Right Ventricular Strain With 4-Dimensional Computed Tomography Identifies Pulmonary Hypertension in Adults With Repaired Tetralogy of Fallot.

BACKGROUND: This study evaluated right ventricular (RV) volume, strain, and morphology using cardiac 4-dimensional computed tomography (4D-CT) to detect pulmonary hypertension (PH) in adults with repaired tetralogy of Fallot (TOF) scheduled for transcatheter pulmonary valve implantation (TPVI). METHODS AND RESULTS: Using cardiac 4D-CT data, we calculated RV strain in 3 different geometries and RV outflow tract (RVOT) mass in 42 patients with repaired TOF. We compared RV strain and RVOT mass between patients with and without PH. Receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic performance of these measurements for identifying PH. Four-chamber (4ch) strain was significantly smaller for patients with (n=10) than without (n=32) PH (8.8±1.7% vs. 11.1±2.4%, respectively; P<0.01), whereas RVOT mass was significantly larger in the PH group (12.5±3.5 vs. 9.2±3.2 cm2; P<0.01). ROC analysis of the diagnostic performance revealed that the respective sensitivity and specificity was 70% and 84% (area under the curve [AUC]=0.784) for 4ch strain of 8.8%; 80% and 69% (AUC=0.766) for RVOT mass of 10.7 cm2; and 80% and 81% (AUC=0.844) for a 4ch strain/RVOT mass ratio of 0.97. CONCLUSIONS: RVOT mass and 4ch strain obtained from cardiac 4D-CT may be helpful for identifying PH in patients with repaired TOF.

Influence of cardiac cycle on myocardial extracellular volume fraction measurements with dual-layer computed tomography.

Background: In cardiac computed tomography (CT), the best image quality is obtained at mid-diastole at low heart rates (HRs) and at end-systole at high HRs. On the other hand, extracellular volume (ECV) measurements may be influenced by the cardiac phase. Therefore, we aimed to clarify the influence of the cardiac phase on the image quality and ECV values obtained using dual-layer spectral computed tomography (DLCT). Methods: Fifty-five patients (68.0±14.5 years; 26 men) with cardiac diseases who underwent retrospective electrocardiogram-gated myocardial CT delayed enhancement (CTDE) between February 2019 to April 2022 were enrolled. The ECVs at the right ventricle (RV) and left ventricle (LV) walls in the end-systolic and mid-diastolic phases were calculated using iodine-density measurements from CTDE spectral data. Iodine-density image quality was classified on a 4-point scale. ECV and image quality across cardiac phases were compared using the t-test and Wilcoxon signed-rank test, respectively. Inter- and intraobserver variability were evaluated using intraclass correlation coefficient (ICC) values. Results: The ECV of the septal regions during mid-diastole was significantly higher than that during end-systole. Other regions showed similar ECV measurements in both groups (P=0.13-0.97), except for the LV anterior wall and LV posterior wall at the base-ventricular level. The image-quality score in end-systole was significantly higher than that in mid-diastole (systole vs. diastole: 3.6±0.5 vs. 3.2±0.7; P=0.0195). Intra- and interobserver variabilities for RV ECV measurements at the end-systolic phase were superior to those at the mid-diastolic phase, whereas the corresponding values for LV ECV measurements were similar. Conclusions: Septal ECV showed small but significant differences while other region ECV showed no difference during the cardiac cycle. RV ECV measurements in the end-systolic phase were more reproducible than those in the mid-diastolic phase.

The Feasibility of Deep Learning-Based Reconstruction for Low-Tube-Voltage CT Angiography for Transcatheter Aortic Valve Implantation.

