Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans.

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants' exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.

Measuring Quantitative Cerebral Blood Flow in Healthy Children: A Systematic Review of Neuroimaging Techniques.

Cerebral blood flow (CBF) is an important hemodynamic parameter to evaluate brain health. It can be obtained quantitatively using medical imaging modalities such as magnetic resonance imaging and positron emission tomography (PET). Although CBF in adults has been widely studied and linked with cerebrovascular and neurodegenerative diseases, CBF data in healthy children are sparse due to the challenges in pediatric neuroimaging. An understanding of the factors affecting pediatric CBF and its normal range is crucial to determine the optimal CBF measuring techniques in pediatric neuroradiology. This review focuses on pediatric CBF studies using neuroimaging techniques in 32 articles including 2668 normal subjects ranging from birth to 18 years old. A systematic literature search was conducted in PubMed, Embase, and Scopus and reported following the preferred reporting items for systematic reviews and meta-analyses (PRISMA). We identified factors (such as age, gender, mood, sedation, and fitness) that have significant effects on pediatric CBF quantification. We also investigated factors influencing the CBF measurements in infants. Based on this review, we recommend best practices to improve CBF measurements in pediatric neuroimaging. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Short- and Long-Term MRI Assessed Hemodynamic Changes in Pediatric Moyamoya Patients After Revascularization.

BACKGROUND: Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change following a vasodilation challenge. Decreased CVR is associated with a higher stroke risk in patients with cerebrovascular diseases. While revascularization can improve CVR and reduce this risk in adult patients with vasculopathy such as those with Moyamoya disease, its impact on hemodynamics in pediatric patients remains to be elucidated. Arterial spin labeling (ASL) is a quantitative MRI technique that can measure CBF, CVR, and arterial transit time (ATT) non-invasively. PURPOSE: To investigate the short- and long-term changes in hemodynamics after bypass surgeries in patients with Moyamoya disease. STUDY TYPE: Longitudinal. POPULATION: Forty-six patients (11 months-18 years, 28 females) with Moyamoya disease. FIELD STRENGTH/SEQUENCE: 3-T, single- and multi-delay ASL, T1-weighted, T2-FLAIR, 3D MRA. ASSESSMENT: Imaging was performed 2 weeks before and 1 week and 6 months after surgical intervention. Acetazolamide was employed to induce vasodilation during the imaging procedure. CBF and ATT were measured by fitting the ASL data to the general kinetic model. CVR was computed as the percentage change in CBF. The mean CBF, ATT, and CVR values were measured in the regions affected by vasculopathy. STATISTICAL TESTS: Pre- and post-revascularization CVR, CBF, and ATT were compared for different regions of the brain. P-values <0.05 were considered statistically significant. RESULTS: ASL-derived CBF in flow territories affected by vasculopathy significantly increased after bypass by 41 ± 31% within a week. At 6 months, CBF significantly increased by 51 ± 34%, CVR increased by 68 ± 33%, and ATT was significantly reduced by 6.6 ± 2.9%. DATA CONCLUSION: There may be short- and long-term improvement in the hemodynamic parameters of pediatric Moyamoya patients after bypass surgery. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment.

BACKGROUND: Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake environments, understanding the model's decision-making process is critical. This study assesses the performance of different pretrained Bidirectional Encoder Representations from Transformers (BERT) models and delves into understanding its decision-making within the context of medical image protocol assignment. METHODS: Four different pre-trained BERT models (BERT, BioBERT, ClinicalBERT, RoBERTa) were fine-tuned for the medical image protocol classification task. Word importance was measured by attributing the classification output to every word using a gradient-based method. Subsequently, a trained radiologist reviewed the resulting word importance scores to assess the model's decision-making process relative to human reasoning. RESULTS: The BERT model came close to human performance on our test set. The BERT model successfully identified relevant words indicative of the target protocol. Analysis of important words in misclassifications revealed potential systematic errors in the model. CONCLUSIONS: The BERT model shows promise in medical image protocol assignment by reaching near human level performance and identifying key words effectively. The detection of systematic errors paves the way for further refinements to enhance its safety and utility in clinical settings.

A within-coil optical prospective motion-correction system for brain imaging at 7T.

