate footprint due to energy-intensive devices, particularly MRI which uses the most energy. Purpose To determine the energy, cost, and carbon savings that could be achieved through different scanner power management strategies. Materials and Methods In this retrospective evaluation, four outpatient MRI scanners from three vendors were individually equipped with power meters (1-Hz sampling rate). Power measurement logs were extracted over 39 days. Data were segmented into off, idle, prepared-to-scan, scan, or power-save modes for each scanner. Energy, cost (assuming a mean cost of $0.14 per kilowatt hour), and carbon savings were calculated for the lowest scanner activity modes. Data was summarized using descriptive statistics and 95% confidence intervals. Results Projected annual energy consumption per scanner ranged from 82.7-171.1 megawatt-hours (MWh), with 72-91% defined as nonproductive. Power draws for each mode were measured as 6.4 ± 0.1 kW (power-save), 7.3 ± 0.6 kW to 9.7 ± 0.2 kW (off), 9.5 ± 0.9 to 14.5 ± 0.5 kW (idle), 17.3 ± 0.5 to 25.6 ± 0.6 kW (prepared-to-scan), and 28.6 ± 8.6 to 48.3 ± 11.8 kW (scan). Switching MRIs from idle to off mode for 12 overnight hours reduced power consumption by 25-33%, translating to a potential annual savings of 12.3-21.0 MWh, $1717-$2943 U.S. dollars, and 8.7-14.9 metric tons of CO2-equivalent (MTCO2eq). The power-save mode further reduced consumption by 22-28% compared to off mode, potentially saving an additional 8.8-11.4 MWh, $1,226-$1,594 U.S. dollars, and 6.2-8.1 MTCO2eq per year for 12 hours overnight. Implementation of a power-save mode for 12 hours overnight on all U.S. outpatient MRI in the U.S. could save U.S. health care 58,863.2-76,288.2 MWh, $8.2-$10.7 million U.S. dollars, and 41,606.4-54,088.3 MTCO2eq. Conclusion Powering down MRIs can make radiology departments more energy-efficient and gain substantial sustainability and cost benefits. See also the editorial by Vosshenrich and Heye.

Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural MR images

Objective:Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction.Approach:We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image.Main results:Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction.Significance:Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.

Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease

Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD. We compared an iterative least-squares-based method (iLSQR), iterative methods that use regularization, single-step approaches, and deep learning-based techniques. Their performance was evaluated by comparing: (1) deviations from a multiple-orientation QSM reference; (2) visual appearance of QSM maps and the presence of artifacts; (3) susceptibility in subcortical brain regions with age; (4) regional brain susceptibility with published postmortem brain iron quantification; and (5) susceptibility in HD-affected basal ganglia regions between HD subjects and healthy controls. We found that single-step QSM methods with either total variation or total generalized variation constraints (SSTV/SSTGV) and the single-step deep learning method iQSM generally provided the best performance in terms of correlation with iron deposition and were better at differentiating between healthy controls and premanifest HD individuals, while deep learning QSM methods trained with multiple-orientation susceptibility data created QSM maps that were most similar to the multiple orientation reference and with the best visual scores.

Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning

BACKGROUND AND PURPOSE

Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages.

MATERIALS AND METHODS

This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains.

RESULTS

Fetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson r = 0.996, P < .001). Normative samples of intracranial and brain volumes across gestational ages were developed. Eight of 9 pathologic fetal intracranial volumes were automatically predicted to be >2 SDs from this age-specific reference mean. There were no effects of fetal sex, maternal diabetes, or maternal age on intracranial or brain volumes across gestational ages.

CONCLUSIONS

Deep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard.

Survey of the American Society of Neuroradiology Membership on the Use and Value of Extracranial Carotid Vessel Wall MRI

BACKGROUND AND PURPOSE

Extracranial vessel wall MRI (EC-VWI) contributes to vasculopathy characterization. This survey study investigated EC-VWI adoption by American Society of Neuroradiology (ASNR) members and indications and barriers to implementation.

MATERIALS AND METHODS

The ASNR Vessel Wall Imaging Study Group survey on EC-VWI use, frequency, applications, MR imaging systems and field strength used, protocol development approaches, vendor engagement, reasons for not using EC-VWI, ordering provider interest, and impact on clinical care was distributed to the ASNR membership between April 2, 2019, to August 30, 2019.

