Use of 3D-CT-derived psoas major muscle volume in defining sarcopenia in colorectal cancer.

BACKGROUND: Sarcopenia is characterized by reduced skeletal muscle volume and is a condition that is prevalent among elderly patients and associated with poor prognosis as a comorbidity in malignancies. Given the aging population over 80 years old in Japan, an understanding of malignancies, including colorectal cancer (CRC), complicated by sarcopenia is increasingly important. Therefore, the focus of this study is on a novel and practical diagnostic approach of assessment of psoas major muscle volume (PV) using 3-dimensional computed tomography (3D-CT) in diagnosis of sarcopenia in patients with CRC. METHODS: The subjects were 150 patients aged ≥ 80 years with CRC who underwent primary tumor resection at Juntendo University Hospital between 2004 and 2017. 3D-CT measurement of PV and conventional CT measurement of the psoas major muscle cross-sectional area (PA) were used to identify sarcopenia (group S) and non-sarcopenia (group nS) cases. Clinicopathological characteristics, operative results, postoperative complications, and prognosis were compared between these groups. RESULTS: The S:nS ratios were 15:135 for the PV method and 52:98 for the PA method. There was a strong positive correlation (r = 0.66, p < 0.01) between PVI (psoas major muscle volume index) and PAI (psoas major muscle cross-sectional area index), which were calculated by dividing PV or PA by the square of height. Surgical results and postoperative complications did not differ significantly in the S and nS groups defined using each method. Overall survival was worse in group S compared to group nS identified by PV (p < 0.01), but not significantly different in groups S and nS identified by PA (p = 0.77). A Cox proportional hazards model for OS identified group S by PV as an independent predictor of a poor prognosis (p < 0.05), whereas group S by PA was not a predictor of prognosis (p = 0.60). CONCLUSIONS: The PV method for identifying sarcopenia in elderly patients with CRC is more practical and sensitive for prediction of a poor prognosis compared to the conventional method.

Usefulness of Ag Additional Filter on Image Quality and Radiation Dose for Low-Dose Chest Computed Tomography.

OBJECTIVE: This study aimed to evaluate the effect of a silver (Ag) additional filter on dose characteristics and image quality in low-dose chest computed tomography (CT). METHODS: A dose evaluation phantom, physical evaluation phantom, and chest phantom were scanned with and without an Ag additional filter. The doses were adjusted so that the displayed the volume CT dose indexes (CTDI vol ) were from 0.3 to 1.6 mGy. For dose characteristics, the spectrum of photon energies and the measured CTDI vol were calculated for each scanning condition. For task-based image quality analysis, task transfer function, noise power spectrum, and system performance were evaluated. Streak artifacts, image noise, and contrast-to-noise ratio were quantified using a chest phantom. RESULTS: With the Ag additional filter, mean energy was 22% higher and the CTDI vol was approximately 30% lower than those without the Ag additional filter. The task transfer function and noise power spectrum with the Ag additional filter were lower than those without the Ag additional filter. The system performance with the Ag additional filter was similar to that without the Ag additional filter. The Ag additional filter reduced streak artifact near the lung apex and image noise in the lung fields. The contrast-to-noise ratio was slightly higher with the Ag additional filter than that without the Ag additional filter. CONCLUSIONS: The output dose and spatial resolution with the Ag additional filter were lower than those without the Ag additional filter. However, this filter helped reduce the radiation dose, image noise, and streak artifacts, particularly when scanning at ultralow doses.

Impact of a Deep Learning-based Super-resolution Image Reconstruction Technique on High-contrast Computed Tomography: A Phantom Study.

RATIONALE AND OBJECTIVES: Deep-learning-based super-resolution image reconstruction (DLSRR) is a novel image reconstruction technique that is expected to contribute to improvement in spatial resolution as well as noise reduction through learning from high-resolution computed tomography (CT). This study aims to evaluate image quality obtained with DLSRR and assess its clinical potential. MATERIALS AND METHODS: CT images of a Mercury CT 4.0 phantom were obtained using a 320-row multi-detector scanner at tube currents of 100, 200, and 300 mA. Image data were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), deep-learning-based image reconstruction (DLR), and DLSRR at image reconstruction strength levels of mild, standard, and strong. Noise power spectrum (NPS), task transfer function (TTF), and detectability index were calculated. RESULTS: The magnitude of the noise-reducing effect in comparison with FBP was in the order MBIR

Dual-energy CT imaging of atherosclerotic plaque using novel ultrasmall superparamagnetic iron oxide in hyperlipidemic rabbits.

