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•SynthSR turns clinical scans of different resolution and contrast into 1 mm MPRAGEs.•It relies on a CNN trained on fake images synthesized on the fly at every minibatch.•It can be retrained for any combination of resolutions / contrasts without new data.•It enables segmentation, registration, etc with existing software (e.g. FreeSurfer) Code is open source. [Display omitted] Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols – even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR.

Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71–0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67–0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.

Objectives Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer. Methods In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest–based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance. Results Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival ( p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94. Conclusions MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer. Key Points • MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.

Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71–0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67–0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.

•The SAMER-MPRAGE sequence effectively corrects motion in 3D volumetric brain MRI.•SAMER increases the accuracy of cortical volume and cortical thickness estimation.•Validation was performed on healthy volunteers and dementia-workup outpatients.•SAMER-MPRAGE allows accurate morphometry even for severely-motion-corrupted scans.•SAMER-MPRAGE has been translated to be utilized in practical clinical settings. Three-dimensional (3D) T1-weighted MRI sequences such as the magnetization prepared rapid gradient echo (MPRAGE) sequence are important for assessing regional cortical atrophy in the clinical evaluation of dementia but have long acquisition times and are prone to motion artifact. The recently developed Scout Accelerated Motion Estimation and Reduction (SAMER) retrospective motion correction method addresses motion artifact within clinically-acceptable computation times and has been validated through qualitative evaluation in inpatient and emergency settings. We evaluated the quantitative accuracy of morphometric analysis of SAMER motion-corrected compared to non-motion-corrected MPRAGE images by estimating cortical volume and thickness across neuroanatomical regions in two subject groups: (1) healthy volunteers and (2) patients undergoing evaluation for dementia. In part (1), we used a set of 108 MPRAGE reconstructed images derived from 12 healthy volunteers to systematically assess the effectiveness of SAMER in correcting varying degrees of motion corruption, ranging from mild to severe. In part (2), 29 patients who were scheduled for brain MRI with memory loss protocol and had motion corruption on their clinical MPRAGE scans were prospectively enrolled. In part (1), SAMER resulted in effective correction of motion-induced cortical volume and thickness reductions. We observed systematic increases in the estimated cortical volume and thickness across all neuroanatomical regions and a relative reduction in percent error values compared to reference standard scans of up to 66 % for the cerebral white matter volume. In part (2), SAMER resulted in statistically significant volume increases across anatomical regions, with the most pronounced increases seen in the parietal and temporal lobes, and general reductions in percent error relative to reference standard clinical scans. SAMER improves the accuracy of morphometry through systematic increases and recovery of the estimated cortical volume and cortical thickness following motion correction, which may affect the evaluation of regional cortical atrophy in patients undergoing evaluation for dementia.

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

BackgroundDiffusion-weighted imaging (DWI) is a useful MRI technique to characterize abdominal lesions in children, but long acquisition times can lead to image degradation. Simultaneous multi-slice accelerated DWI is a promising technique to shorten DWI scan times.ObjectiveTo test the feasibility of simultaneous multi-slice DWI of the kidneys in pediatric patients with tuberous sclerosis complex (TSC) and to evaluate the accelerated protocol regarding image quality and quantitative apparent diffusion coefficient (ADC) values compared to standard echoplanar DWI sequence.Materials and methodsWe included 33 children and adolescents (12 female, 21 male; mean age 10±5 years) with TSC and renal cyst or angiomyolipoma on 3-tesla (T) MRI from 2017 to 2019. All studies included both free-breathing standard echoplanar DWI and simultaneous multi-slice DWI sequences. Subjective and quantitative image quality was evaluated using a predefined 5-point scale. ADC values were obtained for all renal cysts and angiomyolipomas ≥5 mm. All statistical analysis was performed using Stata/SE v15.1.ResultsSimultaneous multi-slice DWI ADC values were slightly lower compared to standard echoplanar DWI for both renal cysts and angiomyolipomas (mean difference 0.05×10−3 mm2/s, 95% confidence interval [CI] 0.40–0.50 and 0.024×10−3 mm2/s, 95% CI 0.17–0.21, respectively, with P>0.1). Our results showed that renal lesions with ADC values >1.69×10−3 mm2/s were all cysts, whereas lesions with values <1.16×10−3 mm2/s were all angiomyolipomas. However, ADC values could not discriminate between lipid-rich and lipid-poor angiomyolipomas (P>0.1, for both sequences).ConclusionA 55% reduction in scan time was achieved using simultaneous multi-slice DWI for abdominal imaging in children with TSC, with near identical image quality as standard DWI. These results suggest that multi-slice techniques should be considered more broadly as an MRI acceleration technique in children.