PURPOSE: Motion artifact limits the clinical translation of high-field MR. We present an optical prospective motion correction system for 7 Tesla MRI using a custom-built, within-coil camera to track an optical marker mounted on a subject. METHODS: The camera was constructed to fit between the transmit-receive coils with direct line of sight to a forehead-mounted marker, improving upon prior mouthpiece work at 7 Tesla MRI. We validated the system by acquiring a 3D-IR-FSPGR on a phantom with deliberate motion applied. The same 3D-IR-FSPGR and a 2D gradient echo were then acquired on 7 volunteers, with/without deliberate motion and with/without motion correction. Three neuroradiologists blindly assessed image quality. In 1 subject, an ultrahigh-resolution 2D gradient echo with 4 averages was acquired with motion correction. Four single-average acquisitions were then acquired serially, with the subject allowed to move between acquisitions. A fifth single-average 2D gradient echo was acquired following subject removal and reentry. RESULTS: In both the phantom and human subjects, deliberate and involuntary motion were well corrected. Despite marked levels of motion, high-quality images were produced without spurious artifacts. The quantitative ratings confirmed significant improvements in image quality in the absence and presence of deliberate motion across both acquisitions (P < .001). The system enabled ultrahigh-resolution visualization of the hippocampus during a long scan and robust alignment of serially acquired scans with interspersed movement. CONCLUSION: We demonstrate the use of a within-coil camera to perform optical prospective motion correction and ultrahigh-resolution imaging at 7 Tesla MRI. The setup does not require a mouthpiece, which could improve accessibility of motion correction during 7 Tesla MRI exams.

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.

BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging. PURPOSE: To demonstrate automated detection and segmentation of brain metastases on multisequence MRI using a deep-learning approach based on a fully convolution neural network (CNN). STUDY TYPE: Retrospective. POPULATION: In all, 156 patients with brain metastases from several primary cancers were included. FIELD STRENGTH: 1.5T and 3T. [Correction added on May 24, 2019, after first online publication: In the preceding sentence, the first field strength listed was corrected.] SEQUENCE: Pretherapy MR images included pre- and postgadolinium T1 -weighted 3D fast spin echo (CUBE), postgadolinium T1 -weighted 3D axial IR-prepped FSPGR (BRAVO), and 3D CUBE fluid attenuated inversion recovery (FLAIR). ASSESSMENT: The ground truth was established by manual delineation by two experienced neuroradiologists. CNN training/development was performed using 100 and 5 patients, respectively, with a 2.5D network based on a GoogLeNet architecture. The results were evaluated in 51 patients, equally separated into those with few (1-3), multiple (4-10), and many (>10) lesions. STATISTICAL TESTS: Network performance was evaluated using precision, recall, Dice/F1 score, and receiver operating characteristic (ROC) curve statistics. For an optimal probability threshold, detection and segmentation performance was assessed on a per-metastasis basis. The Wilcoxon rank sum test was used to test the differences between patient subgroups. RESULTS: The area under the ROC curve (AUC), averaged across all patients, was 0.98 ± 0.04. The AUC in the subgroups was 0.99 ± 0.01, 0.97 ± 0.05, and 0.97 ± 0.03 for patients having 1-3, 4-10, and >10 metastases, respectively. Using an average optimal probability threshold determined by the development set, precision, recall, and Dice score were 0.79 ± 0.20, 0.53 ± 0.22, and 0.79 ± 0.12, respectively. At the same probability threshold, the network showed an average false-positive rate of 8.3/patient (no lesion-size limit) and 3.4/patient (10 mm3 lesion size limit). DATA CONCLUSION: A deep-learning approach using multisequence MRI can automatically detect and segment brain metastases with high accuracy. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:175-182.

Sarcopenia in emergency abdominal surgery.

BACKGROUND: Sarcopenia, a loss of skeletal muscle mass associated with aging, is a practical measure of frailty and has been previously identified as a predictor of outcomes in surgical cohorts including cancer resection and elderly patients. We hypothesized that sarcopenia, as measured by preoperative computerized tomography (CT) scan, predicts mortality and morbidity in emergent laparotomy. METHODS: Institutional American College of Surgeons National Surgical Quality Improvement Program data were queried for adult patients who underwent open emergency abdominal surgery between 2008 and 2013. Patients with abdominal CT scans within 30 d before surgery were included, and cross-sectional areas of the psoas muscles at vertebral level L4 were summed, normalized by patient height, and stratified by sex. The influence of this total psoas area (TPA) on postoperative morbidity and mortality was evaluated using univariate and multivariate analysis. RESULTS: Of 781 surgeries, 593 (75.9%) had appropriate preoperative CT scans. Median patient age was 61 years old, median TPA was 1719 mm2, and median body mass index was 26.7. Univariate analysis demonstrated a significant association between TPA and total postoperative morbidity (P = 0.0133), increased length of stay (<0.0001), and 90-d mortality (P = 0.0008) but not 30-d mortality (P = 0.26). In multivariate analysis, TPA lost its significance compared to more influential predictors of mortality, including American Society of Anesthesiologists classification. CONCLUSIONS: Sarcopenia, as measured by TPA, significantly predicted mortality in univariate analysis but lost significance in multivariate analysis when factors such as American Society of Anesthesiologists score were included. Because TPA is readily available at no additional risk or cost, it is a convenient additional tool for preoperative risk assessment and counseling.