BACKGROUND: A study using magnetic resonance imaging (MRI) revealed that ultra-small superparamagnetic iron oxide is phagocytosed by macrophages. However, MRI has limitations in obtaining clear images due to its poor spatial and temporal resolutions. PURPOSE: To examine whether the use of dual-energy computed tomography (DECT) facilitated the visualization of carboxymethyl-diethylaminoethyl dextran magnetite ultra-small superparamagnetic iron oxide (CMEADM-U) accumulation in arteriosclerotic lesions using hyperlipidemic rabbits. MATERIAL AND METHODS: CMEADM-U at 0.5 mmol Fe/kg was administered to Watanabe hereditary atherosclerotic (WHHL) rabbits (n = 6, 24 sections) and New Zealand white (NZW) rabbits (n = 2, 6 sections). After 72 h, DECT was performed to prepare virtual monochromatic images (35 keV, 70 keV) and an iron-based map. Subsequently, the aorta was collected along with hematoxylin and eosin staining, Berlin blue (BB) staining, and RAM11 immunostaining. RESULTS: In the WHHL rabbits, CMEADM-U accumulation was not observed at 70 keV. However, CMEADM-U accumulation consistent with an arteriosclerotic lesion was observed at 35 keV and the iron-based map. On the other hand, in the NZW rabbits, there was no accumulation of CMEADM-U in any images. Further, there were significant differences in the iron-based map value at the site of accumulation among the grades of expression on BB staining and RAM11 immunostaining. In addition, there was a good correlation at 35 kev and iron-based map value (r = 0.42; P < 0.05). CONCLUSION: DECT imaging for CMEADM-U facilitated the assessment of macrophage accumulation in atherosclerotic lesions in an in vivo study using a rabbit model of induced aortic atherosclerosis.

Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification.

BACKGROUND: Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major limitation in the evaluation of the vascular lumen. Subtraction coronary computed tomography angiography (Sub-CCTA) is a method known to be able to reduce the influence of coronary artery calcification and is therefore feasible for improving the diagnosis of significant stenosis in patients with severe calcification. However, Sub-CCTA still involves some problems, such as the increased radiation dose due to plain (mask) imaging, extended breath-holding time, and misregistration due to differences in the imaging phase. Therefore, we considered using artificial intelligence instead of Sub-CCTA to visualize the coronary lumen with high calcification. Given this background, the present study aimed to evaluate the diagnostic performance of a deep learning-based lumen extraction method (DL-LEM) to detect significant stenosis on CCTA in 99 consecutive patients (891 segments) with severe coronary calcification from November 2015 to March 2018. We also estimated the impact of DL-LEM on the medical economics in Japan. RESULTS: The DL-LEM slightly improved the per-segment diagnostic accuracy from 74.5 to 76.4%, and the area under the curve (AUC) slightly improved from 0.752 to 0.767 (p = 0.030). When analyzing the 228 segments that could not be evaluated because of severe calcification on the original CCTA images, the DL-LEM improved the accuracy from 35.5 to 42.5%, and the AUC improved from 0.500 to 0.587 (p = 0.00018). As a result, DL-LEM analysis could have avoided invasive coronary angiography in 4/99 cases (per patient). From the calculated results, it was estimated that the number of exams that can be avoided in Japan in one year is approximately 747 for invasive coronary angiography, 219 for fractional flow reserve, and 248 for nuclear exam. The total amount of medical fee that could be reduced was 225,629,368 JPY. CONCLUSIONS: These findings suggest that the DL-LEM may improve the diagnostic performance in detecting significant stenosis in patients with severe coronary calcification. In addition, the results suggest that not a small medical economic effect can be expected.

Efficacy of CO2 Infusion for Preoperative Computed Tomographic Angiography with Computed Tomographic Colonography.

