Publications by authors named "Funama Y"
Background: This study aimed to determine whether spectral imaging with dual-energy computed tomography (CT) can improve diagnostic performance for coronary plaque characterization.
Methods And Results: We conducted a retrospective analysis of 30 patients with coronary plaques, using coronary CT angiography (dual-layer CT) and intravascular ultrasound (IVUS) studies. Based on IVUS findings, patients were diagnosed with either vulnerable or stable plaques.
Purpose: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data.
Methods: We retrospectively analyzed scan data of adult patients who underwent body CT during two periods relative to DLR implementation at our facility: a 12-month pre-DLR phase (n = 5553) using hybrid iterative reconstruction and a 12-month post-DLR phase (n = 5494) with routine CT reconstruction transitioning to DLR. To ensure comparability between two groups, we employed propensity score matching 1:1 based on age, sex, and body mass index.
Rationale And Objectives: To evaluate the performance of various multimodal large language models (LLMs) in the Japanese Diagnostic Radiology Board Examinations (JDRBE) both with and without images.
Materials And Methods: Five multimodal LLMs-GPT-4o, Claude 3 Opus, GPT-4 Vision, Gemini Flash 1.5, and Gemini Pro 1.
Article Synopsis
- The air-gap method is a technique used in medical imaging that creates a space of air between the radiation source and the patient to minimize radiation scattering and improve dose distribution.
- This study focused on evaluating the effectiveness of the air-gap method in reducing radiation exposure for pediatric patients undergoing computed tomography (CT) scans, using a specific neonate phantom and a 64 detector-row CT scanner.
- Results showed that the air-gap method reduced exposure dose and image noise by approximately 10% and 15%, respectively, compared to conventional methods, indicating its potential benefits in pediatric imaging (p < 0.05).
Introduction And Objectives: To compare the real time skin dosimeter values at lens between with the lens included in the scan range (orbitomeatal base line [OML]) or without the lens included in the scan range (superior orbitomeatal line [SOML]) at different tube voltages.
Materials And Methods: We used three pediatric anthropomorphic phantoms with a 64 detector-row computed tomography (CT) scanner with the OML- or SOML-protocol at different tube voltages during the head CT. A real time skin dosimeter was inserted into the phantom center of the head, and surfaces of the lens.
Article Synopsis
- - This study compared the effectiveness of super-resolution deep learning-based reconstruction (SR-DLR) with hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) in improving image quality of CT scans, focusing on different field of view sizes, radiation doses, and noise reduction levels.
- - Researchers utilized a Catphan phantom and reconstructed CT images using various methods (FBP, HIR, NR-DLR, SR-DLR) while assessing noise power and calculating noise magnitude and central frequency ratios.
- - Results showed that SR-DLR provided the best noise reduction (lower NMR scores) and improved high-contrast values and spatial resolution at both low and standard radiation doses compared to HIR and was on par with NR-D
The study aimed to compare the performance of photon-counting detector computed tomography (PCD CT) with high-resolution (HR)-plaque kernel with that of the energy-integrating detector CT (EID CT) in terms of the visualization of the lumen size and the in-stent stenotic portion at different coronary vessel angles. The lumen sizes in PCD CT and EID CT images were 2.13 and 1.
View Article and Find Full Text PDFTo assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac computed tomography (CT) for detecting myocardial edema, using MRI T2 mapping as the reference standard. This retrospective study protocol was approved by our institutional review board, which waived the requirement for written informed consent. Between December 2017 to February 2019, consecutive patients who had undergone T2 mapping for myocardial tissue characterization were identified.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to assess a new CT imaging protocol that uses low radiation and iodinated contrast medium doses while maintaining image quality through deep-learning reconstruction (DLR).
- It involved 148 patients, comparing two different voltage protocols (120-kVp and 80-kVp) in relation to the radiation dose, iodine dose, image noise, and overall image quality.
- Results showed that the 80-kVp protocol with DLR provided better image quality and significantly lower radiation and iodine doses compared to the higher voltage protocol.
Objective: The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography.
Methods: We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.
Article Synopsis
- Researchers used machine learning techniques to predict the likelihood of endoleaks following thoracic endovascular aneurysm repair (TEVAR) by analyzing patient data and vessel features from pre-operative CT scans.