OBJECTIVE: The purpose of this study is to evaluate the efficacy of deep learning reconstruction (DLR) on low-tube-voltage computed tomographic angiography (CTA) for transcatheter aortic valve implantation (TAVI). METHODS: We enrolled 30 patients who underwent TAVI-CT on a 320-row CT scanner. Electrocardiogram-gated coronary CTA (CCTA) was performed at 100 kV, followed by nongated aortoiliac CTA at 80 kV using a single bolus of contrast material. We used hybrid-iterative reconstruction (HIR), model-based IR (MBIR), and DLR to reconstruct these images. The contrast-to-noise ratios (CNRs) were calculated. Five-point scales were used for the overall image quality analysis. The diameter of the aortic annulus was measured in each reconstructed image, and we compared the interobserver and intraobserver agreements. RESULTS: In the CCTA, the CNR and image quality score for DLR were significantly higher than those for HIR and MBIR ( P < 0.01). In the aortoiliac CTA, the CNR for DLR was significantly higher than that for HIR ( P < 0.01) and significantly lower than that for MBIR ( P ≤ 0.02). The image quality score for DLR was significantly higher than that for HIR ( P < 0.01). No significant differences were observed between the image quality scores for DLR and MBIR. The measured aortic annulus diameter had high interobserver and intraobserver agreement regardless of the reconstruction method (all intraclass correlation coefficients, >0.89). CONCLUSIONS: In low tube voltage TAVI-CT, DLR provides higher image quality than HIR, and DLR provides higher image quality than MBIR in CCTA and is visually comparable to MBIR in aortoiliac CTA.

[Possible Radiation Dose Reduction in Abdominal Plain CT Using Deep Learning Reconstruction].

PURPOSE: The purposes of this study were to evaluate the low-contrast detectability of CT images assuming hepatocellular carcinoma and to determine whether dose reduction in abdominal plain CT imaging is possible. METHODS: A Catphan 600 was imaged at 350, 250, 150, and 50 mA using an Aquilion ONE PRISM Edition (Canon) and reconstructed using deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR). A low-contrast object-specific contrast-to-noise ratio (CNRLO) was measured and compared in a 5-mm module with a CT value difference of 10 HU, assuming hepatocellular carcinoma; a visual examination was also performed. Moreover, an NPS within a uniform module was measured. RESULTS: CNRLO was higher for DLR at all doses (1.12 at 150 mA for DLR and 1.07 at 250 mA for MBIR). On visual evaluation, DLR could detect up to 150 mA and MBIR up to 250 mA. The NPS was lower for DLR at 0.1 cycles/mm at 150 mA. CONCLUSION: The low-contrast detection performance was better with DLR than with MBIR, indicating the possibility of dose reduction.

Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study.

PURPOSE: We assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging. MATERIALS AND METHODS: A Catphan® 600 phantom was scanned by DL-Spectral CT at various radiation doses. We reconstructed the VMIs obtained at 50, 70, and 100 keV. SECT (120 kVp) images were acquired at the same radiation doses. The standard deviations of the CT number and noise power spectrum (NPS) were calculated for noise characterization. We evaluated the spatial resolution by determining the 10% task-based transfer function (TTF) level, and we assessed the task-based detectability index (d'). RESULTS: Regardless of the radiation dose, the noise was the lowest at 70 keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120 kVp images. The spatial resolution was higher for 70 keV VMI compared to the other VMIs, except for high-contrast objects. The d' of 70 keV VMI was the highest among the VMI and 120 kVp images at all radiation doses and contrast settings. The d' of the 70 keV VMIs at the minimum dose was higher than that at the maximum dose in any other image. CONCLUSION: The physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70 keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.

A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

PURPOSE: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation. METHODS: A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge. Then, another 800 images were used, i.e., 400 with and 400 without surgical sponge. The detection characteristics of retained sponges between the model and a general observer with 10-year clinical experience were analyzed using the receiver operator characteristics. RESULTS: The following values from the deep learning model and observer were, respectively, derived: Cutoff values of probability were 0.37 and 0.45; areas under the curves were 0.87 and 0.76; sensitivity values were 85% and 61%; and specificity values were 73% and 92%. CONCLUSION: For the detection of surgical sponges, we concluded that the deep learning model has higher sensitivity, while the human observer has higher specificity. These characteristics indicate that the deep learning system that is complementary to humans could support the clinical workflow in operation rooms for prevention of retained surgical items.

Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies.

OBJECTIVES: To demonstrate the effect of an improved deep learning-based reconstruction (DLR) algorithm on Ultra-High-Resolution Computed Tomography (U-HRCT) scanners. METHODS: Clinical and phantom studies were conducted. Thirty patients who underwent contrast-enhanced CT examination during the follow-up period were enrolled. Images were reconstructed using improved DLR [termed, New DLR, i.e., Advanced Intelligent Clear-IQ Engine (AiCE) Body Sharp] and conventional DLR (Conv DLR, AiCE Body) algorithms. Two radiologists assessed the overall image quality using a 5-point scale (5 = excellent; 1 = unacceptable). The noise power spectra (NPSs) were calculated to assess the frequency characteristics of the image noise, and the square root of area under the curve (√AUC NPS) between 0.05 and 0.50 cycle/mm was calculated as an indicator of the image noise. Dunnett's test was used for statistical analysis of the visual evaluation score, with statistical significance set at p < 0.05. RESULTS: The overall image quality of New DLR was better than that of the Conv DLR (4.2 ± 0.4 and 3.3 ± 0.4, respectively; p < 0.0001). All New DLR images had an overall image quality score above the average or excellent. The √AUCNPS value of New DLR was lower than that of Conv DLR (13.8 and 14.2, respectively). The median values of reconstruction time required with New DLR and Conv DLR were 5.0 and 7.8 min, respectively. CONCLUSIONS: The new DLR algorithm improved the image quality within a practical reconstruction time. ADVANCES IN KNOWLEDGE: The new DLR enables us to choose whether to improve image quality or reduce the dose.

Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study.

Background: Brain CT needs more attention to improve the extremely low image contrast and image texture. Purpose: To evaluate the performance of iterative progressive reconstruction with visual modeling (IPV) for the improvement of low-contrast detectability (IPV-LCD) compared with filtered backprojection (FBP) and conventional IPV. Materials and methods: Low-contrast and water phantoms were used. Helical scans were conducted with the use of a CT scanner with 64 detectors. The tube voltage was set at 120 kVp; the tube current was adjusted from 60 to 300 mA with a slice thickness of 0.625 mm and from 20 to 150 mA with a slice thickness of 5.0 mm. Images were reconstructed with the FBP, conventional IPV, and IPV-LCD algorithms. The channelized Hotelling observer (CHO) model was applied in conjunction with the use of low-contrast modules in the low-contrast phantom. The noise power spectrum (NPS) and normalized NPS were calculated. Results: At the same standard and strong levels, the IPV-LCD method improved low-contrast detectability compared with the conventional IPV, regardless of contrast-rod diameters. The mean CHO values at a slice thickness of 0.625 mm were 1.83, 3.28, 4.40, 4.53, and 5.27 for FBP, IPV STD, IPV-LCD STD, IPV STR, and IPV-LCD STR, respectively. The normalized NPS for the IPV-LCD STD and STR images were slightly shifted to the higher frequency compared with that for the FBP image. Conclusion: IPV-LCD images further improve the low-contrast detectability compared with FBP and conventional IPV images while maintaining similar FBP image appearances.

Importance of the heart rate in ultra-high-resolution coronary CT angiography with 0.35 s gantry rotation time.