BACKGROUND Computed tomographic colonography (CTC) is useful for patients for whom colonoscopy may be difficult to perform and is widely employed to examine the vasculature prior to colorectal cancer surgery. Computed tomographic angiography (CTA) was shown to be beneficial intraoperatively to manipulate blood vessels and prevent vascular injury. Three-dimensional (3D)-CTA combined with CTC (3D-CTA with CTC) is useful for preoperative evaluations of the anatomy of mesenteric vessels, colon, and lymph nodes. We observed that when the intestine was dilated with carbon dioxide (CO2), the arteriovenous delineation was often more pronounced than without CO2. To clarify the effects of gas injection with and without CO2 on hemodynamics and vascular passage, we compared the effect of contrast for blood vessels. MATERIAL AND METHODS Thirty patients with resectable colorectal cancer who underwent a preoperative CT examination at our institution from January to October 2019 were study participants. Of these, 15 underwent 3D-CTA and 15 had 3D-CTA with CTC. Three board-certified radiologists independently and blindly evaluated 18 blood vessels. CT values for each blood vessel were measured on each image. RESULTS CT values for 3D-CTA with CTC were significantly higher with CO2 than without CO2. The quality of 3D-CTA with CTC images for visualization of blood vessels was also significantly greater than that of 3D-CTA, especially those of arterial and intramesenteric venous systems. CONCLUSIONS Based on the higher image quality and CT values obtained by 3D-CTA with CTC for vessels, compared with by 3D-CTA imaging, 3D-CTA with CTC imaging might be useful in evaluating colorectal cancers.

Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography.

AIMS: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-learning algorithms. The purpose of the study was to evaluate the accuracy of a fully automated 3D deep-learning model for estimating minimum FFR from CCTA data, with invasive FFR as the reference standard. METHODS AND RESULTS: This retrospective study of 1052 patients included 131 patients whose CCTA studies showed 30-90% stenosis and underwent invasive FFR (abnormal FFR observed in 72/131, 55%), and 921 patients who underwent clinically indicated CCTA without invasive FFR. We designed a fully automated 3D deep-learning model that inputs CCTA data and outputs minimum FFR without requiring human input. The model comprised a series of deep-learning algorithms: a conditional generative adversarial network, a 3D convolutional ladder network, and two independent neural networks with integrated virtual adversarial training. We used Monte Carlo cross-validation to evaluate the accuracy of the model for estimating FFR, with invasive FFR as the reference standard. The deep-learning FFR achieved area under the receiver-operating characteristic curve of 0.78 for detection of abnormal FFR; and was significantly higher than for visually determined CCTA >50% stenosis (area under the curve = 0.56). The deep-learning FFR model achieved 76% accuracy for detecting abnormal FFR, with sensitivity of 85% (79-89%) and specificity of 63% (54-70%). CONCLUSION: The 3D deep-learning model, which performs fully automatic estimation of minimum FFR from cardiac CT data, achieved 76% accuracy in detecting abnormal FFR.

Feasibility of Macrophage Plaque Imaging Using Novel Ultrasmall Superparamagnetic Iron Oxide in Dual Energy CT.

PURPOSE: While ultrasmall superparamagnetic iron oxide (USPIO) is useful for identifying atherosclerotic lesions as an MRI contrast medium, there are limitations in its power to quantitatively evaluate and resolve USPIO in atherosclerotic lesions of the heart. Computed tomography (CT) has a higher resolution than MRI, and Dual Energy CT is capable of visualizing iron atoms, the main component of USPIO. More recently, a new USPIO capable of achieving longer retention times in blood circulation compared to the previous USPIO has been developed. The objective of this study was to investigate the feasibility of visualizing and quantifying the new USPIO by dual energy CT. MATERIALS AND METHODS: USPIO with iron concentrations adjusted in 5 steps from 2.5 to 50 mg/mL was visualized by dual energy CT to measure the contrast on virtual monochromatic imaging (40 and 70 keV). In parallel experiments, iodine contrast medium was diluted to the same concentrations and visualized by dual energy CT to measure the contrast at 70 keV. The linearity of the contrast against the iron and iodine concentrations was measured for the quantitative evaluation. Further, a vascular phantom simulating clinical cases (divided into 4 layers: meat alone, meat + USPIO, vascular lumen, and with or without calcification) was prepared. The iron density image was overlaid on the image at 70 keV to evaluate the visualization of the USPIO medium. RESULTS: In the imaging of the medium with an iron concentration of 25 mg/mL, the CT numberat 70 keV was 117.0 HU, or about 17% of that of iodine (664.4 HU). The CT number rose to 319.9 HU at 40 keV, or to about 48% of that of iodine. The linearity of the contrast against the iron concentration in USPIO was R2 = 0.9996, indicating a strong correlation. In the simulated vascular phantom, the iron concentration significantly increased in the region containing USPIO, and the quantity could be visualized by overlaying the iron density image displayed with a color scale on the 70-keV image. CONCLUSION: Our results suggested that macrophages could be both quantified and visualized by USPIO on dual energy CT.

Noninvasive Computed Tomography-Derived Fractional Flow Reserve Based on Structural and Fluid Analysis: Reproducibility of On-site Determination by Unexperienced Observers.