- The study trained an extreme Gradient Boosting (XGBoost) system on data from 145 patients—14 with endoleaks and 131 without—and compared its efficacy to traditional measurement methods.
- Results showed that machine learning significantly outperformed conventional methods in predicting post-TEVAR endoleaks, achieving a higher correlation with patient and vascular characteristics.
Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA.
Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT.
Article Synopsis
- This study evaluated the effectiveness of a patient-specific contrast enhancement optimizer simulation software (p-COP) in reducing contrast material (CM) dosage during TAVI-CTA in patients with aortic stenosis.
- Two groups were compared: one used p-COP with an individualized CM protocol, while the other followed a conventional body weight-tailored protocol, analyzing CM amounts, injection rates, and CT values.
- Results indicated a significant reduction in CM dose and injection rate in the p-COP group, although both groups achieved similar CT values and visualization scores, suggesting the potential of p-COP to optimize contrast use without compromising imagery quality.
Introduction: This study aimed to compare the vascular enhancement and radiation dose in preoperative transcatheter aortic valve implantation (TAVI) computed tomography (CT) with a reduced contrast medium (CM) using volume scans in 256-multidetector row CT (MDCT) with a standard CM using 64-MDCT.
Methods: This study included 78 patients with preoperative TAVI CT with either 64- or 256-MDCT. The CM was injected at 1.
Article Synopsis
- The study analyzed the effects of bolus tracking (BT) on radiation dose, image quality, and vascular enhancement in cardiac CT angiography for infants with congenital heart disease.
- Key measurements included volume CT dose index (CTDIvol), dose length product (DLP), and effective dose, with results showing lower radiation exposure in CCTA without BT methods.
- No significant differences were found in vascular enhancement, image noise, or contrast-to-noise ratio, indicating that CCTA without BT can still provide high-quality images while minimizing radiation exposure.
Purpose: In this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports.
Methods: This retrospective study involved 28 patients who underwent computed tomography (CT) scans and had a diagnosed disease with typical imaging findings. Radiology reports were generated using GPT-2, GPT-3.
Article Synopsis
- The study focused on predicting contrast effects in cardiac CT scans using a deep learning (DL) model and compared its effectiveness against traditional methods based on patients' size.
- Researchers analyzed 473 cardiac CT scans and developed DL models to predict the necessary iodine dose for contrast enhancement.
- Results showed that the DL model had better correlation with actual iodine doses than conventional methods related to body weight, lean body weight, and body surface area, indicating its potential usefulness in clinical settings.
Objectives: To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA).
Methods: Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms.
Objectives: This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT).
Methods: We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)-based protocols (n = 100) by the prospective trial.
Article Synopsis
- A study compared a new deep learning algorithm, SR-DLR, to traditional reconstruction methods for assessing coronary stents in CT scans, focusing on image quality.
- The research included 24 patients with stents and used quantitative measures to analyze image sharpness and artifact presence.
- Results showed that SR-DLR significantly improved key metrics such as stent visibility and reduced image noise, leading to better diagnostic confidence among radiologists.
We investigated the effect of electrocardiographic (ECG) mA-modulation of ECG-gated scans of computed tomography (CTA) on radiation dose and image noise at high heart rates (HR) above 100 bpm between helical pitches (HP) 0.16 and 0.24.
View Article and Find Full Text PDFPurpose: To compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning-based reconstruction (DLR) at a similar noise level in clinical study and compare these outcomes with those in phantom study.
Methods: A Catphan phantom with an external body ring was used in the phantom study. In the clinical study, computed tomography (CT) examination data of 34 patients were reviewed.
Background: A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation.
Purpose: To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms.
Material And Methods: Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms.
Introduction: Dual-energy computed tomography (DECT) can generate virtual non-contrast (VNC) images. Herein, we sought to improve the accuracy of VNC images by identifying the optimal slope of contrast media (SCM) for VNC-image generation based on the iodine concentration and subject's body size.
Methods: We used DECT to scan a multi-energy phantom including four iodine concentration rods (15, 10, 5, and 2 mg/mL), and 240 VNC images (eight SCM ranging from 0.