PURPOSE: We investigated the effects of the heart rate (HR) on the motion artifact in coronary computed tomography angiography (CCTA) with ultra-high-resolution-CT (U-HRCT), and we clarified the upper limit of optimal HR in CCTA with U-HRCT in a comparison with conventional-resolution-CT (CRCT) on a cardiac phantom and in patients with CCTA. MATERIALS AND METHODS: A pulsating cardiac phantom equipped with coronary models was scanned at static and HR simulations of 40-90 beats/min (bpm) at 10-bpm intervals using U-HRCT and CRCT, respectively. The sharpness and lumen diameter of the coronary model were quantitatively compared between U-HRCT and CRCT stratified by HR in the phantom study. We also assessed the visual inspections of clinical images in CCTA with U-HRCT. RESULTS: At the HRs ≤ 60 bpm, the error of the lumen diameter of the U-HRCT tended to be smaller than that of the CRCT. However, at the HRs > 60 bpm, the inverse was shown. For the image sharpness, the U-HRCT was significantly superior to the CRCT (p < 0.05). In the visual assessment, the scores were negatively correlated with HRs in patients (Spearman r = - 0.71, p < 0.01). A receiver-operating characteristic analysis revealed the HR of 61 bpm as the optimal cutoff of the non-diagnostic image quality, with an area under the curve of 0.87, 95% sensitivity, and 71% specificity. CONCLUSION: At HRs ≤ 60 bpm, U-HRCT was more accurate in the imaging of coronary arteries than CRCT. The upper limit of the optimal HR in CCTA with U-HRCT was approx. 60 bpm.

Low-radiation dose scan protocol for preoperative imaging for dental implant surgery using deep learning-based reconstruction in multidetector CT.

OBJECTIVES: This study aimed to investigate the impact of a deep learning-based reconstruction (DLR) technique on image quality and reduction of radiation exposure, and to propose a low-dose multidetector-row computed tomography (MDCT) scan protocol for preoperative imaging for dental implant surgery. METHODS: The PB-1 phantom and a Catphan phantom 600 were scanned using volumetric scanning with a 320-row MDCT scanner. All scans were performed with a tube voltage of 120 kV, and the tube current varied from 120 to 60 to 40 to 30 mA. Images of the mandible were reconstructed using DLR. Additionally, images acquired with the 120-mA protocol were reconstructed using filtered back projection as a reference. Two observers independently graded the image quality of the mandible images using a 4-point scale (4, superior to reference; 1, unacceptable). The system performance function (SPF) was calculated to comprehensively evaluate image quality. The Wilcoxon signed-rank test was employed for statistical analysis, with statistical significance set at p value < 0.05. RESULTS: There was no significant difference between the image quality acquired with the 40-mA tube current and reconstructed with the DLR technique (40DLR), and that acquired with the reference protocol (3.00, 3.00, p = 1.00). The SPF at 1.0 cycles/mm acquired with 40DLR was improved by 156.7% compared to that acquired with the reference protocol. CONCLUSIONS: Our proposed protocol, which achieves a two-thirds reduction in radiation dose, can provide a minimally invasive MDCT scan of acceptable image quality for dental implant surgery.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.

OBJECTIVES: The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. METHODS: Artificial ground-glass nodules (6 mm and 10 mm diameters, -660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. RESULTS: DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. CONCLUSIONS: DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. ADVANCES IN KNOWLEDGE: DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

Improvement of Image Quality Using Hybrid Iterative Reconstruction with Noise Power Spectrum Model in Computed Tomography During Hepatic Arteriography.