RESULTS

There were 532 responses; 79 were excluded due to minimal, incomplete response and 42 due to redundant institutional responses, leaving 411 responses. Twenty-six percent indicated that their institution performed EC-VWI, with 66.3% performing it ≤1-2 times per month, most frequently on 3T MR imaging, with most using combined 3D and 2D protocols. Protocols most commonly included pre- and postcontrast T1-weighted imaging, TOF-MRA, and contrast-enhanced MRA. Inflammatory vasculopathy (63.3%), plaque vulnerability assessments (61.1%), intraplaque hemorrhage (61.1%), and dissection-detection/characterization (51.1%) were the most frequent applications. For those not performing EC-VWI, the reasons were a lack of ordering provider interest (63.9%), lack of radiologist time/interest (47.5%) or technical support (41.4%) for protocol development, and limited interpretation experience (44.9%) and knowledge of clinical applications (43.7%). Reasons given by 46.9% were that no providers approached radiology with interest in EC-VWI. If barriers were overcome, 51.1% of those not performing EC-VWI indicated they would perform it, and 40.6% were unsure; 48.6% did not think that EC-VWI had impacted patient management at their institution.

CONCLUSIONS

Only 26% of neuroradiology groups performed EC-VWI, most commonly due to limited clinician interest. Improved provider and radiologist education, protocols, processing techniques, technical support, and validation trials could increase adoption.

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset

Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.

Functional network alterations in young brain tumor patients with radiotherapy-induced memory impairments and vascular injury

Background

Cognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.

Methods

Nineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.

Results

Compared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34–0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.

Conclusions

MR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 patients with glioblastoma, 72 patients with astrocytoma, and 74 patients with oligodendroglioma) who underwent two consecutive multimodal MRI examinations were randomly selected into training (n = 198) and testing (n = 100) samples. A posttreatment tumor segmentation three-dimensional nnU-Net convolutional neural network with multichannel inputs (T1, T2, and T1 postcontrast and fluid-attenuated inversion recovery [FLAIR]) was trained to segment three multiclass tissue types (peritumoral edematous, infiltrated, or treatment-changed tissue [ED]; active tumor or enhancing tissue [AT]; and necrotic core). Separate longitudinal change nnU-Nets were trained on registered and subtracted FLAIR and T1 postlongitudinal images to localize and better quantify and classify changes in ED and AT. Segmentation Dice scores, volume similarities, and 95th percentile Hausdorff distances ranged from 0.72 to 0.89, 0.90 to 0.96, and 2.5 to 3.6 mm, respectively. Accuracy rates of the posttreatment tumor segmentation and longitudinal change networks being able to classify longitudinal changes in ED and AT as increased, decreased, or unchanged were 76%-79% and 90%-91%, respectively. The accuracy levels of the longitudinal change networks were not significantly different from those of three neuroradiologists (accuracy, 90%-92%; κ, 0.58-0.63; P > .05). The results of this study support the potential clinical value of artificial intelligence-based automated longitudinal assessment of posttreatment diffuse glioma. Keywords: MR Imaging, Neuro-Oncology, Neural Networks, CNS, Brain/Brain Stem, Segmentation, Quantification, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T

Quantitative susceptibility mapping (QSM) has the potential for being a biomarker for various diseases because of its ability to measure tissue susceptibility related to iron deposition, myelin, and hemorrhage from the phase signal of a T2 *-weighted MRI. Despite its promise as a quantitative marker, QSM is faced with many challenges, including its dependence on preprocessing of the raw phase data, the relatively weak tissue signal, and the inherently ill posed relationship between the magnetic dipole and measured phase. The goal of this study was to evaluate the effects of background field removal and dipole inversion algorithms on noise characteristics, image uniformity, and structural contrast for cerebral microbleed (CMB) quantification at both 3T and 7T. We selected four widely used background phase removal and five dipole field inversion algorithms for QSM and applied them to volunteers and patients with CMBs, who were scanned at two different field strengths, with ground truth QSM reference calculated using multiple orientation scans. 7T MRI provided QSM images with lower noise than did 3T MRI. QSIP and VSHARP + iLSQR achieved the highest white matter homogeneity and vein contrast, with QSIP also providing the highest CMB contrast. Compared with ground truth COSMOS QSM images, overall good correlations between susceptibility values of dipole inversion algorithms and the COSMOS reference were observed in basal ganglia regions, with VSHARP + iLSQR achieving the susceptibility values most similar to COSMOS across all regions. This study can provide guidance for selecting the most appropriate QSM processing pipeline based on the application of interest and scanner field strength.

