Shaojie Chang

CT data harmonization via learned virtual monoenergetic imaging for cross‐kV scan translation and radiomics reproducibility

Shaojie Chang, Joseph R. Swicklik, Zhongxing Zhou, Shravani Kharat et autres

BACKGROUND: Radiomics extracts quantitative imaging features from computed tomography (CT) data for clinical decision-making. However, variations in acquisition parameters-particularly x-ray tube voltage (kV)-introduce non-biological variability in attenuation values, limiting the reproducibility of radiomic features across scanners, protocols, and institutions. PURPOSE: To develop …

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Deep Learning–based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT

Shaojie Chang, Emily K. Koons, Hao Gong, Jamison E. Thorne et autres

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.

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Insertion of hepatic lesions into clinical photon-counting-detector CT projection data

Shravani Kharat, Jarod Wellinghoff, Ahmed O El Sadaney, Joel G. Fletcher et autres

Abstract Objective. To facilitate task-driven image quality assessment of lesion detectability in clinical photon-counting-detector CT (PCD-CT), it is desired to have patient image data with known pathology and precise annotation. Standard patient case collection and reference standard establishment are time- and resource-intensive. …

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Preserving noise texture through training data curation for deep learning denoising of high‐resolution cardiac EID‐CT

Kevin J. Treb, Shaojie Chang, Emily K. Koons, Jeffrey F. Marsh et autres

BACKGROUND: To utilize high spatial resolution reconstructions for cardiac imaging at energy-integrating detector CT (EID)-CT with comparable noise to similar reconstructions at photon-counting detector (PCD)-CT, methods to control EID-CT image noise are needed. Supervised convolutional neural networks (CNN) have shown promise for …

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Learned high resolution energy‐integrating detector CT angiography: Harnessing the power of ultra‐high‐resolution photon counting detector CT

Emily K. Koons, Shaojie Chang, Hao Gong, Jamison E. Thorne et autres

BACKGROUND: Coronary computed tomography angiography (cCTA) is a widely used noninvasive diagnostic exam to assess patients for coronary artery disease (CAD). However, the spatial resolution of most CT scanners is limited due to the use of energy-integrating detectors (EIDs). PURPOSE: To develop …

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Leveraging prior knowledge in machine intelligence to improve lesion diagnosis for early cancer detection

Zhengrong Jerome Liang, Shaojie Chang, Yongfeng Gao, Weiguo Cao et autres

BACKGROUND: Experts' interpretations of medical images for lesion diagnosis may not always align with the underlying in vivo tissue pathology and, therefore, cannot be considered the definitive truth regarding malignancy or benignity. While current machine learning (ML) models in medical imaging can …

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Task-specific deep learning-based denoising for UHR cardiac PCD-CT adaptive to imaging conditions and patient characteristics: impact on image quality and clinical diagnosis and quantitative assessment

Shaojie Chang, Emily K. Koons, Cynthia H. McCollough, Shuai Leng

Ultra-high-resolution (UHR) photon-counting detector (PCD) CT offers superior spatial resolution compared to conventional CT, benefiting various clinical areas. However, the UHR resolution also significantly increases image noise, which can limit its clinical adoption in areas such as cardiac CT. In clinical practice, …

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Contrast-guided virtual monoenergetic image synthesis via adversarial learning for coronary CT angiography using photon counting detector CT

Shaojie Chang, Emily K. Koons, Hao Gong, Scott S. Hsieh et autres

Coronary CT angiography (cCTA) is a non-invasive diagnostic test for coronary artery disease (CAD) that often faces challenges with dense calcifications and stents due to blooming artifacts, leading to stenosis overestimation. Virtual monoenergetic images (VMIs) from photon counting detector CT (PCD-CT) provide …

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The impact of noise texture on a deep learning denoising model for high-resolution cardiac EID-CT

Kevin J. Treb, Shaojie Chang, Cynthia H. McCollough, Shuai Leng

Leveraging prior knowledge in machine intelligence for improving cancer diagnostic accuracy

Zhengrong Liang, Shaojie Chang, Yongfeng Gao, Weiguo Cao et autres

Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging

Shaojie Chang, John Charles Benson, John Ignatius Lane, Michael R. Bruesewitz et autres

ABSTRACT BACKGROUND AND PURPOSE: Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating use of smoother reconstruction kernels that reduce resolution below the system’s 0.110 mm maximum spatial resolution. To address this, a denoising convolutional neural network (CNN) was …

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Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network

Shaojie Chang, Jeffrey F. Marsh, Emily K. Koons, Hao Gong et autres

Purpose: We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy …

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