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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

New transluminal attenuation gradient derived from dynamic coronary CT angiography: diagnostic ability of ischemia detected by N-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 N-ammonia positron emission tomography (PET). Data from 48 consecutive patients who had undergone both dynamic CCTA and N-ammonia PET were retrospectively analyzed.

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.

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.

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).

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

Unlabelled: 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.

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.

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.

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 T weighted imaging (FSE-T2WI), RADAR T 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.

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).