Year: 2025
Type: article
Abstract: Diffuse correlation tomography (DCT) is an emerging technique for detecting diseases associated with localized abnormal perfusion from near-infrared light intensity temporal autocorrelation functions (g2(τ)). However, a critical drawback of traditional reconstruction methods is the imbalance between optical measurements and the voxels to be reconstructed. To address this issue, this paper proposes Conv-TransNet, a convolutional neural network (CNN)–Transformer hybrid model that directly maps g2(τ) data to blood flow index (BFI) images. For model training and testing, we constructed a dataset of 18,000 pairs of noise-free and noisy g2(τ) data with their corresponding BFI images. In simulation validation, the root mean squared error (RMSE) for the five types of anomalies with noisy data are 2.13%, 4.43%, 2.15%, 4.05%, and 4.39%, respectively. The MJR (misjudgment ratio)of them are close to zero. In the phantom experiments, the CONTRAST of the quasi-solid cross-shaped anomaly reached 0.59, with an MJR of 2.21%. Compared with the traditional Nth-order linearization (NL) algorithm, the average CONTRAST of the speed-varied liquid tubular anomaly increased by 0.55. These metrics also demonstrate the superior performance of our method over traditional CNN-based approaches. The experimental results indicate that the Conv-TransNet model would achieve more accurate and robust reconstruction, suggesting its potential as an alternative for blood flow imaging.
Source: Photonics
Authors: Xiaojuan Zhang, Wen Yan, Peng Zhang, Xiaogang Tong, Haifeng Zhou, Yu Shang
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G2750549332, G3599896623
OpenAlex ID: W4413358347