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.

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.

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.

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.

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.

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.

Feasible scan timing for 320-row coronary CT angiography generated by the time to peak in the ascending aorta.

PURPOSE: A 320-row CT scanner can briefly scan the entire heart. Therefore, the feasible scan timing is required. The aim of this study was to propose a refined method for feasible scan timing for coronary CT angiography (CCTA) using a time-density curve of the ascending aorta (AAo). METHODS: One-hundred and twenty-nine patients were prospectively enrolled. All patients were performed test-bolus method. For the initial 65 patients, the scan timing was determined as a 3.0 s delay at the peak time in the AAo, which was defined as the conventional protocol (COV-P). For the next 64 patients, a scan timing of 1.0, 3.0, or 5.0 s delay was determined according to the interval from the contrast media arrival to peak time in the AAo, which was defined as the arrival to peak protocol (AP-P). The optimal scan timing was identified by the measurement of CT number in the left atrium, left ventricle, AAo, and descending aorta. The coronary enhancement and heterogeneity were compared between the two protocols. RESULTS: The optimal scan timing was significantly higher in the AP-P than in the COV-P (85.9% vs. 61.5%, p = 0.0017). The CT number in the left circumflex artery (LCX) was significantly higher in the AP-P than the COV-P (344.5 Hounsfield units vs. 316.3 Hounsfield units, p = 0.0484). The heterogeneous index of the LCX was significantly greater for the COV-P than the AP-P (-36.8 vs. -25.8, p = 0.0028). CONCLUSIONS: The AP-P can be used to determine the optimal scan timing for CCTA and contributes to stable coronary enhancement.

Assessment by airway ellipticity on cine-MRI to differentiate severe obstructive sleep apnea.

INTRODUCTION: The severity of obstructive sleep apnea (OSA) is assessed by the apnea-hypopnea index (AHI) determined from polysomnography (PSG). However, PSG requires a specialized facility with well-trained specialists and takes overnight. Therefore, simple tools, which could distinguish severe OSA, have been needed before performing PSG. OBJECTIVES: We propose the new index using cine-MRI as a screening test to differentiate severe OSA patients, who would need PSG and proper treatment. METHODS: Thirty-six patients with suspected OSA (mean age 54.6 y, mean AHI 52.6 events/h, 33 males) underwent airway cine-MRI at the fourth cervical vertebra level during 30 s of free breathing and PSG. The minimum airway ellipticity (AE) in 30 s duration was measured, and was defined as the severity of OSA. Patients were divided into severe OSA, not-severe OSA, and normal groups, according to PSG results. The comparison of AE between any two of the three groups was performed by Wilcoxon rank-sum test. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off of AE for identifying severe OSA patients. RESULTS: The minimum AE for severe OSA was significantly lower than that for not-severe OSA and normal (severe, 0.17 ± 0.16; not severe, 0.31 ± 0.17; normal, 0.38 ± 0.19, P < .05). ROC analysis revealed that the optimal cutoff of the minimum AE 0.21 identified severe OSA patients, with an area under the curve of 0.75, 68% sensitivity, and 83% specificity. CONCLUSION: AE is a feasible quantitative index, and a promising screening test for detecting severe OSA patients.

Dynamic flow imaging using 320-detector row CT and motion coherence analysis in coronary aneurysms associated with Kawasaki disease.

Introduction We propose a new dynamic flow imaging using 320-detector row CT, and investigate the assessment of coronary flow in aneurysms of Kawasaki disease in adulthood. METHODS: Six patients with Kawasaki disease and coronary aneurysms associated (26.7 years old) and six controls were enrolled. Dynamic coronary CT angiography with 320-row CT was continuously performed at mid-diastole throughout 15-25 cardiac cycles with prospective Electrocardiogram gating after injection of contrast media. Dynamic data sets of 15-25 cycles were computed into 90-100 data sets by motion coherence image processing. Next, time-density curves for coronary arteries were calculated for all the phases. On the basis of the maximum slope method, coronary flow index was defined as the ratio of the maximum upslope of the attenuation of coronary arteries to the upslope of the attenuation of ascending aorta on the time-density curves. Coronary flow indexes for the proximal and distal sites of coronary arteries and intra-aneurysm were measured.

Efficacy of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) for shoulder magnetic resonance (MR) imaging.

OBJECTIVES: To elucidate the utility of PROPELLER for motion artefact reduction on shoulder MRI and to examine the influence of streak artefacts on diagnosis of clinical images. METHODS: 15 healthy volunteers and 48 patients underwent shoulder MRI with/without PROPELLER (coronal oblique proton density-fast spin echo [PD-FSE], sagittal oblique T2-FSE). In a volunteer study, all sequences were performed in both static and exercise-loaded conditions. Two radiologists graded artefacts and delineation of various anatomical structures in the volunteer study and motion and streak artefacts in the clinical study. Mean scores were compared between sequences with/without PROPELLER. In the clinical study, mean scores of motion artefacts were compared with mean scores of streak artefacts. Wilcoxon signed-rank test was used for all comparisons. RESULTS: In both studies, PROPELLER significantly reduced motion artefacts (P<0.05). In the volunteer study, it significantly improved delineations in sagittal oblique images in the exercise-loaded condition (P<0.05). In the clinical study, streak artefacts appeared dominantly on images with PROPELLER (P<0.05), but influenced diagnosis to a lesser extent than motion artefacts. CONCLUSION: PROPELLER can reduce motion artefacts in shoulder MRI. While it does cause streak artefacts, it affects diagnosis to a lesser extent.

