Mr. Minghao Xue Profile

Minghao Xue

at Washington Univ in St Louis

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Publications (6)

SPIE Journal Paper | 25 July 2024 Open Access

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model

Minghao Xue, Shuying Li, Quing Zhu

JBO, Vol. 29, Issue 08, 086001, (July 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.8.086001

KEYWORDS: Image restoration, Tumor growth modeling, Medical image reconstruction, Data modeling, Diffuse optical tomography, Image quality, Tissues, Image enhancement, Lithium, Commercial off the shelf technology

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SPIE Journal Paper | 24 July 2024 Open Access

Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer

Yun Zou, Minghao Xue, Md Iqbal Hossain, Quing Zhu

JBO, Vol. 29, Issue 07, 076007, (July 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.7.076007

KEYWORDS: Tumors, Tumor growth modeling, Data modeling, Performance modeling, Breast cancer, Diffuse optical tomography, Ultrasonography, Optical tomography, Acoustic tomography, Transformers

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SignificanceWe evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning.AimWe aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC.ApproachWe developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model’s performance, comparing its results against a single modality of US or DOT.ResultsThe USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model’s ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction.ConclusionThe USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

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SPIE Journal Paper | 26 August 2023 Open Access

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning

Menghao Zhang, Shuying Li, Minghao Xue, Quing Zhu

JBO, Vol. 28, Issue 08, 086002, (August 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.8.086002

KEYWORDS: Histograms, Tumor growth modeling, Image classification, Breast cancer, Feature extraction, Breast, Data modeling, Image restoration, Deep learning, Education and training

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Proceedings Article | 7 March 2023 Presentation

An automated clinical study pipeline for diffuse optical tomography of breast cancer

Minghao Xue, Menghao Zhang, Shuying Li, Yun Zou, Quing Zhu

Proceedings Volume PC12376, PC123760I (2023) https://doi.org/10.1117/12.2651358

KEYWORDS: Diffuse optical tomography, Breast cancer, Image classification, Feature extraction, Data processing, Ultrasonography, Neural networks, Image restoration, Image quality, Functional imaging

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Proceedings Article | 7 March 2023 Presentation

Classification of breast lesions with deep learning combining diffuse optical tomography frequency-domain data and coregistered ultrasound images

Menghao Zhang, Shuying Li, Minghao Xue, Quing Zhu

Proceedings Volume PC12376, PC1237609 (2023) https://doi.org/10.1117/12.2650179

KEYWORDS: Breast, Ultrasonography, Diffuse optical tomography, Image classification, Image restoration, Neural networks, Diagnostics, Data modeling, Tumor growth modeling, Statistical analysis

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SPIE Journal Paper | 25 August 2022 Open Access

Difference imaging from single measurements in diffuse optical tomography: a deep learning approach

Shuying Li, Menghao Zhang, Minghao Xue, Quing Zhu

JBO, Vol. 27, Issue 08, 086003, (August 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.8.086003

KEYWORDS: Chest, Data modeling, Diffuse optical tomography, Optical testing, Diffuse optical imaging, Monte Carlo methods, Breast, Data acquisition, Tissues, Target acquisition

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