Faculty Bibliography

Dual-energy computed tomography (DECT) has emerged as a transformative tool in the past decade. Initially employed in gout within the field of rheumatology to distinguish and quantify monosodium urate crystals through its dual-material discrimination capability, DECT has since broadened its clinical applications. It now encompasses various rheumatic diseases, employing advanced techniques such as bone marrow edema assessment, iodine mapping, and collagen-specific imaging. This review article aims to examine the unique characteristics of DECT, discuss its strengths and limitations, illustrate its applications for accurately evaluating various rheumatic diseases in clinical practice, and propose future directions for DECT in rheumatology.

PURPOSE/OBJECTIVE:Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatically by thin-client artificial intelligence (AI) mechanisms. METHODS:A retrospective PACS search identified adults who underwent an emergency department contrast-enhanced abdominopelvic CT in 07/2022 (Console Cohort) and 07/2023 (Server Cohort). Coronal and sagittal multiplanar reformatted images (MPR) were created by AI software in the Server cohort. Time to completion of MPR images was compared using 2-sample t-tests for all patients in both cohorts. Two radiologists qualitatively assessed image quality and diagnostic confidence on 5-point Likert scales for 50 consecutive examinations from each cohort. Additionally, they assessed for acute abdominopelvic findings. Continuous variables and qualitative scores were compared with the Mann-Whitney U test. A p < .05 indicated statistical significance. RESULTS:Mean[SD] time to exam completion in PACS was 8.7[11.1] minutes in the Console cohort (n = 728) and 4.6[6.6] minutes in the Server cohort (n = 892), p < .001. 50 examinations in the Console Cohort (28 women 22 men, 51[19] years) and Server cohort (27 women 23 men, 57[19] years) were included for radiologist review. Age, sex, CTDlvol, and DLP were not statistically different between the cohorts (all p > .05). There was no significant difference in image quality or diagnostic confidence for either reader when comparing the Console and Server cohorts (all p > .05). CONCLUSION/CONCLUSIONS:Examinations utilizing AI generated MPRs on a thin-client architecture were completed approximately 50% faster than those utilizing reconstructions generated at the console with no statistical difference in diagnostic confidence or image quality.

OBJECTIVE:To determine the optimal virtual monoenergy for viewing the bowel at photon-counting CT enterography using quantitative assessment of mural attenuation, contrast-to-noise ratio, signal-to-noise ratio and noise. METHODS:This study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Consecutive adults (≥ 18 years) who underwent photon-counting CT enterography from 5/1/2022-5/31/2022 with available Spectral Postprocessing (SPP) images for retrospective virtual monoenergy creation were identified. Nine virtual monoenergetic series (40-120 keV, 10 keV increments) were created. Two region-of-interest measurements were placed in the stomach wall, jejunum wall, ileum wall, and each psoas muscle by two radiologists on 0.6 mm images in PACS. Region-of-interests were copied to other virtual monoenergies to ensure identical placement and size. Attenuation (HU) and noise (HU standard deviation) were recorded from each region-of-interest. Signal-to-noise ratio and contrast-to-noise ratio were computed for stomach, jejunum, ileum, and all bowel combined. Pairwise comparisons for attenuation, noise, signal-to-noise ratio and contrast-to-noise ratio for each virtual monoenergy were performed with ANOVA. A p <.05 indicated statistical significance. RESULTS:50 patients (32 female; mean[SD] age: 57 years) were included. Attenuation and noise for all bowel regions were highest at 40 keV with statistically significant pairwise comparisons from 40 to 70 keV (all p <.05), but similar for 70-120 keV (all p >.05). Signal-to-noise ratio was similar from 40 to 70 keV (all p >.05) for all bowel regions. Contrast-to-noise ratio decreased with increasing keV. Contrast-to-noise ratio was similar for all bowel at 40 keV and 50 keV (p =.06), for stomach from 40 to 70 keV (all p >.05), for jejunum from 40 to 50 keV (p =.21), and for ileum from 40 to 60 keV (all p >.05). CONCLUSION/CONCLUSIONS:50 keV virtual monoenergetic images from photon-counting CT enterography optimizes contrast-to-noise ratio while mitigating noise and should routinely be utilized for bowel assessment at photon-counting CT enterography. As most photon-counting CT users primarily interpret virtual monoenergetic images in clinical practice, knowledge of the optimal virtual monoenergy can inform protocol development.