BackgroundIn children exposed to multiple computed tomography (CT) exams, performed with varying z-axis coverage and often with tube current modulation, it is inaccurate to add volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE) to obtain cumulative dose values.ObjectiveTo introduce the patient-size-specific z-axis dose profile and its dose line integral (DLI) as new dose metrics, and to use them to compare cumulative dose calculations against conventional measures.Materials and methodsIn all children with 2 or more abdominal-pelvic CT scans performed from 2013 through 2019, we retrospectively recorded all series kV, z-axis tube current profile, CTDIvol, dose-length product (DLP) and calculated SSDE. We constructed dose profiles as a function of z-axis location for each series. One author identified the z-axis location of the superior mesenteric artery origin on each series obtained to align the dose profiles for construction of each patient’s cumulative profile. We performed pair-wise comparisons between the peak dose of the cumulative patient dose profile and ΣSSDE, and between ΣDLI and ΣDLP.ResultsWe recorded dose data in 143 series obtained in 48 children, ages 0–2 years (n=15) and 8–16 years (n=33): ΣSSDE 12.7±6.7 and peak dose 15.1±8.1 mGy, ΣDLP 278±194 and ΣDLI 550±292 mGy·cm. Peak dose exceeded ΣSSDE by 20.6% (interquartile range [IQR]: 9.9–26.4%, P<0.001), and ΣDLI exceeded ΣDLP by 114% (IQR: 86.5–147.0%, P<0.001).ConclusionOur methodology represents a novel approach for evaluating radiation exposure in recurring pediatric abdominal CT examinations, both at the individual and population levels. Under a wide range of patient variables and acquisition conditions, graphic depiction of the cumulative z-axis dose profile across and beyond scan ranges, including the peak dose of the profile, provides a better tool for cumulative dose documentation than simple summations of SSDE. ΣDLI is advantageous in characterizing overall energy absorption over ΣDLP, which significantly underestimated this in all children.

Purpose: The goal of this study was to evaluate the outpatient workflow efficiency of an optimized facility (OF) compared to an established reference facility (RF) for abdominal magnetic resonance imaging (MRI). Methods: In this retrospective study, we analyzed 2,723 contrast-enhanced liver and prostate MRI examinations conducted between March 2022 and April 2024. All examinations were performed on 3T scanners (MAGNETOM Vida, Siemens Healthineers) at two different imaging facilities within our institution. The optimized facility featured a three-bay setup, with each bay consisting of one magnet, two dockable tables, and one dedicated preparation room, while the reference facility utilized a single scanner-single table setup with one dedicated preparation room. Workflow metrics were extracted from scanner logs and electronic health records. Three-way ANOVA and chi-square tests were used to assess the impact of facility design, body region, and date on workflow metrics. Results: The OF significantly reduced mean table turnaround times (4.6 min vs. 8.3 min, p  < 0.001) and achieved shorter total process cycle times for both liver (30.6 min vs. 32.7 min, p  < 0.01) and prostate exams (32.5 min vs. 36.4 min, p  < 0.001) compared to the RF. Additionally, the OF achieved turnaround times of ≤ 1 min in 37.2% of exams, compared to just 0.6% at the RF ( p  < 0.001). On-time performance was also notably higher at the OF (79.4% vs. 66.0%, p  < 0.001). Furthermore, the mean time from patient arrival to exam start was reduced by 9 min at the OF ( p  < 0.001). Minor differences in acquisition times were observed between facilities, with both benefiting from deep learning reconstruction techniques. Conclusion: The optimized MRI facility demonstrated superior outpatient workflow efficiency compared to an already efficient reference facility, particularly in table turnover time, resulting in increased patient throughput for abdominal MRI examinations. These findings highlight that even highly efficient MRI facilities can significantly benefit from comprehensive redesign strategies.

PurposePatients with inflammatory bowel disease (IBD) are at risk for intra-abdominal abscesses requiring CT-guided drainage. These patients are at baseline risk of high cumulative radiation exposure from imaging, which may be exacerbated by CT-guided drainage. This study aimed to determine the radiation dose associated with percutaneous drainage in the setting of IBD and identify risk factors associated with high exposure.MethodsAn IRB-approved single-center retrospective study was performed to identify patients with IBD who underwent percutaneous abscess drainage over a 5-year period. An episode of drainage was defined from drain placement to removal, with all intervening procedures and diagnostic CT scans included in the cumulative radiation dose.ResultsThe mean cumulative effective dose for a drainage episode was 47.50 mSv. The mean duration of a drainage episode was 68.7 days. Patients with a cumulative dose greater than 50 mSv required higher number of follow-up visits compared to patients with less than 50 mSv (6.9 vs. 3.5, p = 0.003*). Patients with higher cumulative dose were also more likely to require drain upsize (54% vs. 13%, p = 0.01*) or additional drain placement (63% vs 24%, p = 0.03*) compared to patients with lower dose.ConclusionIntra-abdominal abscess drainage may be associated with significant cumulative radiation exposure. Requirement of drain upsizing or additional drain placement were associated with higher cumulative radiation dose, which may be related to more severe underlying inflammatory bowel disease.