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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.

PurposeTo evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of images reconstructed using hybrid iterative reconstruction (IR).MethodsOur institutional review board approved this prospective study. Written informed consent was obtained from all patients. Twenty-eight consecutive patients with PDAC undergoing chemotherapy (14 men and 14 women; mean age, 68.4 years) underwent pancreatic LDCT for therapy evaluation. The LDCT images were reconstructed using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H). The image noise, diagnostic acceptability, and conspicuity of PDAC were qualitatively assessed using a 5-point scale. CT numbers of the abdominal aorta, portal vein, pancreas, PDAC, background noise, signal-to-noise ratio (SNR) of the anatomical structures, and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H images.ResultsCT dose-index volumes and dose-length product in pancreatic LDCT were 2.3 ± 1.0 mGy and 74.9 ± 37.0 mGy•cm, respectively. The image noise, diagnostic acceptability, and conspicuity of PDAC were significantly better in DLIR-H than those in hybrid-IR and DLIR-M (all P < 0.001). The background noise was significantly lower in the DLIR-H images (P < 0.001) and resulted in improved SNRs (P < 0.001) and CNR (P < 0.001) compared with those in the hybrid-IR and DLIR-M images.ConclusionDLIR significantly reduced image noise and improved image quality in pancreatic LDCT images compared with hybrid-IR.

PurposeTo compare the quantitative parameters and qualitative image quality of dual-energy CT angiography (CTA) between two rapid kVp-switching dual-energy CT scanners.Materials and methodsBetween May 2021 and March 2022, 79 participants underwent whole-body CTA using either Discovery CT750 HD (Group A, n = 38) or Revolution CT Apex (Group B, n = 41). All data were reconstructed at 40-keV and with adaptive statistical iterative reconstruction-Veo of 40%. The two groups were compared in terms of CT numbers of the thoracic and abdominal aorta, and the iliac artery, background noise, signal-to-noise ratio (SNR) of the artery, CT dose-index volume (CTDIvol), and qualitative scores for image noise, sharpness, diagnostic acceptability, and arterial depictions.ResultsThe median CT number of the abdominal aorta (p = 0.04) and SNR of the thoracic aorta (p = 0.02) were higher in Group B than in Group A, while no difference was observed in the other CT numbers and SNRs of the artery (p = 0.09–0.23). The background noises at the thoracic (p = 0.11), abdominal (p = 0.85), and pelvic (p = 0.85) regions were comparable between the two groups. CTDIvol was lower in Group B than in Group A (p = 0.006). All qualitative scores were higher in Group B than in Group A (p < 0.001–0.04). The arterial depictions were nearly identical in both two groups (p = 0.005–1.0).ConclusionIn dual-energy CTA at 40-keV, Revolution CT Apex improved qualitative image quality and reduced radiation dose.

PurposeTo evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).Materials and methodsTwo patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups.ResultsThe background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001).ConclusionsDLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

PurposeTo define the radiological arterial anatomy in mature microminipigs as a pre-clinical research animal model in interventional radiology.Materials and MethodsFive female microminipigs (weighing 20.9 ± 2.9 kg) were used in this study. Under general anesthesia, computed tomography (CT) angiography was performed using a 16-slice CT scanner. CT was performed 12 s after initiation of an intravenous injection of 40 ml of nonionic contrast media at 3.0 ml/second using a power injector. The transverse CT angiography images were evaluated using a digital imaging and communication in medicine viewer, and the diameters of the following 41 arteries were measured.: ascending aorta, descending aorta, thoracoabdominal aorta, abdominal aorta, pulmonary artery trunk, both pulmonary, brachiocephalic artery, short common bicarotid, both common carotid artery, subclavian, bronchial, internal mammary, celiac, common hepatic, left lateral hepatic, middle hepatic, left hepatic, gastroduodenal, cranial duodenopancreatic, splenic, left gastric, cranial mesenteric, ileocolic , bilateral colic artery, caudal mesenteric, cranial rectal, renal, both external iliac arteries, internal iliac common trunk, and both internal iliac and femoral arteries.ResultsThe microminipigs’ vascular anatomy was the same as domestic pig anatomy and similar to human anatomy. The diameter of the aorta (ascending to abdominal) was 17.1–7.0 mm, iliac and femoral arteries (internal iliac common trunk to femoral artery): 5.5–3.8 mm, pulmonary arteries: 9.3–14.7 mm, and major first aortic branches (e.g., celiac or brachiocephalic artery): 2.2–9.2 mm.ConclusionThis study defined the microminipig arterial anatomy in the trunk.