OBJECTIVES: In CT during hepatic arteriography (CTHA), the addition of a noise power spectrum (NPS) model to conventional hybrid iterative reconstruction (HIR) may improve spatial resolution and reduce image noise. This study aims at assessing the image quality provided by HIR with a NPS model at CTHA. METHODS: This institutional review board-approved retrospective analysis included 26 patients with hepatocellular carcinomas (HCCs) who underwent CTHA. In all acquisitions, images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR), and AIDR enhanced (eAIDR) with the NPS model. Four radiologists analyzed the signal-to-noise ratio (SNR) of HCC nodules and its associated feeding arteries. The radiologists used a semiquantitative scale (-3 to +3) to rate the subjective image quality comparing both the FBP and eAIDR images with the AIDR images. RESULTS: The feeding arteries' attenuation was significantly higher in eAIDR compared to AIDR [514.3 ± 121.4 and 448.3 ± 107.3 Hounsfield units (HU), p < 0.05]. The image noise of eAIDR was significantly lower than that of FBP (15.2 ± 2.2 and 28.5 ± 4.8 HU, p < 0.05) and comparable to that of AIDR. The SNR of feeding arteries on eAIDR was significantly higher than on AIDR (34.1 ± 7.9 and 27.4 ± 6.3, p < 0.05). Subjective assessment scores showed that eAIDR provided better visibility of feeding arteries and overall image quality compared to AIDR (p < 0.05). The HCC nodule visibility was not significantly different among the three reconstructions. CONCLUSION: In CTHA, eAIDR improved the visibility of feeding arteries associated with HCC nodules without compromising nodule detection.

Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study.

PURPOSE: In an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol. METHODS: For the normal-sized abdominal models, a Catphan 600 was scanned by U-HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model-based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms. RESULTS: Deep learning-based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the 100% and 50% radiation doses (100%, 50%, DLR: 15.4, 16.9 vs MBIR: 10.2, 15.6 Hounsfield units: HU). However, at the 25% radiation dose, the image noise in DLR was lower than that in MBIR (16.7 vs. 26.6 HU). The spatial frequency at 10% of the modulation transfer function (MTF) in DLR was 1.0 cycles/mm, slightly lower than that in MBIR (1.05 cycles/mm) at the 100% radiation dose. Even when the radiation dose decreased, the spatial frequency at 10% of the MTF of DLR did not change significantly (50% and 25% doses, 0.98 and 0.99 cycles/mm, respectively). CONCLUSION: Deep learning-based reconstruction performs more consistently at decreasing dose in abdominal ultrahigh-resolution CT compared to all other commercially available reconstruction algorithms evaluated.

Influence of beam hardening in dual-energy CT imaging: phantom study for iodine mapping, virtual monoenergetic imaging, and virtual non-contrast imaging.

In this study, we investigated the influence of beam hardening on the dual-energy computed tomography (DECT) values of iodine maps, virtual monoenergetic (VME) images, and virtual non-contrast (VNC) images. 320-row DECT imaging was performed by changing the x-ray tube energy for the first and second rotations. DECT values of 5 mg/mL iodine of the multi-energy CT phantom were compared with and without a 2-mm-thick attenuation rubber layer (~700 HU) wound around the phantom. It was found that the CT density values UH, with/without the rubber layer had statistical differences in the iodine map (184 ± 0.7 versus 186 ± 1.8), VME images (125 ± 0.3 versus 110 ± 0.4), and VNC images (-58 ± 0.7 versus -76 ± 1.7) (p < 0.010 for all). This suggests that iodine mapping may be underestimated by DECT and overestimated by VME imaging because of x-ray beam hardening. The use of VNC images instead of plain CT images requires further investigation because of underestimation.

Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study.

OBJECTIVES: This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner. METHODS: The mandible tooth phantom with and without the lesion was scanned using super-high-resolution, high-resolution (HR), and normal-resolution (NR) modes. Images were reconstructed with deep learning-based reconstruction (DLR) and hybrid iterative reconstruction (HIR) using the MAR algorithm. Two dental radiologists independently graded the degree of metal artefact (1, very severe; 5, minimum) and lesion shape reproducibility (1, slight; 5, almost perfect). The signal-to-artefact ratio (SAR), accuracy of the CT number of the lesion, and image noise were calculated quantitatively. The Tukey-Kramer method with a p-value of less than 0.05 was used to determine statistical significance. RESULTS: The HRDLR visual score was better than the NRHIR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, p < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, p = 0.0005). The SAR of HRDLR was significantly better than that of NRHIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HRDLR was lower than that in NRHIR (0.8% in HRDLR and 23.8% in NRIR). The image noise of HRDLR was lower than that of NRHIR (15.7 ± 1.4 and 51.6 ± 15.3, p < 0.0001). CONCLUSIONS: Our study demonstrated that the combination of HR mode and DLR in UHRCT scanner improved the impact of the MAR algorithm in the oral cavity.

