Faculty Bibliography

PURPOSE/OBJECTIVE:Though prior studies have proven CTC's efficacy in outpatients, its utility in the inpatient setting has not been studied. We evaluated the efficacy of a modified CTC protocol in the inpatient setting, primarily for patients awaiting organ transplantation. METHODS:This retrospective study compared a group of inpatient CTCs from 2019 to 2021 and a randomly selected, age-matched 2:1 control group of outpatient CTCs. Both groups were assessed based on established criteria from literature. RESULTS:10 % (63/652) of CTCs were performed in the inpatient setting, of which 29 were excluded, yielding 34 inpatient cases. 90 % (589/652) of CTCs were performed in the outpatient setting, from which 68 randomly selected, age-matched patients were selected as controls. Significantly more (24 %, 8/34) inpatients expired due to extracolonic causes (vs. 1 %, 1/68 outpatients, p < 0.05). 62 % (21/34) of inpatient CTCs were reported as diagnostic (vs. 74 %, 50/68 outpatient, p = 0.22). Significantly more inpatients (12 %, 4/34) than outpatients (1 %, 1/68) were unable to tolerate two imaging positions (p = 0.02). Subsequent colonoscopy was performed in 24 % (8/34) of inpatients, revealing pathologies including colonic polyps and non-bleeding ulcers. Inpatient CTCs had lower average quality scores, significant for one reviewer (p = 0.009-0.054). Inpatients had a larger number of segments with: >25 % residual fluid (1.22-1.28 inpatients vs. 0.60-0.73 outpatients, p = 0.003-0.026) and inadequate fluid tagging (1.10 inpatients vs. 0.49 outpatients, p = 0.046-0.0501). Distention was not significantly different between groups (p = 0.317-0.410). CONCLUSION/CONCLUSIONS:Quality of inpatient CTC was inferior to outpatient CTCs across several metrics. 24 % undergoing inpatient CTC died of extracolonic causes within 22 months, and most did not have findings warranting intervention, questioning the value of this difficult exam in this patient population. Routine CT may be sufficient to exclude large or metastatic colonic lesions precluding transplant.

Acute and chronic bowel pathologies can often be mistaken for manifestations of inflammatory bowel disease (IBD), and there are many entities with imaging and clinical features that overlap with IBD, making diagnosis difficult. We describe multiple inflammatory, infectious, neoplastic, and vascular entities with imaging and clinical features that may mimic IBD, and highlight differentiating features to assist in diagnosis.

Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most existing literature reviews focusing solely on LLMs, this work examines both LLMs and MLLMs, highlighting their potential to support radiology workflows such as report generation, image interpretation, EHR summarization, differential diagnosis generation, and patient education. By streamlining these tasks, LLMs and MLLMs could reduce radiologist workload, improve diagnostic accuracy, support interdisciplinary collaboration, and ultimately enhance patient care. We also discuss key limitations, such as the limited capacity of current MLLMs to interpret 3D medical images and to integrate information from both image and text data, as well as the lack of effective evaluation methods. Ongoing efforts to address these challenges are introduced.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R² of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R² of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at this stage. Recently, there has been significant interest in improving risk stratification and developing imaging biomarkers, through novel imaging techniques, and most notably, artificial intelligence (AI) technology. This review provides an overview of these advancements, with a focus on deep learning methods for early detection of PDA.

OBJECTIVE:To retrospectively compare inter- and intra-reader agreement of abbreviated MRCP (aMRCP) with comprehensive MRI (cMRCP) protocol for detection of worrisome features, high-risk stigmata, and concomitant pancreatic cancer in pancreatic cyst surveillance. METHODS:151 patients (104 women, mean age: 69[10] years) with baseline and follow-up contrast-enhanced MRIs were included. This comprised 138 patients under cyst surveillance with 5-year follow-up showing no pancreatic ductal adenocarcinoma (PDAC), 6 with pancreatic cystic lesion-derived malignancy, and 7 with concomitant PDAC. The aMRCP protocol used four sequences (axial and coronal Half-Fourier Single-shot Turbo-spin-Echo, axial T1 fat-saturated pre-contrast, and 3D-MRCP), while cMRCP included all standard sequences, including post-contrast. Three blinded abdominal radiologists assessed baseline cyst characteristics, worrisome features, high-risk stigmata, and PDAC signs using both aMRCP and cMRCP, with a 2-week washout period. Intra- and inter-reader agreement were calculated using Fleiss' multi-rater kappa and Intra-class Correlation Coefficient (ICC). 95% confidence intervals (CI) were calculated. RESULTS:Cyst size, growth, and abrupt main pancreatic duct transition had strong intra- and inter-reader agreement. Intra-reader agreement was ICC = 0.93-0.99 for cyst size, ICC = 0.71-1.00 for cyst growth, and kappa = 0.83-1.00 for abrupt duct transition. Inter-reader agreement for cyst size was ICC = 0.86 (aMRCP) and ICC = 0.83 (cMRCP), and for abrupt duct transition was kappa = 0.84 (aMRCP) and kappa = 0.69 (cMRCP). Thickened cyst wall, mural nodule and cyst-duct communication demonstrated varying intra-reader agreements and poor inter-reader agreements. CONCLUSION/CONCLUSIONS:aMRCP showed high intra- and inter-reader agreement for most pancreatic cyst parameters that highly rely on T2-weighted sequences.