Efficacy of the radial acquisition regime (RADAR) for acquiring head and neck MR images.

OBJECTIVE: To investigate the efficacy of the radial acquisition regime (RADAR) for acquiring head and neck MR images. METHODS: 15 healthy volunteers underwent imaging with 4 sequences [fast spin echo T2 weighted imaging (FSE-T2WI), RADAR T2 weighted imaging (RADAR-T2WI), single-shot echo planar imaging diffusion-weighted imaging (SS-EPI-DWI) and RADAR diffusion-weighted imaging (RADAR-DWI)]. Both standard images and images during periodic mouth motion were acquired. Two radiologists scored the overall image artefacts and detectability of several anatomical structures without knowledge of sequence type. For each sequence, image distortion was quantitatively compared by the anteroposterior to right-left ratio of several anatomical structures. The mean scores of artefacts and distortion of several anatomical structures were compared using the multiple comparison test. The detectabilities were compared using the Wilcoxon signed-rank test. RESULTS: Regardless of mouth motion, RADAR-T2WI was significantly superior to FSE-T2WI in artefacts and oral-area detectability (p < 0.01), and RADAR-DWI was significantly superior to SS-EPI-DWI in terms of artefacts (p < 0.01). In terms of image distortion, RADAR-DWI was significantly superior to SS-EPI-DWI (p < 0.01). CONCLUSION: RADAR-T2WI could replace FSE-T2WI as a conventional T2WI protocol for the head and neck. For the RADAR-DWI sequence, validation studies are needed. Advances in knowledge: RADAR-T2WI was superior to FSE-T2WI with regard to artefacts and detectability, and RADAR-DWI was superior in terms of artefacts compared with SS-EPI-DWI.

Volumetric measurement of artificial pure ground-glass nodules at low-dose CT: Comparisons between hybrid iterative reconstruction and filtered back projection.

PURPOSE: To compare hybrid iterative reconstruction (HIR) with filtered back projection (FBP) in the volumetry of artificial pure ground-glass nodules (GGNs) with low-dose computed tomography (CT). MATERIALS AND METHODS: Artificial GGNs (10 mm-diameter, 523.6 mm(3), -660 HU) in an anthropomorphic chest phantom were scanned by a 256-row multi-slice CT with three dose levels (10, 30, 100 mAs). Each scan was repeated six times. Each set was reconstructed by FBP and HIR at 0.625-mm thickness. The volumes of artificial GGNs placed at the lung apex and middle lung field of the chest phantom were measured by two observers. Semi-automated measurements were performed by clicking the cursor in the center of GGNs, and manual measurements were performed by tracing GGNs on axial section. Modification of the trace was added on a sagittal or coronal section if necessary. Measurement errors were calculated for both the FBP and HIR at each dose level. We used the Wilcoxon signed rank test to identify any significant difference between the measurement errors of the FBP and HIR. Inter-observer, intra-observer, and inter-scan variabilities were evaluated by Bland Altman analysis with limits of agreements given by 95% confidence intervals. RESULTS: There were significant differences in measurement errors only at the lung apex between FBP and HIR with 10 mAs in both the semi-automated (observer 1, -37% vs. 7.2%; observer 2, -39% vs. 1.9%) and manual methods (observer 1, -29% vs. 7.5%; observer 2, -30% vs. 1.1%), respectively (P<0.05). HIR provided each variability equal to or less than one half of that of FBP at 10 mAs in both methods. In the semi-automated method, the inter-observer and intra-observer variabilities obtained by HIR at 10 mAs were -11% to 17% and -6.7% to 6.7%, whereas those for FBP at 10 mAs were -29% to 30% and -38% to 20%, respectively. The inter-scan variability for FBP at 100 mAs vs. HIR at 10 mAs was -9.5% to 11%, and that for FBP at 100 mAs vs. FBP at 10 mAs was -73% to 32%. In the manual method, the inter-observer and intra-observer variabilities for HIR at 10 mAs were -14% to 22% and -9.8% to 22%, and those for FBP at 10 mAs were -45% to 36% and -31% to 28%, respectively. The inter-scan variability for FBP at 100 mAs vs. HIR at 10 mAs was -7.4% to 23%, and that for FBP at 100 mAs vs. FBP at 10 mAs was -52% to 26%. CONCLUSION: HIR is superior to FBP in the volumetry of artificial pure GGNs at lung apex with low-dose CT.