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

PURPOSE

To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.

MATERIALS AND METHODS

In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients.

RESULTS

The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98).

CONCLUSION

For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

Striatal networks for tinnitus treatment targeting

Neuromodulation treatment effect size for bothersome tinnitus may be larger and more predictable by adopting a target selection approach guided by personalized striatal networks or functional connectivity maps. Several corticostriatal mechanisms are likely to play a role in tinnitus, including the dorsal/ventral striatum and the putamen. We examined whether significant tinnitus treatment response by deep brain stimulation (DBS) of the caudate nucleus may be related to striatal network increased functional connectivity with tinnitus networks that involve the auditory cortex or ventral cerebellum. The first study was a cross-sectional 2-by-2 factorial design (tinnitus, no tinnitus; hearing loss, normal hearing, n = 68) to define cohort level abnormal functional connectivity maps using high-field 7.0 T resting-state fMRI. The second study was a pilot case-control series (n = 2) to examine whether tinnitus modulation response to caudate tail subdivision stimulation would be contingent on individual level striatal connectivity map relationships with tinnitus networks. Resting-state fMRI identified five caudate subdivisions with abnormal cohort level functional connectivity maps. Of those, two connectivity maps exhibited increased connectivity with tinnitus networks-dorsal caudate head with Heschl's gyrus and caudate tail with the ventral cerebellum. DBS of the caudate tail in the case-series responder resulted in dramatic reductions in tinnitus severity and loudness, in contrast to the nonresponder who showed no tinnitus modulation. The individual level connectivity map of the responder was in alignment with the cohort expectation connectivity map, where the caudate tail exhibited increased connectivity with tinnitus networks, whereas the nonresponder individual level connectivity map did not.

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Somatic mosaicism in the MAPK pathway in sporadic brain arteriovenous malformation and association with phenotype

OBJECTIVE

Sporadic brain arteriovenous malformation (BAVM) is a tangled vascular lesion characterized by direct artery-to-vein connections that can cause life-threatening intracerebral hemorrhage (ICH). Recently, somatic mutations in KRAS have been reported in sporadic BAVM, and mutations in other mitogen-activated protein kinase (MAPK) signaling pathway genes have been identified in other vascular malformations. The objectives of this study were to systematically evaluate somatic mutations in MAPK pathway genes in patients with sporadic BAVM lesions and to evaluate the association of somatic mutations with phenotypes of sporadic BAVM severity.

METHODS

The authors performed whole-exome sequencing on paired lesion and blood DNA samples from 14 patients with sporadic BAVM, and 295 genes in the MAPK signaling pathway were evaluated to identify genes with somatic mutations in multiple patients with BAVM. Digital droplet polymerase chain reaction was used to validate KRAS G12V and G12D mutations and to assay an additional 56 BAVM samples.

RESULTS

The authors identified a total of 24 candidate BAVM-associated somatic variants in 11 MAPK pathway genes. The previously identified KRAS G12V and G12D mutations were the only recurrent mutations. Overall, somatic KRAS G12V was present in 14.5% of BAVM lesions and G12D was present in 31.9%. The authors did not detect a significant association between the presence or allelic burden of KRAS mutation and three BAVM phenotypes: lesion size (maximum diameter), age at diagnosis, and age at ICH.

CONCLUSIONS

The authors confirmed the high prevalence of somatic KRAS mutations in sporadic BAVM lesions and identified several candidate somatic variants in other MAPK pathway genes. These somatic variants may contribute to understanding of the etiology of sporadic BAVM and the clinical characteristics of patients with this condition.

A lactation credit model to support breastfeeding in radiology: The new gold standard to support "liquid gold"

Breastfeeding has medical and economic benefits and providing an environment supportive of breastfeeding should be a priority in radiology to promote diversity, equity and inclusion. Most breastfeeding radiologists do not meet their breastfeeding goals and inadequate time for pumping is the most commonly cited barrier. The UCSF lactation credit model sets the standard for breastfeeding support in medicine by providing protected time without productivity penalties and it should be adapted and implemented across radiology practices to more fully support breastfeeding radiologists and radiation oncologists.

Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases

PURPOSE

To develop and validate a neural network for automated detection and segmentation of intracranial metastases on brain MRI studies obtained for stereotactic radiosurgery treatment planning.