OBJECTIVE: The aim of this study was to evaluate the reproducibility of computed tomography (CT)-derived fractional flow reserve (FFR) determined on site by inexperienced observers using a postprocessing software based on structural and fluid analysis. METHODS: Using 21 coronary vessels in 7 patients who underwent 320-row coronary CT angiography and catheter-FFR, 2 independent inexperienced observers (A: a student radiation technologist; B: a nonmedical staff) determined the CT-FFR using a postprocessing software. After a 20-minute training session, both observers postprocessed all vessels and readjusted their settings after another training/feedback. These CT-FFRs were compared with values determined by an expert analyst. RESULTS: The mean processing times were 23 ± 4 minutes (automatic), 71 ± 5 minutes (observer A), and 57 ± 7 minutes (observer B) per patient. The initial correlations with expert data were r = 0.92 (observer A) and 0.73 (observer B) and increased to 0.83 for observer B after additional training. The final absolute difference with the expert data was 0.000 to 0.020. The correlation between catheter-FFR and expert CT-FFR was r = 0.76. CONCLUSIONS: The CT-derived FFR on-site postprocessing software showed good reproducibility for measurements by inexperienced observers.

[The Effect of Reconstruction Method on the Ability to Analyze of FFR Using CT].

BACKGROUND: Invasive-fractional flow reserve (FFR) is the reference standard to evaluate functional ischemia of coronary arteries, and is used to decide if percutaneous transluminal coronary angioplasty is necessary. Recently, computed tomography-derived FFR (CT-FFR) is emerged as an alternative non-invasive method. OBJECTIVES: To evaluate the effect of reconstruction methods and image parameters on the accuracy of CT-FFR calculation. METHODS: A total of 26 segments in the consecutive 10 coronary CT angiography (CCTA) studies were evaluated. All studies were reconstructed using three different techniques: 1) filtered back projection (FBP), 2) adaptive iterative dose reduction 3D (AIDR 3D), and 3) forward projected model-based iterative reconstruction solution (FIRST). Vessel segmentation was performed automatically by CT-FFR software, with manual adjustment if necessary. Calculated CT-FFR was compared with the invasive FFR data. RESULTS: Compared to FBP, AIDR 3D and FIRST resulted in more successful automatic segmentation. When using FIRST, 7 segments (27%) were completed without manual adjustment. These segments had relatively larger vessel diameter, higher CT number, and lower noise. The difference between the calculated CT-FFR and invasive-FFR was 0.02±0.01. Among the remaining, 10 segments (38%) required manual adjustments of centerline, 7 segments (27%) required manual adjustments of contour, and 2 segments (8%) did not reach to the CT-FFR calculation. CONCLUSION: AIDR 3D and FIRST were useful for reliable automatic segmentation and analysis of CT-FFR.

A strategy to optimize radiation exposure for non-contrast head CT: comparison with the Japanese diagnostic reference levels.

PURPOSE: To describe how we performed a protocol review, analyzed data, identified opportunities to reduce radiation exposure, and then implemented a new imaging protocol for non-contrast adult head CT at our institution with reduced radiation exposure, using the Japanese diagnostic reference levels (DRLs) as the reference. METHODS: After analyzing the CT dose index (CTDIvol) and dose length product (DLP) in all non-contrast adult head CT examinations performed during a 3-month period (September to November 2015) in order to identify a specific protocol that contributed to the above-DRL-level radiation exposure observed for non-contrast adult head CT at our institution, phantom studies with objective and subjective image quality analyses were performed to develop a new imaging protocol. After implementing the new protocol, its feasibility was evaluated in terms of radiation exposure, prevalence of significant disease detection, and subjective image quality. RESULTS: The review of 2040 examinations revealed that a helical protocol (CTDIvol = 93.4 mGy) with one of four CT scanners mainly contributed to the above-DRL-level radiation exposure (mean DLP at this scanner = 1401.2 mGy cm) in non-contrast adult head CT at our institution. To replace this protocol, the phantom study identified a wide-volume scan using 120 kVp, 350 mAs, a 4-cm detector, a slice thickness of 5 mm, and a CTDIvol of 69.8 mGy as a new protocol that yielded comparable image quality to the existing protocol. After the implementation of the new protocol, the overall mean DLP reduced to 1365 mGy cm without any apparent degradation of image quality. No significant decrease in the prevalence of significant findings after protocol revision was noted. CONCLUSION: We report the successful implementation of a new protocol with reduced radiation exposure for non-contrast head CT examinations.