PURPOSE/OBJECTIVE:As the pancreas is a low contrast visibility organ, pancreatic ductal adenocarcinoma detection is challenging due to subtle attenuation differences between tumor and pancreatic parenchyma. Photon counting CT (PCCT) has superior iodine contrast-to-noise ratio than conventional CT and also affords the creation of low keV virtual monoenergetic images, both of which increase adenocarcinoma conspicuity. The purpose therefore was to identify the optimal virtual monoenergy for visualizing PDAC during the pancreatic parenchymal phase of enhancement at PCCT using both quantitative and qualitative analyses. METHODS:Consecutive patients with pancreatic parenchymal phase PCCT source data were retrospectively identified by PACS search. For the quantitative analysis, region of interest (ROI) measurements were drawn in the pancreatic head, body, tail, pancreatic adenocarcinoma (if present), and psoas muscles on 40-120 keV virtual monoenergetic images in 10 keV increments. Based on the quantitative analysis results and vendor recommendations, four virtual monoenergies(40 keV, 55 keV, 70 keV, and 85 keV) were selected for additional qualitative analysis. Three radiologists blinded to four virtual monoenergies assessed overall image quality, image noise, pancreatic enhancement, and pancreatic mass conspicuity on 5-point Likert scales. RESULTS:54 patients (28/54 male, mean[SD] age: 62 [13] years) were included. Quantitatively, 40 keV had the highest pancreatic parenchymal CNR and attenuation difference between the adenocarcinoma and parenchyma, but also the highest noise (HUsd). Qualitatively, 70 keV had the best overall image quality (Mean [SE]: 3.7[0.1]) and lower noise than 40 and 55 keV (3.6[0.08] vs. 1.8[0.07] and 2.7[0.05], respectively, p < .001). 40 keV had the greatest pancreatic enhancement (mean[SE] 4.6[0.11]). Adenocarcinoma conspicuity ratings were greatest at 40 keV and 55 keV, and not significantly different from each other (mean[SE] 4.4[0.13] and 4.3[0.14], respectively, Tukey adj-p =.20). 55 keV had greater overall image quality and lower noise than 40 keV (mean[SE] 3.4[0.08] vs. 2.5[0.08], Tukey adj-p < .001 and 2.7[0.05] vs. 1.8[0.07], Tukey adj-p < .001 respectively). CONCLUSION/CONCLUSIONS:55 keV pancreatic parenchymal phase virtual monoenergetic images afford optimal pancreatic assessment at PCCT for the visualization of pancreatic adenocarcinoma. Routinely viewing 55 keV virtual monoenergetic images at PCCT may improve PDAC detection.

PURPOSE/OBJECTIVE:To determine the performance of volumetric dual energy low kV and iodine radiomic features for the differentiation of intrathoracic lymph node histopathology, and influence of contrast protocol. MATERIALS AND METHODS/METHODS:Intrathoracic lymph nodes with histopathologic correlation (neoplastic, granulomatous sarcoid, benign) within 90 days of DECT chest imaging were volumetrically segmented. 1691 volumetric radiomic features were extracted from iodine maps and low-kV images, totaling 3382 features. Univariate analysis was performed using 2-sample t-test and filtered for false discoveries. Multivariable analysis was used to compute AUCs for lymph node classification tasks. RESULTS:129 lymph nodes from 72 individuals (mean age 61 ± 15 years) were included, 52 neoplastic, 51 benign, and 26 granulomatous-sarcoid. Among all contrast enhanced DECT protocol exams (routine, PE and CTA), univariable analysis demonstrated no significant differences in iodine and low kV features between neoplastic and non-neoplastic lymph nodes; in the subset of neoplastic versus benign lymph nodes with routine DECT protocol, 199 features differed (p = .01- < 0.05). Multivariable analysis using both iodine and low kV features yielded AUCs >0.8 for differentiating neoplastic from non-neoplastic lymph nodes (AUC 0.86), including subsets of neoplastic from granulomatous (AUC 0.86) and neoplastic from benign (AUC 0.9) lymph nodes, among all contrast protocols. CONCLUSIONS:Volumetric DECT radiomic features demonstrate strong collective performance in differentiation of neoplastic from non-neoplastic intrathoracic lymph nodes, and are influenced by contrast protocol.

Dual-energy computed tomography (DECT) has emerged as a transformative tool in the past decade. Initially employed in gout within the field of rheumatology to distinguish and quantify monosodium urate crystals through its dual-material discrimination capability, DECT has since broadened its clinical applications. It now encompasses various rheumatic diseases, employing advanced techniques such as bone marrow edema assessment, iodine mapping, and collagen-specific imaging. This review article aims to examine the unique characteristics of DECT, discuss its strengths and limitations, illustrate its applications for accurately evaluating various rheumatic diseases in clinical practice, and propose future directions for DECT in rheumatology.