MATERIALS AND METHODS

In this retrospective study, 413 patients (average age, 61 years ± 12 [standard deviation]; 238 women) with a total of 5202 intracranial metastases (median volume, 0.05 cm³; interquartile range, 0.02-0.18 cm³) undergoing stereotactic radiosurgery at one institution were included (January 2017 to February 2020). A total of 563 MRI examinations were performed among the patients, and studies were split into training (n = 413), validation (n = 50), and test (n = 100) datasets. A three-dimensional (3D) U-Net convolutional network was trained and validated on 413 T1 postcontrast or subtraction scans, and several loss functions were evaluated. After model validation, 100 discrete test patients, who underwent imaging after the training and validation patients, were used for final model evaluation. Performance for detection and segmentation of metastases was evaluated using Dice scores, false discovery rates, and false-negative rates, and a comparison with neuroradiologist interrater reliability was performed.

RESULTS

The median Dice score for segmenting enhancing metastases in the test set was 0.75 (interquartile range, 0.63-0.84). There were strong correlations between manually segmented and predicted metastasis volumes (r = 0.98, P < .001) and between the number of manually segmented and predicted metastases (R = 0.95, P < .001). Higher Dice scores were strongly correlated with larger metastasis volumes on a logarithmically transformed scale (r = 0.71). Sensitivity across the whole test sample was 70.0% overall and 96.4% for metastases larger than 6 mm. There was an average of 0.46 false-positive results per scan, with the positive predictive value being 91.5%. In comparison, the median Dice score between two neuroradiologists was 0.85 (interquartile range, 0.80-0.89), with sensitivity across the test sample being 87.9% overall and 98.4% for metastases larger than 6 mm.

CONCLUSION

A 3D U-Net-based convolutional neural network was able to segment brain metastases with high accuracy and perform detection at the level of human interrater reliability for metastases larger than 6 mm.Keywords: Adults, Brain/Brain Stem, CNS, Feature detection, MR-Imaging, Neural Networks, Neuro-Oncology, Quantification, Segmentation© RSNA, 2021.

Cocaine Use and White Matter Hyperintensities in Homeless and Unstably Housed Women

OBJECTIVES

Cocaine use has been linked to stroke in several studies. However, few studies have considered the influence of cocaine use on stroke mechanisms such as small vessel disease (SVD). We conducted a study to assess associations between the toxicology-confirmed use of multiple drugs, including cocaine, and a marker of SVD, white matter hyperintensities (WMH).

MATERIALS AND METHODS

We conducted a nested case-control study (n = 30) within a larger cohort study (N = 245) of homeless and unstably housed women recruited from San Francisco community venues. Participants completed six monthly study visits consisting of an interview, blood draw, vital sign assessment and baseline brain MRI. We examined associations between toxicology-confirmed use of multiple substances, including cocaine, methamphetamine, heroin, alcohol and tobacco, and WMH identified on MRI.

RESULTS

Mean study participant age was 53 years, 70% of participants were ethnic minority women and 86% had a history of cocaine use. Brain MRIs indicated the presence of WMH (i.e., Fazekas score>0) in 54% (18/30) of imaged participants. The odds of WMH were significantly higher in women who were toxicology-positive for cocaine (Odd Ratio=7.58, p=0.01), but not in women who were toxicology-positive for other drugs or had several other cerebrovascular risk factors.

CONCLUSIONS

Over half of homeless and unstably housed women showed evidence of WMH. Cocaine use is highly prevalent and a significant correlate of WMH in this population, while several traditional CVD risk factors are not. Including cocaine use in cerebrovascular risk calculators may improve stroke risk prediction in high-risk populations and warrants further investigation.

Arterial Spin-Labeling Perfusion for PHACE Syndrome

BACKGROUND AND PURPOSE

Arterial stroke is a rare-but-reported complication in patients with posterior fossa brain malformations, hemangiomas, arterial anomalies, coarctation of the aorta and cardiac defects, and eye abnormalities (PHACE) syndrome. Currently, stroke risk is inferred by the severity of arterial anomalies identified on MRA, though no evidenced-based data exist. The purpose of our study was to determine whether arterial spin-labeling MR imaging perfusion can detect alterations in CBF in patients with PHACE syndrome.

MATERIALS AND METHODS

Records were reviewed from 3 institutions for all patients with PHACE syndrome who underwent arterial spin-labeling from 2000 to 2019. CBF was qualitatively investigated with arterial spin-labeling to determine whether there was decreased or normal perfusion. Arterial anomalies were characterized on MRA imaging, and parenchymal brain findings were evaluated on conventional MR imaging sequences.

