Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction

Ferri, Fabrice; Bouzerar, R.; Auquier, M.; Vial, J; Renard, C.

doi:10.1016/j.ejrad.2022.110338

Cited by 2 publications

(5 citation statements)

References 24 publications

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“…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 – 39 Four studies compared LD with DLIR and SD with IR for chest CT and observed a reduction (62-97%) in RD. 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)].…”

Section: Resultsmentioning

confidence: 99%

“… 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%

“… 36 Ferri et al reported DLIR reconstruction series provided the smallest volume of emphysema compared with Adaptive statistical iterative reconstruction-V (ASIR-V) and FBP and also observed the increase in strength of DLIR led to a decrease in the size of emphysema. 30 …”

Section: Discussionmentioning

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: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

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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Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm

Bocquet,

Bouzerar,

François

et al. 2024

Journal of Thoracic Imaging

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Purpose: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). Material and Methods: This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. Results: The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6 mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). Conclusions: At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

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Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction

Cited by 2 publications

References 24 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

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm

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