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. 2024 Dec;25(12):e14560.
doi: 10.1002/acm2.14560. Epub 2024 Nov 14.

Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy

Hua Jiang  1 , Songbing Qin  1 , Lecheng Jia  2   3 , Ziquan Wei  2 , Weiqi Xiong  4 , Wentao Xu  1 , Wei Gong  1 , Wei Zhang  4 , Liqin Yu  1
Affiliations

Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy

Hua Jiang et al. J Appl Clin Med Phys. 2024 Dec.

Abstract

Objective: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.

Methods: A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR).

Results: The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p < 0.05), and the subjective scores of DLR were close to NDCT.

Conclusion: The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.

Keywords: deep learning; fan‐beam CT; image quality; image‐guided radiotherapy; ultra‐low dose CT.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Catphan504 phantom.
FIGURE 2
FIGURE 2
United Imaging uRT‐linac 506c.
FIGURE 3
FIGURE 3
Comparison of NDCT (right) and LDCT (left).
FIGURE 4
FIGURE 4
Network structure of CNCycleGAN.
FIGURE 5
FIGURE 5
Comparison of NPS for NDCT, DLR, and LDCT.
FIGURE 6
FIGURE 6
Comparison of LDCT, NDCT, and DLR.
See this image and copyright information in PMC

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