Time-resolved CT assessment of collaterals as imaging biomarkers to predict clinical outcomes in acute ischemic stroke.

PURPOSE: Collateral circulation plays a pivotal role in the pathophysiology of acute ischemic stroke and is increasingly recognized as a promising biomarker for predicting the clinical outcome. However, there is no single established grading system. We designed a novel machine-learning software that allows non-invasive, objective, and quantitative assessment of collaterals according to their vascular territories. Our goal is to investigate the prognostic and predictive value of this collateral score for the prediction of acute stroke outcome. METHODS: This is a retrospective study of 135 patients with anterior circulation stroke treated with IV TPA. An equation using this collateral score (adjusting for age, baseline NIHSS, and recanalization) was derived to predict the clinical outcome (90-day mRS). The primary analyses focused on determining the prognostic value of our newly developed collateral scores. Secondary analyses examined the interrelationships between the collateral score and other variables. RESULTS: The collateral score emerged as a statistically significant prognostic biomarker for good clinical outcome (p < 0.033) among recanalized patients, but not among non-recanalized patients (p < 0.497). Our results also showed that collateral score was a predictive biomarker (p < 0.044). These results suggest that (1) patients with good collateral score derive more benefit from successful recanalization than patients with poor collateral score and (2) collateral status is inconsequential if recanalization is not achieved. CONCLUSION: Our data results reinforce the importance of careful patient selection for recanalization therapy to avoid futile recanalization. The paucity of collaterals predicts poor clinical outcome despite recanalization. On the other hand, robust collaterals warrant consideration for recanalization therapy given the better odds of good clinical outcome.

Effective time window in reducing pituitary adenoma size by gamma knife radiosurgery.

OBJECTIVES: Although the effectiveness of gamma knife radiosurgery (GKRS) in controlling the size of pituitary adenomas has been well demonstrated in many studies, the time period in which significant changes in tumor size occurs has been investigated in a limited fashion. It is important to determine the therapeutic window of GKRS in treating pituitary adenomas, i.e., the effective timeframe during which significant size reduction of these tumors occurs, so that alternative treatments such as further GKRS or microsurgery might be prescribed in a timely manner if clinically indicated. METHODS: This was a nested sample of an ongoing local cohort study on GKRS for pituitary adenomas at the University of Virginia. Magnetic resonance imaging (MRI) using dedicated sequences was employed. Only patients with a baseline MRI (TP0) and at least 1 follow-up study performed in the University Hospital after GKRS were included. The follow-up scans were performed at five time-points (TP1-TP5) which were 6, 12, 24, 36 and 48 months after GKRS. The dimensional indices of the tumors were measured in three orthogonal planes, i.e., transverse (TR), antero-posterior (AP) and cranio-caudal (CC). The volumes of the tumors were estimated by using the following formula: [Formula: see text]. Tumor volume decrease by more than 25% from baseline was considered as 'shrinkage', <25% tumor size increase or decrease was considered 'static', and more than 25% increase as 'increment'. Our cohort consisted of 21 patients, with functioning adenomas in 13 subjects i.e. six adrenocorticotrophic hormone (ACTH)-secreting and seven growth hormone (GH)-secreting, and non-functioning (NF) adenomas in eight subjects. RESULTS: In 26 adenomas (8 ACTH, 9 GH and 9 NF), tumor control (tumor shrinkage or static) were achieved in 21 tumors (80.8%); 89, 75, and 78% for GH-secreting, ACTH-secreting and NF adenomas respectively, at the end of the 4-year follow-up period. Analysis of variance showed significant differences of GKRS margin dose among different types of tumors (p = 0.013), but not of baseline tumor volumes (p = 0.240). Logistic regression analysis showed no significant association of margin dose, baseline volume or tumor type with the tumor control outcome. Comparison of tumor change using dimensional indices relative to the base time point (TP0) showed that in the sample there was an average reduction of 1.290 mm at TP1 (6 months) with p values 0.155 (parametric t test) and 0.098 (non-parametric Wilcoxon signed-ranked test) respectively, showing a moderate reduction in tumor dimensional indices. The change in dimensional indices at later time points (TP2-TP5) showed an average reduction ranging from 1.930 to 2.471 mm. Significant reduction in the mean dimensional indices was firstly observed at TP2 (1 year) with p values 0.013 (t test) and 0.018 (Wilcoxon signed-rank test). Such scale of reduction in the dimensional indices appeared to be maintained along the time axis (from TP2 to TP5). CONCLUSIONS: Significant decrease in tumor dimensional indices tended to occur at 1 year post-GKRS. Although to a lesser extent, such decrease in dimensional indices continued up to the end of our follow-up period.