RESULTS

Forty-one patients with PHACE syndrome had arterial spin-labeling imaging. There were 30 females and 11 males (age range, 7 days to 15 years). Of the 41 patients, 10 (24%) had decreased CBF signal corresponding to a major arterial territory. Ten of 10 patients had decreased CBF signal in the anterior circulation, 2/10 had decreased anterior and posterior circulation CBF signal, 2/10 had decreased bilateral anterior circulation CBF signal, and 1/10 had globally decreased CBF signal. Forty of 41 (97.5%) patients had at least 1 arteriopathy, and in those with decreased CBF signal, the arteriopathy corresponded to the CBF signal alteration in 10/10 patients.

CONCLUSIONS

Arterial spin-labeling can potentially characterize hemodynamic changes in patients with PHACE syndrome.

Consensus-based perioperative protocols during the COVID-19 pandemic

OBJECTIVE

During the COVID-19 pandemic, quaternary-care facilities continue to provide care for patients in need of urgent and emergent invasive procedures. Perioperative protocols are needed to streamline care for these patients notwithstanding capacity and resource constraints.

METHODS

A multidisciplinary panel was assembled at the University of California, San Francisco, with 26 leaders across 10 academic departments, including 7 department chairpersons, the chief medical officer, the chief operating officer, infection control officers, nursing leaders, and resident house staff champions. An epidemiologist, an ethicist, and a statistician were also consulted. A modified two-round, blinded Delphi method based on 18 agree/disagree statements was used to build consensus. Significant disagreement for each statement was tested using a one-sided exact binomial test against an expected outcome of 95% consensus using a significance threshold of p < 0.05. Final triage protocols were developed with unblinded group-level discussion.

RESULTS

Overall, 15 of 18 statements achieved consensus in the first round of the Delphi method; the 3 statements with significant disagreement (p < 0.01) were modified and iteratively resubmitted to the expert panel to achieve consensus. Consensus-based protocols were developed using unblinded multidisciplinary panel discussions. The final algorithms 1) quantified outbreak level, 2) triaged patients based on acuity, 3) provided a checklist for urgent/emergent invasive procedures, and 4) created a novel scoring system for the allocation of personal protective equipment. In particular, the authors modified the American College of Surgeons three-tiered triage system to incorporate more urgent cases, as are often encountered in neurosurgery and spine surgery.

CONCLUSIONS

Urgent and emergent invasive procedures need to be performed during the COVID-19 pandemic. The consensus-based protocols in this study may assist healthcare providers to optimize perioperative care during the pandemic.

Rate of radiation-induced microbleed formation on 7T MRI relates to cognitive impairment in young patients treated with radiation therapy for a brain tumor

BACKGROUND

Radiation therapy (RT) is essential to the management of many brain tumors, but has been known to lead to cognitive decline and vascular injury in the form of cerebral microbleeds (CMBs).

PURPOSE

In a subset of children, adolescents, and young adults recruited from a larger trial investigating arteriopathy and stroke risk after RT, we evaluated the prevalence of CMBs after RT, examined risk factors for CMBs and cognitive impairment, and related their longitudinal development to cognitive performance changes.

METHODS

Twenty-five patients (mean 17 years, range: 10-25 years) underwent 7-Tesla MRI and cognitive assessment. Nineteen patients were treated with whole-brain or focal RT 1-month to 20-years prior, while 6 non-irradiated patients with posterior-fossa tumors served as controls. CMBs were detected on 7T susceptibility-weighted imaging (SWI) using semi-automated software, a first use in this population.

RESULTS

CMB detection sensitivity with 7T SWI was higher than previously reported at lower field strengths, with one or more CMBs detected in 100% of patients treated with RT at least 1-year prior. CMBs were localized to dose-targeted brain volumes with risk factors including whole-brain RT (p = 0.05), a higher RT dose (p = 0.01), increasing time since RT (p = 0.03), and younger age during RT (p = 0.01). Apart from RT dose, these factors were associated with impaired memory performance. Follow-up data in a subset of patients revealed a proportional increase in CMB count with worsening verbal memory performance (r = -0.85, p = 0.03).

CONCLUSIONS

Treatment with RT during youth is associated with the chronic development of CMBs that evolve with memory impairment over time.

MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

Early-Stage Radiology Volume Effects and Considerations with the Coronavirus Disease 2019 (COVID-19) Pandemic: Adaptations, Risks, and Lessons Learned

Objective

The coronavirus disease 2019 (COVID-19) pandemic resulted in significant loss of radiologic volume as a result of shelter-at-home mandates and delay of non-time-sensitive imaging studies to preserve capacity for the pandemic. We analyze the volume-related impact of the COVID-19 pandemic on six academic medical systems (AMSs), three in high COVID-19 surge (high-surge) and three in low COVID-19 surge (low-surge) regions, and a large national private practice coalition. We sought to assess adaptations, risks of actions, and lessons learned.

Methods

Percent change of 2020 volume per week was compared with the corresponding 2019 volume calculated for each of the 14 imaging modalities and overall total, outpatient, emergency, and inpatient studies in high-surge AMSs and low-surge AMSs and the practice coalition.

Results

Steep examination volume drops occurred during week 11, with slow recovery starting week 17. The lowest total AMS volume drop was 40% compared with the same period the previous year, and the largest was 70%. The greatest decreases were seen with screening mammography and dual-energy x-ray absorptiometry scans, and the smallest decreases were seen with PET/CT, x-ray, and interventional radiology. Inpatient volume was least impacted compared with outpatient or emergency imaging.

Conclusion

Large percentage drops in volume were seen from weeks 11 through 17, were seen with screening studies, and were larger for the high-surge AMSs than for the low-surge AMSs. The lowest drops in volume were seen with modalities in which delays in imaging had greater perceived adverse consequences.

Allogeneic HSCT for adult-onset leukoencephalopathy with spheroids and pigmented glia

Adult-onset leukoencephalopathy with spheroids and pigmented glia (ALSP) is an autosomal dominant leukoencephalopathy caused by mutations in colony stimulating factor 1 receptor (CSF1R). Here we report clinical and imaging outcomes following allogeneic haematopoietic stem cell transplantation (HSCT) in two patients with ALSP at the University of California, San Francisco between January 2016 and December 2017. Patient 1 proceeded to transplantation at age 53 with a haplo-identical sibling donor. Patient 2, whose sister and mother had died of the disease, proceeded to transplantation at age 49 with a 12/12 human leukocyte antigen-matched unrelated donor. Both patients received reduced intensity conditioning regimens. At 28 and 26 months post-HSCT, respectively, both patients were alive, without evidence of graft-versus-host disease, with major infection at 1 year in one and new-onset seizures in the other. In both cases, neurological worsening continued post-HSCT; however, the progression in cognitive deficits, overall functional status and gait impairment gradually stabilized. There was continued progression of parkinsonism in both patients. On brain MRI, within 1 year there was stabilization of T2/FLAIR abnormalities, and after 2 years there was complete resolution of abnormal multifocal reduced diffusion. In summary, after >2 years of follow-up, allogeneic HSCT in ALSP led to interval resolution of diffusion MRI abnormalities, stabilization of T2/FLAIR MRI abnormalities, and partial clinical stabilization, supportive of treatment response. Allogeneic HSCT may be beneficial in ALSP by providing a supply of bone marrow-derived brain-engrafting myeloid cells with donor wild-type CSF1R to repopulate the microglial niche.

A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness

PURPOSE

Precise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses.

METHODS

A novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation.

RESULTS

Our new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions.

CONCLUSION

We have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.

Predictive Value of Noncontrast Head CT with Negative Findings in the Emergency Department Setting

BACKGROUND AND PURPOSE

Noncontrast head CTs are routinely acquired for patients with neurologic symptoms in the emergency department setting. Anecdotally, noncontrast head CTs performed in patients with prior negative findings with the same clinical indication are of low diagnostic yield. We hypothesized that the rate of acute findings in noncontrast head CTs performed in patients with a preceding study with negative findings would be lower compared with patients being imaged for the first time.

MATERIALS AND METHODS

We retrospectively evaluated patients in the emergency department setting who underwent noncontrast head CTs at our institution during a 4-year period, recording whether the patient had undergone a prior noncontrast head CT, the clinical indication for the examination, and the examination outcome. Positive findings on examinations were defined as those that showed any intracranial abnormality that would necessitate a change in acute management, such as acute hemorrhage, hydrocephalus, herniation, or interval worsening of a prior finding.