OBJECTIVE:The aim of this study was to compare portal venous phase photon-counting CT (PCCT) using 20 cc less than weight-based contrast dosing with energy-integrating detector CT (EID-CT) using weight-based dosing by quantitative and qualitative analysis. METHODS:Fifty adult patients who underwent a reduced intravenous contrast dose portal venous phase PCCT from May 1, 2023, to August 10, 2023, and a prior portal-venous EID-CT with weight-based contrast dosing were retrospectively identified. Hounsfield units (HU) and noise (SD of HU) were obtained from region-of-interest measurements on 70-keV PCCT and EID-CT in 4 hepatic segments, the main and right portal vein, and both paraspinal muscles. Signal-to-noise and contrast-to-noise ratios were computed. Three abdominal radiologists qualitatively assessed overall image quality, hepatic enhancement, and confidence for metastasis identification on 5-point Likert scales. Readers also recorded the presence/absence of hepatic metastases. Quantitative variables were compared with paired t tests, and multiple comparisons were accounted for with a Bonferroni-adjusted α level of .0016. Ordinal logistic regression was used to evaluate qualitative assessments. Interreader agreement for hepatic metastases was calculated using Fleiss' κ. RESULTS:Fifty patients (32 women; mean [SD] age, 64 [13] years) were included. There was no significant difference in hepatic HU, portal vein HU, noise, and signal-to-noise or contrast-to-noise ratio between reduced contrast dose portal venous phase PCCT versus EID-CT (all Ps > 0.0016). Image quality, degree of hepatic enhancement, and confidence for metastasis identification were not different for reduced dose PCCT 70-keV images and EID-CT (P = 0.06-0.69). κ Value for metastasis identification was 0.86 (95% confidence interval, 0.70-1.00) with PCCT and 0.78 (95% confidence interval, 0.59-0.98) with EID-CT. CONCLUSION/CONCLUSIONS:Reduced intravenous contrast portal venous phase PCCT 70-keV images had similar attenuation and image quality as EID-CT with weight-based dosing. Metastases were identified with near-perfect agreement in reduced dose PCCT 70-keV images.

PURPOSE/OBJECTIVE:Photon-counting detector CT (PCD CT) is a promising technology for abdominal imaging due to its ability to provide high spatial and contrast resolution images with reduced patient radiation exposure. However, there is currently no consensus regarding the optimal imaging protocols for PCD CT. This article aims to present the PCD CT abdominal imaging protocols used by two tertiary care academic centers in the United States. METHODS:A review of PCD CT abdominal imaging protocols was conducted by two abdominal radiologists at different academic institutions. Protocols were compared in terms of acquisition parameters and reconstruction settings. Both imaging centers independently selected similar protocols for PCD CT abdominal imaging, using QuantumPlus mode. RESULTS:There were some differences in the use of reconstruction kernels and iterative reconstruction levels, however the individual combination at each site resulted in similar image impressions. Overall, the imaging protocols used by both centers provide high-quality images with low radiation exposure. CONCLUSION/CONCLUSIONS:These findings provide valuable insights into the development of standardized protocols for PCD CT abdominal imaging, which can help to ensure consistent as well as high-quality imaging across different institutions and allow for future multicenter research collaborations.

PURPOSE/OBJECTIVE:This study aimed to compare the image quality of portal venous phase-derived virtual noncontrast (VNC) images from photon-counting computed tomography (PCCT) with energy-integrating dual-energy computed tomography (EI-DECT) in the same patient using quantitative and qualitative analyses. METHODS:Consecutive patients retrospectively identified with available portal venous phase-derived VNC images from both PCCT and EI-DECT were included. Patients without available VNC in picture archiving and communication system in PCCT or prior EI-DECT and non-portal venous phase acquisitions were excluded. Three fellowship-trained radiologists blinded to VNC source qualitatively assessed VNC images on a 5-point scale for overall image quality, image noise, small structure delineation, noise texture, artifacts, and degree of iodine removal. Quantitative assessment used region-of-interest measurements within the aorta at 4 standard locations, both psoas muscles, both renal cortices, spleen, retroperitoneal fat, and inferior vena cava. Attenuation (Hounsfield unit), quantitative noise (Hounsfield unit SD), contrast-to-noise ratio (CNR) (CNRvascular, CNRkidney, CNRspleen, CNRfat), signal-to-noise ratio (SNR) (SNRvascular, SNRkidney, SNRspleen, SNRfat), and radiation dose were compared between PCCT and EI-DECT with the Wilcoxon signed rank test. A P < 0.05 indicated statistical significance. RESULTS:A total of 74 patients (27 men; mean ± SD age, 63 ± 13 years) were included. Computed tomography dose index volumes for PCCT and EI-DECT were 9.2 ± 3.5 mGy and 9.4 ± 9.0 mGy, respectively (P = 0.06). Qualitatively, PCCT VNC images had better overall image quality, image noise, small structure delineation, noise texture, and fewer artifacts (all P < 0.00001). Virtual noncontrast images from PCCT had lower attenuation (all P < 0.05), noise (P = 0.006), and higher CNR (P < 0.0001-0.04). Contrast-enhanced structures had lower SNR on PCCT (P = 0.001, 0.002), reflecting greater contrast removal. The SNRfat (nonenhancing) was higher for PCCT than EI-DECT (P < 0.00001). CONCLUSIONS:Virtual noncontrast images from PCCT had improved image quality, lower noise, improved CNR and SNR compared with those derived from EI-DECT.