CTA-enhanced perfusion CT: an original method to perform ultra-low-dose CTA-enhanced perfusion CT.

INTRODUCTION: Utilizing CT angiography enhances image quality in PCT, thereby permitting acquisition at ultra-low dose. METHODS: Dynamic CT acquisitions were obtained at 80 kVp with decreasing tube current-time product [milliamperes × seconds (mAs)] in patients suspected of ischemic stroke, with concurrent CTA of the cervical and intracranial arteries. By utilizing fast Fourier transformation, high spatial frequencies of CTA were combined with low spatial frequencies of PCT to create a virtual PCT dataset. The real and virtual PCT datasets with decreasing mAs were compared by assessing contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and noise and PCT values and by visual inspection of PCT parametric maps. RESULTS: Virtual PCT attained CNR and SNR three- to sevenfold superior to real PCT and noise reduction by a factor of 4-6 (p < 0.05). At 20 mAs, virtual PCT achieved diagnostic parametric maps, while the quality of real PCT maps was inadequate. At 10 mAs, both real and virtual PCT maps were nondiagnostic. Virtual PCT (but not real PCT) maps regained diagnostic quality at 10 mAs by applying 40 % adaptive statistical iterative reconstruction (ASIR) and improved further with 80 % ASIR. CONCLUSION: Our new method of creating virtual PCT by combining ultra-low-dose PCT with CTA information yields diagnostic perfusion parametric maps from PCT acquired at 20 or 10 mAs with 80 % ASIR. Effective dose is approximately 0.20 mSv, equivalent to two chest radiographs.

The role of imaging in acute ischemic stroke.

Neuroimaging has expanded beyond its traditional diagnostic role and become a critical tool in the evaluation and management of stroke. The objectives of imaging include prompt accurate diagnosis, treatment triage, prognosis prediction, and secondary preventative precautions. While capitalizing on the latest treatment options and expanding upon the "time is brain" doctrine, the ultimate goal of imaging is to maximize the number of treated patients and improve the outcome of one the most costly and morbid disease. A broad overview of comprehensive multimodal stroke imaging is presented here to affirm its utilization.

Management of peripherally inserted central catheters (PICC) in pediatric heart failure patients receiving continuous inotropic support.

The study aim was to evaluate present practice of maintaining PICC line patency in pediatric heart failure patients receiving continuous inotropes by comparing one cohort receiving low dose continuous heparin with one receiving no heparin. A case control retrospective chart review compared the two cohorts on duration of patency (measured in days) and need for thrombolytic agents. Median duration of patency for the heparin group was 24 days versus 16 days for the no heparin group (p=0.07). Use of thrombolytic agents was 28% in the heparin group compared to 50% in the no heparin group (p=0.08). Although not statistically significant, findings were clinically significant and supportive of current practice.

Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience.

BACKGROUND AND PURPOSE: MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging. MATERIALS AND METHODS: This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined. RESULTS: Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone. CONCLUSIONS: Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics.

Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.

The Sensitivity of Limited-Sequence MRI in Identifying Pediatric Cervical Spine Injury: A Western Pediatric Surgery Research Consortium Multicenter Retrospective Cohort Study.