RESULTS

During the study period, 8160 patients in the emergency department setting underwent a total of 9593 noncontrast head CTs; 88.2% (7198/8160) had a single examination, and 11.8% (962/8160) had at least 1 repeat examination. The baseline positive rate of the "nonrepeat" group was 4.3% (308/7198). The 911 patients in the "repeat" group with negative findings on a baseline/first CT had a total of 1359 repeat noncontrast head CTs during the study period. The rate of positive findings for these repeat examinations was 1.8% (25/1359), significantly lower than the 4.3% baseline rate (P < .001). Of the repeat examinations that had positive findings, 80% (20/25) had a study indication that was discordant with that of the prior examination, compared with only 44% (593/1334) of the repeat examinations that had negative findings (P < .001).

CONCLUSIONS

In a retrospective observational study based on approximately 10,000 examinations, we found that serial noncontrast head CT examinations in patients with prior negative findings with the same study indication are less likely to have positive findings compared with first-time examinations or examinations with a new indication. This finding suggests a negative predictive value of a prior noncontrast head CT examination with negative findings with the same clinical indication.

Women in radiology: gender diversity is not a metric-it is a tool for excellence

Women in Focus: Be Inspired was a unique programme held at the 2019 European Congress of Radiology that was structured to address a range of topics related to gender and healthcare, including leadership, mentoring and the generational progression of women in medicine. In most countries, women constitute substantially fewer than half of radiologists in academia or private practice despite frequently accounting for at least half of medical school enrolees. Furthermore, the proportion of women decreases at higher academic ranks and levels of leadership, a phenomenon which has been referred to as a "leaky pipeline". Gender diversity in the radiologic workplace, including in academic and leadership positions, is important for the present and future success of the field. It is a tool for excellence that helps to optimize patient care and research; moreover, it is essential to overcome the current shortage of radiologists. This article reviews the current state of gender diversity in academic and leadership positions in radiology internationally and explores a wide range of potential reasons for gender disparities, including the lack of role models and mentorship, unconscious bias and generational changes in attitudes about the desirability of leadership positions. Strategies for both individuals and institutions to proactively increase the representation of women in academic and leadership positions are suggested. KEY POINTS: • Gender-diverse teams perform better. Thus, gender diversity throughout the radiologic workplace, including in leadership positions, is important for the current and future success of the field. • Though women now make up roughly half of medical students, they remain underrepresented among radiology trainees, faculty and leaders. • Factors leading to the gender gap in academia and leadership positions in Radiology include a lack of role models and mentors, unconscious biases, other societal barriers and generational changes.

Selective Genetic Overlap Between Amyotrophic Lateral Sclerosis and Diseases of the Frontotemporal Dementia Spectrum

Importance

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by loss of upper and lower motor neurons. Although novel ALS genetic variants have been identified, the shared genetic risk between ALS and other neurodegenerative disorders remains poorly understood.

Objectives

To examine whether there are common genetic variants that determine the risk for ALS and other neurodegenerative diseases and to identify their functional pathways.

Design, Setting, and Participants

In this study conducted from December 1, 2016, to August 1, 2017, the genetic overlap between ALS, sporadic frontotemporal dementia (FTD), FTD with TDP-43 inclusions, Parkinson disease (PD), Alzheimer disease (AD), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP) were systematically investigated in 124 876 cases and controls. No participants were excluded from this study. Diagnoses were established using consensus criteria.

Main Outcomes and Measures

The primary outcomes were a list of novel loci and their functional pathways in ALS, FTD, PSP, and ALS mouse models.