A novel fast kilovoltage switching dual-energy CT with deep learning: Accuracy of CT number on virtual monochromatic imaging and iodine quantification.

PURPOSE: A novel fast kilovoltage switching dual-energy CT with deep learning [Deep learning based-spectral CT (DL-Spectral CT)], which generates a complete sinogram for each kilovolt using deep learning views that complement the measured views at each energy, was commercialized in 2020. The purpose of this study was to evaluate the accuracy of CT numbers in virtual monochromatic images (VMIs) and iodine quantifications at various radiation doses using DL-Spectral CT. MATERIALS AND METHODS: Two multi-energy phantoms (large and small) using several rods representing different materials (iodine, calcium, blood, and adipose) were scanned by DL-Spectral CT at varying radiation doses. Images were reconstructed using three reconstruction parameters (body, lung, bone). The absolute percentage errors (APEs) for CT numbers on VMIs at 50, 70, and 100 keV and iodine quantification were compared among different radiation dose protocols. RESULTS: The APEs of the CT numbers on VMIs were <15% in both the large and small phantoms, except at the minimum dose in the large phantom. There were no significant differences among radiation dose protocols in computed tomography dose index volumes of 12.3 mGy or larger. The accuracy of iodine quantification provided by the body parameter was significantly better than those obtained with the lung and bone parameters. Increasing the radiation dose did not always improve the accuracy of iodine quantification, regardless of the reconstruction parameter and phantom size. CONCLUSION: The accuracy of iodine quantification and CT numbers on VMIs in DL-Spectral CT was not affected by the radiation dose, except for an extremely low radiation dose for body size.

New transluminal attenuation gradient derived from dynamic coronary CT angiography: diagnostic ability of ischemia detected by 13N-ammonia PET.

Coronary computed tomography angiography (CCTA) has low specificity for detecting significant functional coronary stenosis. We developed a new transluminal attenuation gradient (TAG)-derived dynamic CCTA with dose modulation, and we investigated its diagnostic performance for myocardial ischemia depicted by 13N-ammonia positron emission tomography (PET). Data from 48 consecutive patients who had undergone both dynamic CCTA and 13N-ammonia PET were retrospectively analyzed. Dynamic CCTA was continuously performed in mid-diastole for five cardiac cycles with prospective electrocardiography gating after a 10-s contrast medium injection. One scan of the dynamic CCTA was performed as a boost scan for conventional CCTA at the peak phase of the ascending aorta. Absolute TAG values at five phases around the boost scan were calculated. The dynamic TAG index (DTI) was defined as the ratio of the maximum absolute TAG to the standard deviation of five TAG values. We categorized the coronary territories as non-ischemia or ischemia based on the 13N-ammonia PET results. A receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff of the DTI for identifying ischemia. The DTI was significantly higher for ischemia compared to non-ischemia (8.8 ± 3.9 vs. 4.6 ± 2.0, p < 0.01). The ROC analysis revealed 5.60 as the optimal DTI cutoff to detect ischemia, with an area under the curve of 0.87, 85.7% sensitivity, and 76.2% specificity. TAG provided no additional diagnostic value for the detection of ischemia. We propose the DTI derived from dynamic CCTA as a novel coronary flow index. The DTI is a valid technique for detecting functional coronary stenosis.

Improved scan method for dental imaging using multidetector computed tomography: a phantom study.

