Abstract
Objective. To determine the minimum detector width along the fan-angle direction in offset-detector cone-beam breast CT for multiple advanced reconstruction algorithms and to investigate the effect on radiation dose, scatter, and image quality. Approach. Complete sinograms (m × n = 1024 × 768 pixels) of 30 clinical breast CT datasets previously acquired on a clinical-prototype cone-beam breast CT system were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. Complete sinograms were retrospectively truncated to varying widths to understand the limits of four image reconstruction algorithms—FDK with redundancy weighting (FDK-W), compressed-sensing based FRIST, fully-supervised MS-RDN, and self-supervised AFN. Upon determining the truncation limits, numerical phantoms generated by segmenting the reference reconstructions into skin, adipose, and fibroglandular tissues were used to determine the radiation dose and scatter-to-primary ratio (SPR) using Monte Carlo simulations. Main results. FDK-W, FRIST, and MS-RDN showed artifacts when m < 596, whereas AFN reconstructed images without artifacts for m > = 536. Reducing the detector width reduced signal-difference to noise ratio (SDNR) for FDK-W, whereas FRIST, MS-RDN and AFN maintained or improved SDNR. Reference reconstruction and AFN with m = 536 had similar quantitative measures of image quality. Significance. For the 30 cases, AFN with m = 536 reduced the radiation dose and SPR by 37.85% and 33.46%, respectively, compared to the reference. Qualitative and quantitative image quality indicate the feasibility of AFN for offset-detector cone-beam breast CT. Radiation dose and SPR were simultaneously reduced with a 536 × 768 detector and when used in conjunction with AFN algorithm had similar image quality as the reference reconstruction.
| Original language | English (US) |
|---|---|
| Article number | 025047 |
| Journal | Biomedical Physics and Engineering Express |
| Volume | 11 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 31 2025 |
Keywords
- breast CT
- deep learning
- dose reduction
- image reconstruction
- scatter reduction
- self-supervised
ASJC Scopus subject areas
- General Nursing
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