Results

Among 124 876 cases and controls, genome-wide conjunction analyses of ALS, FTD, PD, AD, CBD, and PSP revealed significant genetic overlap between ALS and FTD at known ALS loci: rs13302855 and rs3849942 (nearest gene, C9orf72; P = .03 for rs13302855 and P = .005 for rs3849942) and rs4239633 (nearest gene, UNC13A; P = .03). Significant genetic overlap was also found between ALS and PSP at rs7224296, which tags the MAPT H1 haplotype (nearest gene, NSF; P = .045). Shared risk genes were enriched for pathways involving neuronal function and development. At a conditional FDR P < .05, 22 novel ALS polymorphisms were found, including rs538622 (nearest gene, ERGIC1; P = .03 for ALS and FTD), which modifies BNIP1 expression in human brains (35 of 137 females; mean age, 59 years; P = .001). BNIP1 expression was significantly reduced in spinal cord motor neurons from patients with ALS (4 controls: mean age, 60.5 years, mean [SE] value, 3984 [760.8] arbitrary units [AU]; 7 patients with ALS: mean age, 56 years, mean [SE] value, 1999 [274.1] AU; P = .02), in an ALS mouse model (mean [SE] value, 13.75 [0.09] AU for 2 SOD1 WT mice and 11.45 [0.03] AU for 2 SOD1 G93A mice; P = .002) and in brains of patients with PSP (80 controls: 39 females; mean age, 82 years, mean [SE] value, 6.8 [0.2] AU; 84 patients with PSP: 33 females, mean age 74 years, mean [SE] value, 6.8 [0.1] AU; β = -0.19; P = .009) or FTD (11 controls: 4 females; mean age, 67 years; mean [SE] value, 6.74 [0.05] AU; 17 patients with FTD: 10 females; mean age, 69 years; mean [SE] value, 6.53 [0.04] AU; P = .005).

Conclusions and Relevance

This study found novel genetic overlap between ALS and diseases of the FTD spectrum, that the MAPT H1 haplotype confers risk for ALS, and identified the mitophagy-associated, proapoptotic protein BNIP1 as an ALS risk gene. Together, these findings suggest that sporadic ALS may represent a selectively pleiotropic, polygenic disorder.

MRI safety and devices: An update and expert consensus

The use of magnetic resonance imaging (MRI) is increasing globally, and MRI safety issues regarding medical devices, which are constantly being developed or upgraded, represent an ongoing challenge for MRI personnel. To assist the MRI community, a panel of 10 radiologists with expertise in MRI safety from nine high-volume academic centers formed, with the objective of providing clarity on some of the MRI safety issues for the 10 most frequently questioned devices. Ten device categories were identified. The panel reviewed the literature, including key MRI safety issues regarding screening and adverse event reports, in addition to the manufacturer's Instructions For Use. Using a Delphi-inspired method, 36 practical recommendations were generated with 100% consensus that can aid the clinical MRI community. Level of Evidence: 5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:657-674.

Genetic variation across RNA metabolism and cell death gene networks is implicated in the semantic variant of primary progressive aphasia

The semantic variant of primary progressive aphasia (svPPA) is a clinical syndrome characterized by neurodegeneration and progressive loss of semantic knowledge. Unlike many other forms of frontotemporal lobar degeneration (FTLD), svPPA has a highly consistent underlying pathology composed of TDP-43 (a regulator of RNA and DNA transcription metabolism). Previous genetic studies of svPPA are limited by small sample sizes and a paucity of common risk variants. Despite this, svPPA's relatively homogenous clinicopathologic phenotype makes it an ideal investigative model to examine genetic processes that may drive neurodegenerative disease. In this study, we used GWAS metadata, tissue samples from pathologically confirmed frontotemporal lobar degeneration, and in silico techniques to identify and characterize protein interaction networks associated with svPPA risk. We identified 64 svPPA risk genes that interact at the protein level. The protein pathways represented in this svPPA gene network are critical regulators of RNA metabolism and cell death, such as SMAD proteins and NOTCH1. Many of the genes in this network are involved in TDP-43 metabolism. Contrary to the conventional notion that svPPA is a clinical syndrome with few genetic risk factors, our analyses show that svPPA risk is complex and polygenic in nature. Risk for svPPA is likely driven by multiple common variants in genes interacting with TDP-43, along with cell death,x` working in combination to promote neurodegeneration.

Hyperpolarized 13C MRI: State of the Art and Future Directions

Hyperpolarized (HP) carbon 13 (¹³C) MRI is an emerging molecular imaging method that allows rapid, noninvasive, and pathway-specific investigation of dynamic metabolic and physiologic processes that were previously inaccessible to imaging. This technique has enabled real-time in vivo investigations of metabolism that are central to a variety of diseases, including cancer, cardiovascular disease, and metabolic diseases of the liver and kidney. This review provides an overview of the methods of hyperpolarization and ¹³C probes investigated to date in preclinical models of disease. The article then discusses the progress that has been made in translating this technology for clinical investigation. In particular, the potential roles and emerging clinical applications of HP [1-¹³C]pyruvate MRI will be highlighted. The future directions to enable the adoption of this technology to advance the basic understanding of metabolism, to improve disease diagnosis, and to accelerate treatment assessment are also detailed.

Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-β protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.