OBJECTIVES: This study aimed to propose an improved scan method to shorten irradiation time and reduce radiation exposure. METHODS: The maxilla of a human head CT phantom and a Catphan phantom were used for qualitative and quantitative assessment, respectively. The phantoms were scanned by a 160-row multidetector CT scanner using volumetric and helical scanning. In volumetric scanning, the tube current varied from 120 to 60 to 30 to 20 mA with a tube voltage of 120 kV. Images were reconstructed with a bone kernel using iterative reconstruction (IR) and filtered back projection. As a reference protocol, helical scanning was performed using our clinical setting with 120 kV. Two dental radiologists independently graded the quality of dental images using a 4-point scale (4, superior to reference; 1, unacceptable). For the quantitative assessment, we assessed the system performance from each scan. RESULTS: There was no significant difference between the image quality of volumetric scanning using the 60 mA protocol reconstructed with IR and that of the reference (3.08 and 3.00, p = 0.3388). The system performance values at 1.0 cycles/mm of volumetric scanning and 60 mA protocol reconstructed with IR and reference were 0.0038 and 0.0041, respectively. The effective dose of volumetric scanning using the 60 mA protocol was 51.8 µSv, which is a 64.2% reduction to that of the reference. CONCLUSIONS: We proposed an improved scan method resulting in a 64.2% reduction of radiation dose with one-fourth of irradiation time by combining volumetric scanning and IR technique in multidetector CT.

Effect of scan mode and focal spot size in airway dimension measurements for ultra-high-resolution computed tomography of chronic obstructive pulmonary disease: A COPDGene phantom study.

PURPOSE: Quantitative evaluations of airway dimensions through computed tomography (CT) have revealed a good correlation with airflow limitation in chronic obstructive pulmonary disease. However, large inaccuracies have been known to occur in CT airway measurements. Ultra-high-resolution CT (UHRCT) might improve measurement accuracy using precise scan modes with minimal focal spot. We assessed the effects of scan mode and focal spot size on airway measurements in UHRCT. METHODS: COPDGene Ⅱ phantom, comprising a plastic tube mimicking human airway of inner diameter 3 mm, wall thickness 0.6 mm, and inclination 30 degrees was scanned at super high resolution (SHR, beam collimation of 0.25 mm × 160 rows) and high resolution (HR, beam collimation of 0.5 mm × 80 rows) modes using UHRCT. Each acquisition was performed both with small (0.4 × 0.5 mm) and large (0.6 × 1.3 mm) focal spots. The wall area percentage (WA%) was calculated as the percentage of total airway area occupied by the airway wall. Statistical analysis was performed to compare the WA% measurement errors for each scan mode and focal spot size. RESULTS: The WA% measurement errors in the SHR mode were 9.8% with a small focal spot and 18.8% with a large one. The measurement errors in the HR mode were 13.3% with a small focal spot and 21.4% with a large one. There were significant differences between each scan mode and focal spot size (p < 0.05). CONCLUSIONS: The SHR mode with a small focal spot could improve airway measurement accuracy of UHRCT.

Low Radiation Dose and High Image Quality of 320-Row Coronary Computed Tomography Angiography Using a Small Dose of Contrast Medium and Refined Scan Timing Prediction.

OBJECTIVE: The aim of the study was to investigate the feasibility of coronary computed tomography (CT) angiography with a low kilovoltage peak scan and a refined scan timing prediction using a small contrast medium (CM) dose. METHODS: In protocol A, 120-kVp scanning and a standard CM dose were used. The scan timing was fixed. In protocol B, 80 kVp and a 60% CM dose were used. The scan timing was determined according to the interval from the CM arrival to the peak time in the ascending aorta. We measured the CT number and recorded the radiation dose. RESULTS: Higher CT numbers were observed in the left circumflex (proximal, P = 0.0235; middle, P = 0.0007; distal, P < 0.0001) in protocol B compared with protocol A. The radiation dose in protocol B was significantly lower than in protocol A (2.2 ± 0.9 vs 4.3 ± 1.7 mSv). CONCLUSIONS: Low-contrast, low-radiation dose, high-image quality coronary CT angiography can be performed with low kilovoltage peak scanning and a refined scan timing prediction.