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Updated: May 15, 2025

Hybrid µCT-FMT imaging and image analysis
Published on: June 4, 2015
Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses
Qingyao Li1,2, Ling Liu3, Yaping Zhang2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Jun Gong Rd. 516, Shanghai, 200093, China.
Purpose:
To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols.
Materials And Methods:
The Catphan 600 phantom was scanned using a multidetector CT scanner under two protocols: 120 kV/100 mA (CT dose index volume = 3.4 mGy) to simulate low-dose CT, and 70 kV/40 mA (0.27 mGy) to simulate ultralow-dose CT. Raw data were reconstructed into standard 512-matrix images using three methods: filtered back projection (FBP), adaptive statistical iterative reconstruction at 40% intensity (ASIR-V), and deep learning image reconstruction at high intensity (DLIR-H). The Swin2SR super-resolution model was used to generate 2048-matrix images (Swin2SR-2048), while the super-resolution convolutional neural network (SRCNN) model generated 2048-matrix images (SRCNN-2048). The quality of 2048-matrix images generated by the two models (Swin2SR and SRCNN) was compared. Image quality was evaluated by ImQuest software (v7.2.0.0, Duke University) based on line pair clarity, task-based transfer function (TTF), image noise, and noise power spectrum (NPS).
Results:
At equivalent radiation doses and reconstruction method, Swin2SR-2048 images identified more line pairs than both standard-512 and standard-1024 images. Except for the 0.27 mGy/DLIR-H/standard kernel sequence, TTF-50% of Teflon increased after super-resolution processing. Statistically significant differences in TTF-50% were observed between the standard 512, 1024, and Swin2SR-2048 images (all p < 0.05). Swin2SR-2048 images exhibited lower image noise and NPSpeak compared to both standard 512- and 1024-matrix images, with significant differences observed in all three matrix types (all p < 0.05). Swin2SR-2048 images also demonstrated superior quality compared to SRCNN-2048, with significant differences in image noise (p < 0.001), NPSpeak (p < 0.05), and TTF-50% for Teflon (p < 0.05).
Conclusion:
Transformer-enhanced 2048-matrix CT images improve spatial resolution and reduce image noise compared to standard-512 and -1024 matrix images.
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