Simulated twin-phase pancreatic CT generated using single portal venous phase dual-energy CT acquisition in pancreatic ductal adenocarcinoma

CT energy spectral parameters of creeping fat in Crohn's disease and correlation with inflammatory activity

OBJECTIVES

Creeping fat is a kind of unique abnormal mesenteric tissue at the sites of diseased bowel of Crohn's disease. By using dual-energy CT enterography, this study aimed to evaluate the feasibility of spectral parameters in the quantitative analysis of mesenteric adipose tissue or creeping fat.

METHODS

In this study, patients with known or suspected Crohn's disease who underwent dual-energy CT enterography from March 1, 2019, to March 31, 2021, were enrolled. Among them, 40 patients with surgery and pathology-proven creeping fat were selected as the creeping fat Crohn's disease group, and 40 normal patients were selected as the control group. The quantitative spectral parameters including the slope of the Hounsfield unit curve, normalised fat-water concentration, normalised fat-iodine concentration, and normalised fat volume fraction at the enteric phases were obtained. Mann-Whitney U test, Kruskal-Wallis H test, and receiver operating characteristic curve analysis were applied to compare quantitative parameters among various groups.

RESULTS

A significant difference was observed in the slope of the Hounsfield unit curve, normalised fat-water concentration, normalised fat-iodine concentration, and normalised fat volume fraction between mesenteric adipose tissue and creeping fat with Crohn's disease at the enteric phase (all p < 0.001). The slope of the Hounsfield unit curve of creeping fat at the enteric phase had a better capability to distinguish inactive and active Crohn's disease (AUC = 0.93, p < 0.001).

CONCLUSION

Dual-energy CT enterography with quantitative spectral parameters is a potentially novel noninvasive tool for evaluating creeping fat in Crohn's disease.

CRITICAL RELEVANCE STATEMENT

Energy spectral parameters of creeping fat in Crohn's disease are significantly different from normal mesenteric adipose tissues and are correlated with inflammatory activity.

KEY POINTS

• Dual-energy CT enterography allows quantitatively assessing creeping fat with spectral parameters. • The creeping fat has distinct spectral parameters to normal mesenteric adipose. • The spectral parameters accurately differentiate active and inactive Crohn's disease.

Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction

PURPOSE

To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms.

METHOD

A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test.

RESULTS

The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003).

CONCLUSION

In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.

Dual-Energy CT in the Pancreas

Dual-energy computed tomography (DECT) is an evolving imaging technology that is gaining popularity, particularly in different abdominopelvic applications. Essentially, DECT uses two energy spectra simultaneously to acquire CT attenuation data which is used to distinguish among structures with different tissue composition. The wide variety of reconstructed image data sets makes DECT especially attractive in pancreatic imaging. This article reviews the current literature on DECT as it applies to imaging the pancreas, focusing on pancreatitis, trauma, pancreatic ductal adenocarcinoma, and other solid and cystic neoplasms. The advantages of DECT over conventional CT are highlighted, including improved lesion detection, radiation dose reduction, and enhanced image contrast. Additionally, data exploring the ideal protocol for pancreatic imaging using DECT is reviewed. Finally, limitations of DECT in pancreatic imaging as well as recommendations for future research are provided.

Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma: Role of the Radiologist and Oncologist in the Era of Precision Medicine

The incidence and mortality of pancreatic ductal adenocarcinoma are growing over time. The management of patients with pancreatic ductal adenocarcinoma involves a multidisciplinary team, ideally involving experts from surgery, diagnostic imaging, interventional endoscopy, medical oncology, radiation oncology, pathology, geriatric medicine, and palliative care. An adequate staging of pancreatic ductal adenocarcinoma and re-assessment of the tumor after neoadjuvant therapy allows the multidisciplinary team to choose the most appropriate treatment for the patient. This review article discusses advancement in the molecular basis of pancreatic ductal adenocarcinoma, diagnostic tools available for staging and tumor response assessment, and management of resectable or borderline resectable pancreatic cancer.

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

OBJECTIVES

To evaluate the image quality and iodine concentration (IC) measurements in pancreatic protocol dual-energy computed tomography (DECT) reconstructed using deep learning image reconstruction (DLIR) and compare them with those of images reconstructed using hybrid iterative reconstruction (IR).

METHODS

The local institutional review board approved this prospective study. Written informed consent was obtained from all participants. Thirty consecutive participants with pancreatic cancer (PC) underwent pancreatic protocol DECT for initial evaluation. DECT data were reconstructed at 70 keV using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The diagnostic acceptability and conspicuity of PC were qualitatively assessed using a 5-point scale. IC values of the abdominal aorta, pancreas, PC, liver, and portal vein; standard deviation (SD); and coefficient of variation (CV) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups.

RESULTS

The diagnostic acceptability and conspicuity of PC were significantly better in the DLIR-M group compared with those in the other groups (p < .001-.001). The IC values of the anatomical structures were almost comparable between the three groups (p = .001-.9). The SD of IC values was significantly lower in the DLIR-H group (p < .001) and resulted in the lowest CV (p < .001-.002) compared with those in the hybrid-IR and DLIR-M groups.

CONCLUSIONS

DLIR could significantly improve image quality and reduce the variability of IC values than could hybrid-IR.

KEY POINTS

Image quality and conspicuity of pancreatic cancer were the best in DLIR-M. DLIR significantly reduced background noise and improved SNR and CNR. The variability of iodine concentration was reduced in DLIR.