Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Research Article Volume 6 Issue 12

Differences in Image Quality Between Different Deep Learning Algorithms in Chest CT Scans

Faizan Fuzail1, Muhammad Mehran Mouzam2*, Aamna Bibi2, Saud Ur Rehman3 and Tayba Batool4

1Allama Iqbal Medical College Lahore, Department of Medicine and Allied, Pakistan
2University of Agriculture Faisalabad, Department of Medicine and Surgery, Pakistan
3Dera Gazi Khan Medical College, Department of Medicine and Surgery, Pakistan
4Government College Women University Fsd, department of Computer Science, Pakistan

*Corresponding Author: Muhammad Mehran Mouzam, University of Agriculture Faisalabad, Department of Medicine and Surgery, Pakistan.

Received: August 09, 2022; Published: November 28, 2022

Abstract

Background: Lately, however, deep learning reconstruction (DLR) technologies have emerged as a viable technical option for reducing radiation dose because they can effectively reconstruct images developed at a low radiation dose to create clear and interpretable images for diagnostic and clinical use.

Objectives: This study explores the possibility of using DLR algorithms in healthcare imaging by comparing image quality between vendor-specific DLR algorithms like TrueFidelityTM and vendor-non-specific DLRs to improve the diagnostic quality of ultra-low-dose CT scans in lung imaging.

Methods: The study involved studying past CT scans of 50 patients who had undergone CT scans between January and February of 2021. Two reconstructed images for each patient were submitted to experienced radiographers from whom reconstruction details had been hidden for qualitative assessment. The assessors rated the images on a scale of 1 to 4 for the noise, resolution, and distortion properties and gave their preferred choice between the two images for each case. Quantitatively, one experienced radiographer assessed the signal-to-noise ratio (SNR) and Edge-Rise-Distance (ERD) for each image.

Results: Non-tuberculous lung diseases, precisely, were the main, accounting for 62% (n = 31). The other conditions included atelectasis (12%, n = 6), pneumonia including COVID-19 (18%, N = 9), and active tuberculosis (8%, n = 4). For subjective noise, TrueFidelityTM scored higher than ClariCT.AI. On the qualitative noise assessment scale, the former scored 3.72, whereas the latter scored an average of 3.22. On resolution, whereas TrueFidelityTM had a score of 3.66, ClariCT.AI recorded an average score of 3.49. In terms of Image distortion, TrueFidelityTM had a score of 3.46, while ClariCT.AI recorded an average score of 3.51. the average preference rate for TrueFidelityTM was 72%, while for ClariCT.AI was 28%. Quantitatively, whereas the SNR for TrueFidelityTM was 22.65 ± 2.84, that for ClariCT.AI was 25.95 ± 5.82. While the ERD for TrueFidelityTM was 0.97 ± 0.19, that for ClariCT.AI was 1.48 ± 0.19.

This study confirmed that vendor-specific DLR algorithms were generally more effective at delivering quality images, confirming the need to develop more specific DLR for the different CT scanners available in the market.

 Keywords: Iterative Reconstruction (IR); Signal-To-Noise Ratio (SNR); Edge-Rise-Distance (ERD)

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Citation

Citation: Muhammad Mehran Mouzam., et al. “Differences in Image Quality Between Different Deep Learning Algorithms in Chest CT Scans”.Acta Scientific Medical Sciences 6.12 (2022): 114-120.

Copyright

Copyright: © 2022 Muhammad Mehran Mouzam., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate30%
Acceptance to publication20-30 days
Impact Factor1.403

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