PURPOSE/OBJECTIVE:To compare the inter-reader agreement of pancreatic adenocarcinoma resectability assessment at pancreatic protocol photon-counting CT (PCCT) with conventional energy-integrating detector CT (EID-CT). METHODS:A retrospective single institution database search identified all contrast-enhanced pancreatic mass protocol abdominal CT performed at an outpatient facility with both a PCCT and EID-CT from 4/11/2022 to 10/30/2022. Patients without pancreatic adenocarcinoma were excluded. Four fellowship-trained abdominal radiologists, blinded to CT type, independently assessed vascular tumor involvement (uninvolved, abuts ≤ 180°, encases > 180°; celiac, superior mesenteric artery (SMA), common hepatic artery (CHA), superior mesenteric vein (SMV), main portal vein), the presence/absence of metastases, overall tumor resectability (resectable, borderline resectable, locally advanced, metastatic), and diagnostic confidence. Fleiss's kappa was used to calculate inter-reader agreement. CTDIvol was recorded. Radiation dose metrics were compared with a two-sample t-test. A p < .05 indicated statistical significance. RESULTS:145 patients (71 men, mean[SD] age: 66[9] years) were included. There was substantial inter-reader agreement, for celiac artery, SMA, and SMV involvement at PCCT (kappa = 0.61-0.69) versus moderate agreement at EID-CT (kappa = 0.56-0.59). CHA had substantial inter-reader agreement at both PCCT (kappa = 0.67) and EIDCT (kappa = 0.70). For metastasis identification, radiologists had substantial inter-reader agreement at PCCT (kappa = 0.78) versus moderate agreement at EID-CT (kappa = 0.56). CTDIvol for PCCT and EID-CT were 16.9[7.4]mGy and 29.8[26.6]mGy, respectively (p < .001). CONCLUSION/CONCLUSIONS:There was substantial inter-reader agreement for involvement of 4/5 major peripancreatic vessels (celiac artery, SMA, CHA, and SMV) at PCCT compared with 2/5 for EID-CT. PCCT also afforded substantial inter-reader agreement for metastasis detection versus moderate agreement at EID-CT with statistically significant radiation dose reduction.

This work proposes MP-Grasp4D (magnetization-prepared golden-angle radial sparse parallel 4D) MRI, a free-breathing, inversion recovery (IR)-prepared, time-resolved 4D MRI technique with improved T1-weighted contrast. MP-Grasp4D MRI acquisition incorporates IR preparation into a radial gradient echo sequence. MP-Grasp4D employs a golden-angle navi-stack-of-stars sampling scheme, where imaging data of rotating radial stacks and navigator stacks (acquired at a consistent rotation angle) are alternately acquired. The navigator stacks are used to estimate a temporal basis for low-rank subspace-constrained reconstruction. This allows for the simultaneous capture of both IR-induced contrast changes and respiratory motion. One temporal frame of the imaging volume in MP-Grasp4D MRI is reconstructed from a single stack and an adjacent navigator stack on average, resulting in a nominal temporal resolution of 0.16 seconds per volume. Images corresponding to the optimal inversion time (TI) can be retrospectively selected for providing the best image contrast. Reader studies were conducted to assess the performance of MP-Grasp4D MRI in liver imaging across 30 subjects in comparison with standard Grasp4D MRI without IR preparation. MP-Grasp4D MRI received significantly higher scores (P < 0.05) than Grasp4D in all assessment categories. There was a moderate to almost perfect agreement (kappa coefficient from 0.42 to 0.9) between the two readers for image quality assessment. When the scan time is reduced, MP-Grasp4D MRI preserves image contrast and quality, demonstrating additional acceleration capability. MP-Grasp4D MRI improves T1-weighted contrast for free-breathing time-resolved 4D MRI and eliminates the need for explicit motion compensation. This method is expected to be valuable in different MRI applications such as MR-guided radiotherapy.