The purpose of this study was to investigate the utility of reconstructed CT images perpendicular to the artery for assessing arterial involvement from pancreatic cancer and compare the interobserver variability between it and the current diagnostic imaging method. This retrospective study included patients with pancreatic cancer in the pancreatic body or tail who underwent preoperative pancreatic protocol CT and distal pancreatectomy. Five radiologists used axial and coronal CT images (current method) and perpendicular reconstructed CT images (proposed method) to determine if the degree of solid soft-tissue contact with the splenic artery was ≤180° or >180°. The generalized estimating equations were used to compare the diagnostic performance of solid soft-tissue contact >180° between the current and proposed methods. Fleiss’ ĸ statistics were used to assess interobserver variability. The sensitivity and negative predictive value for diagnosing solid soft-tissue contact >180° were higher (p < 0.001 for each) and the specificity (p = 0.003) and positive predictive value (p = 0.003) were lower in the proposed method than the current method. Interobserver variability was improved in the proposed method compared with the current method (ĸ = 0.87 vs. 0.67). Reconstructed CT images perpendicular to the artery showed higher sensitivity and negative predictive value for diagnosing solid soft-tissue contact >180° than the current method and demonstrated improved interobserver variability.

This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.

The aim of this study was to examine whether the Wnt/β-catenin signal activation is a cause of radioresistance in colon cancer by assessing the β-catenin localization and its correlation with cancer stem cells (CSCs). The nuclear levels of β-catenin, the hallmark of Wnt activation, were analyzed in HCT116 and SW480 cells by immunohistochemistry, before and after irradiation. Further, we assessed CSC populations by staining for aldehyde dehydrogenase-1 (ALDH1) and CD44. β-catenin was localized predominantly in the nucleus and plasma membrane in SW480 and HCT116 cells, respectively. Compared to HCT116 cells, SW480 cells displayed higher Wnt activation. At 24 h after irradiation, most of the DSBs in SW480 cells were repaired, but were still present in HCT116 cells. Additionally, compared to HCT116 cells, a significantly higher proportion of SW480 cells were ALDH1- and CD44-positive. Colon cancers with nuclear β-catenin accumulation demonstrated greater radio-resistance with a higher number of CSCs.

Purpose To determine if a 20 % reduction in the contrast material dose is acceptable in the CT evaluation of patients with head and neck malignancy. Materials and methods Sixty consecutive patients (mean age 67 years) with head and neck malignancy underwent contrast-enhanced CT according to two different protocols: protocol A (80 mL of contrast material administered at an injection rate of 1.5 mL/s) and protocol B (100 mL at 1.9 mL/s). The enhancement of anatomical structures and detectability of metastatic nodes were compared between the two protocols. Pathologic analysis of the surgical resection served as the reference standard. Results CT numbers of the anatomical structures were not significantly different between the two protocols. Mean sensitivity (64 and 77 % for protocols A and B, respectively), specificity (78 and 84 %), and accuracy (74 and 83 %) tended to be higher for protocol B than for A, but no significant difference was found. Conclusion Reducing the contrast material dose by 20 % did not significantly impair the enhancement of anatomical structures or the detection of metastatic cervical lymph nodes. Radiologists should therefore consider reducing the contrast material dose used in head and neck CT.

This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.