The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease

Kim, Chu hyun; Chung, Myung Jin; Cha, Yoon Ki; Oh, Seok; Kim, Kwang gi; Yoo, Hongseok

doi:10.1371/journal.pone.0291745

Cited by 3 publications

(4 citation statements)

References 35 publications

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“… 31 , 37 – 39 Another 4 studies done with LD chest CT with DLIR [CTDI vol (1.07-2.38 mGy.cm), DLP (79.69-08.7 mGy.cm), SSDE (0.69-1.07 mGy), ED (0.98-1.03 mSv)]. 30 , 32 , 34 , 35 One study done with Quarter low dose (QLD) CT Chest using DLIR showed 75% reduction in radiation dose compared to IR. 33 …”

Section: Resultsmentioning

confidence: 99%

“…29 CT chest examination: A total of nine studies from chest CT that used DLIR showed a reduction in IN (9-50%) compared with IR and FBP. [30][31][32][33][34][35][36][37][38][39] Four studies compared LD with DLIR and SD with IR for chest CT and observed a reduction (62-97%) in RD. 30,32,34,35 One study done with Quarter low dose (QLD) CT Chest using DLIR showed 75% reduction in radiation dose compared to IR.…”

Section: Image Noise (In) and Radiation Dose (Rd) Reduction In Ct Hea...mentioning

confidence: 99%

“…[30][31][32][33][34][35][36][37][38][39] Four studies compared LD with DLIR and SD with IR for chest CT and observed a reduction (62-97%) in RD. 30,32,34,35 One study done with Quarter low dose (QLD) CT Chest using DLIR showed 75% reduction in radiation dose compared to IR. 33…”

Section: Image Noise (In) and Radiation Dose (Rd) Reduction In Ct Hea...mentioning

confidence: 99%

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Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Chandran M,

Pendem,

P S

et al. 2024

F1000Res

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Background The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Image Noise (In) and Radiation Dose (Rd) Reduction In Ct Hea...mentioning

confidence: 99%

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Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Chandran M,

Pendem,

P S

et al. 2024

F1000Res

View full text Add to dashboard Cite

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“…On the other hand, some studies accessed the quality of denoised medical images by relying on IQA metrics from the natural image processing domain [ 18 , 19 , 20 ]. These metrics fall into two categories: full-reference IQA (FR-IQA) and non-reference IQA (NR-IQA).…”

Section: Introductionmentioning

confidence: 99%

AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography

Ahn,

Kim

2023

Diagnostics

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Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial clinical consequences. We present a preliminary version of AntiHalluciNet, which is designed to predict spurious structural components embedded in the residual noise from DL denoising models in low-dose CT and assess its feasibility for auditing the behavior of DL denoising models. We created a paired set of structure-embedded and pure noise images and trained AntiHalluciNet to predict spurious structures in the structure-embedded noise images. The performance of AntiHalluciNet was evaluated by using a newly devised residual structure index (RSI), which represents the prediction confidence based on the presence of structural components in the residual noise image. We also evaluated whether AntiHalluciNet could assess the image fidelity of a denoised image by using only a noise component instead of measuring the SSIM, which requires both reference and test images. Then, we explored the potential of AntiHalluciNet for auditing the behavior of DL denoising models. AntiHalluciNet was applied to three DL denoising models (two pre-trained models, RED-CNN and CTformer, and a commercial software, ClariCT.AI [version 1.2.3]), and whether AntiHalluciNet could discriminate between the noise purity performances of DL denoising models was assessed. AntiHalluciNet demonstrated an excellent performance in predicting the presence of structural components. The RSI values for the structural-embedded and pure noise images measured using the 50% low-dose dataset were 0.57 ± 31 and 0.02 ± 0.02, respectively, showing a substantial difference with a p-value < 0.0001. The AntiHalluciNet-derived RSI could differentiate between the quality of the degraded denoised images, with measurement values of 0.27, 0.41, 0.48, and 0.52 for the 25%, 50%, 75%, and 100% mixing rates of the degradation component, which showed a higher differentiation potential compared with the SSIM values of 0.9603, 0.9579, 0.9490, and 0.9333. The RSI measurements from the residual images of the three DL denoising models showed a distinct distribution, being 0.28 ± 0.06, 0.21 ± 0.06, and 0.15 ± 0.03 for RED-CNN, CTformer, and ClariCT.AI, respectively. AntiHalluciNet has the potential to predict the structural components embedded in the residual noise from DL denoising models in low-dose CT. With AntiHalluciNet, it is feasible to audit the performance and behavior of DL denoising models in clinical environments where only residual noise images are available.

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Progress in the radiologic diagnosis of idiopathic pulmonary fibrosis

Ledda,

Marrocchio,

Sverzellati

2024

Current Opinion in Pulmonary Medicine

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Purpose of review To discuss the most recent applications of radiological imaging, from conventional to quantitative, in the setting of idiopathic pulmonary fibrosis (IPF) diagnosis. Recent findings In this article, current concepts on radiological diagnosis of IPF, from high-resolution computed tomography (CT) to other imaging modalities, are reviewed. In a separate section, advances in quantitative CT and development of novel imaging biomarkers, as well as current limitations and future research trends, are described. Summary Radiological imaging in IPF, particularly quantitative CT, is an evolving field which holds promise in the future to allow for an increasingly accurate disease assessment and prognostication of IPF patients. However, further standardization and validation studies of alternative imaging applications and quantitative biomarkers are needed.

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The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease

Cited by 3 publications

References 35 publications

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography

Progress in the radiologic diagnosis of idiopathic pulmonary fibrosis

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