An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.
In this article, we propose an AI-based low-risk visualization framework for lung health monitoring using low-resolution ultra-low-dose CT (LR-ULDCT). We present a novel deep cascade processing workflow to achieve diagnostic visualization on LR-ULDCT (<0.3 mSv) at par high-resolution CT (HRCT) of 10...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2047 - 2063 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515385&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515385 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515385 181515385 181515385 10.1007/s10278-024-01062-5 181515385 ppf: 2047 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT. aug: au: Rai, Swati Bhatt, Jignesh S. Patra, Sarat Kumar affil: https://ror.org/0163rt176 Indian Institute of Information Technology Vadodara, Vadodara, India sug: subj: Lung Radiography Lung Physiopathology Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Artificial Intelligence Utilization Human Qualitative Studies Quantitative Studies Case Studies Deep Learning Radiographic Image Interpretation, Computer-Assisted Pneumonia Pulmonary Edema COVID-19 Data Collection Data Analysis Software Descriptive Statistics Funding Source ab: In this article, we propose an AI-based low-risk visualization framework for lung health monitoring using low-resolution ultra-low-dose CT (LR-ULDCT). We present a novel deep cascade processing workflow to achieve diagnostic visualization on LR-ULDCT (<0.3 mSv) at par high-resolution CT (HRCT) of 100 mSV radiation technology. To this end, we build a low-risk and affordable deep cascade network comprising three sequential deep processes: restoration, super-resolution (SR), and segmentation. Given degraded LR-ULDCT, the first novel network unsupervisedly learns restoration function from augmenting patch-based dictionaries and residuals. The restored version is then super-resolved (SR) for target (sensor) resolution. Here, we combine perceptual and adversarial losses in novel GAN to establish the closeness between probability distributions of generated SR-ULDCT and restored LR-ULDCT. Thus SR-ULDCT is presented to the segmentation network that first separates the chest portion from SR-ULDCT followed by lobe-wise colorization. Finally, we extract five lobes to account for the presence of ground glass opacity (GGO) in the lung. Hence, our AI-based system provides low-risk visualization of input degraded LR-ULDCT to various stages, i.e., restored LR-ULDCT, restored SR-ULDCT, and segmented SR-ULDCT, and achieves diagnostic power of HRCT. We perform case studies by experimenting on real datasets of COVID-19, pneumonia, and pulmonary edema/congestion while comparing our results with state-of-the-art. Ablation experiments are conducted for better visualizing different operating pipelines. Finally, we present a verification report by fourteen (14) experienced radiologists and pulmonologists. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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