Hyeong Min Jin

Lung densitometry is being frequently adopted in CT-based emphysema quantification, yet known to be affected by the choice of reconstruction kernel. This study presents a two-step deep learning architecture that enables accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT. Deep learning is used to convert a CT image of a sharp kernel to that of a standard kernel with restoration of truncation artifacts and smoothing-free pixel size normalization. We

BACKGROUND: This study was conducted to evaluate the efficiency and accuracy of the daily patient setup for breast cancer patients by applying surface-guided radiation therapy (SGRT) using the Halcyon system instead of conventional laser alignment based on the skin marking method. METHODS AND MATERIALS: We retrospectively investigated 228 treatment fractions using two different initial patient setup methods. The accuracy of the residual rotational error of the SGRT system was evaluated by using

This study presents a novel deep learning approach for denoising of ultra-low-dose cardiac CT angiography (CCTA) by combining a low-dose simulation technique and convolutional neural network (CNN). Twenty-five CT angiography (CTA) scans acquired with ECG gating (70 – 100 kVp, 100 – 200 mAs) were fed into the low-dose simulation tool to generate a paired set of simulated low-dose CTA and synthetic low-dose noise. A modified U-net model with 4x4 kernel size and five layers was trained with these p

Purpose Online magnetic resonance-guided adaptive radiotherapy (MRgART), an emerging technique, is used to address the change in anatomical structures, such as treatment target region, during the treatment period. However, the electron density map used for dose calculation differs from that for daily treatment, owing to the variation in organ location and, notably, air pockets. In this study, we evaluate the dosimetric effect of electron density override on air pockets during online ART for panc

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exa

CT scan parameters are known to strongly affect imaging biomarker quantification and increase variability of measurements. We present a deep learning-enabled recon kernel normalization technique and its effect in emphysema quantification in low-dose lung CT.

This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to co

Abstract Purpose The objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep‐learning‐based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)‐only radiotherapy. Methods Polyurethane‐based and silicone‐based materials with various silicone oil concentrations were scanned using 0.35 T MR and CT scanner to determine the tissue surrogate. Five tissue surrogates were determined by comparing the organ intensity with patient CT

PURPOSE: Iterative reconstruction (IR) technique is growingly used in clinical CT imaging due to its ability to provide improved image quality at lower patient doses. However, the nonlinear frequency response of IR technique, which may affect quantitative analysis, is rarely explored. This study evaluates noise level and contrast dependent behavior of MTF in IR CT imaging with a multi-contrast slit phantom scanned at different dose levels. METHODS: A multi-contrast slit phantom was created consi

Purpose: The emphysema index (EI) in CT is a quantitative measure of emphysema, which is known to be affected by reconstruction kernel. This study presents an image‐based kernel conversion technique which converts CT image of sharp kernel to that of standard kernel and evaluates its impact on EI normalization for images obtained with different kernels. Methods: Sixty cases of CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low

BACKGROUND: Dose variation due to changes in bowel air poses significant challenges for carbon radiotherapy in pancreatic cancer. This retrospective study evaluated a density-override optimization technique to mitigate dosimetric uncertainties caused by bowel air changes. MATERIALS AND METHODS: Planning CT and cone-beam CT data from 8 patients with locally advanced pancreatic cancer undergoing stereotactic ablative radiotherapy were analyzed. Treatment simulations used a dose of 55.2 GyE in 12 f

Purpose: Radiogenomics promises to discover quantitative imaging features which are associated with genomic profiles and of prognostic in cancer patients. However, the CT imaging features are known to be sensitive to noise characteristic and affected by CT parameters. We investigate the variability of CT imaging features which are previously reported as radiogenomic markers in non‐small cell lung cancer (NSCLC). Methods: Three NSCLC cases of CT exams with 2 reconstruction kernels (B30f, B60f) we