INTRODUCTION: Clinical clearance of a child's cervical spine after trauma is often challenging due to impaired mental status or an unreliable neurologic examination. Magnetic resonance imaging (MRI) is the gold standard for excluding ligamentous injury in children but is constrained by long image acquisition times and frequent need for anesthesia. Limited-sequence MRI (LSMRI) is used in evaluating the evolution of traumatic brain injury and may also be useful for cervical spine clearance while potentially avoiding the need for anesthesia. The purpose of this study was to assess the sensitivity and negative predictive value of LSMRI as compared to gold standard full-sequence MRI as a screening tool to rule out clinically significant ligamentous cervical spine injury. METHODS: We conducted a ten-center, five-year retrospective cohort study (2017-2021) of all children (0-18y) with a cervical spine MRI after blunt trauma. MRI images were re-reviewed by a study pediatric radiologist at each site to determine if the presence of an injury could be identified on limited sequences alone. Unstable cervical spine injury was determined by study neurosurgeon review at each site. RESULTS: We identified 2,663 children less than 18 years of age who underwent an MRI of the cervical spine with 1,008 injuries detected on full-sequence studies. The sensitivity and negative predictive value of LSMRI were both >99% for detecting any injury and 100% for detecting any unstable injury. Young children (age < 5 years) were more likely to be electively intubated or sedated for cervical spine MRI. CONCLUSION: LSMRI is reliably detects clinically significant ligamentous injury in children after blunt trauma. To decrease anesthesia use and minimize MRI time, trauma centers should develop LSMRI screening protocols for children without a reliable neurologic exam. LEVEL OF EVIDENCE: 2 (Diagnostic Tests or Criteria).

Use of computed tomography to identify atrial fibrillation associated differences in left atrial wall thickness and density.

INTRODUCTION: Left atrial (LA) tissue characteristics may play an important role in atrial fibrillation (AF) induction and perpetuation. Although frequently used in clinical practice, computed tomography (CT) has not been employed to describe differences in LA wall properties between AF patients and controls. We sought to noninvasively characterize AF-associated differences in LA tissue using CT. METHODS: CT images of the LA were obtained in 98 consecutive patients undergoing AF ablation and in 89 controls. A custom software algorithm was used to measure wall thickness and density in four prespecified regions of the LA. RESULTS: On average, LA walls were thinner (-15.5%, 95% confidence interval [CI] -23.2 to -7.8%, P < 0.001) and demonstrated significantly lower density (-19.7 Hounsfield Units [HU], 95% CI -27.0 to -12.5 HU, P < 0.001) in AF patients compared to controls. In linear mixed models adjusting for demographics, clinical variables, and other CT measurements, the average LA, interatrial septum, LA appendage, and anterior walls remained significantly thinner in AF patients. After adjusting for the same potential confounders, history of AF was associated with reduced density in the LA anterior wall and increased density below the right inferior pulmonary vein and in the LA appendage. CONCLUSION: Application of an automated measurement algorithm to CT imaging of the atrium identified significant thinning of the LA wall and regional alterations in tissue density in patients with a history of AF. These findings suggest differences in LA tissue composition can be noninvasively identified and quantified using CT.

Revascularization improves vascular hemodynamics - a study assessing cerebrovascular reserve and transit time in Moyamoya patients using MRI.

Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change. Decreased CVR implies poor hemodynamics and is linked to a higher risk for stroke. Revascularization has been shown to improve CBF in patients with vasculopathy such as Moyamoya disease. Dynamic susceptibility contrast (DSC) can measure transit time to evaluate patients suspected of stroke. Arterial spin labeling (ASL) is a non-invasive technique for CBF, CVR, and arterial transit time (ATT) measurements. Here, we investigate the change in hemodynamics 4-12 months after extracranial-to-intracranial direct bypass in 52 Moyamoya patients using ASL with single and multiple post-labeling delays (PLD). Images were collected using ASL and DSC with acetazolamide. CVR, CBF, ATT, and time-to-maximum (Tmax) were measured in different flow territories. Results showed that hemodynamics improved significantly in regions affected by arterial occlusions after revascularization. CVR increased by 16 ± 11% (p < 0.01) and 25 ± 13% (p < 0.01) for single- and multi-PLD ASL, respectively. Transit time measured by multi-PLD ASL and post-vasodilation DSC reduced by 13 ± 7% (p < 0.01) and 9 ± 5% (p < 0.01), respectively. For all regions, ATT correlated significantly with Tmax (R2 = 0.59, p < 0.01). Thus, revascularization improved CVR and decreased transit times. Multi-PLD ASL can serve as an effective and non-invasive modality to examine vascular hemodynamics in Moyamoya patients.

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.

Introduction: Spatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that don't match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements. Methods: Deep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction. Results: Deli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes. Conclusion: Deli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.