PhD Thesis Defense: Mengyang Zhao

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Meeting ID: 929 8151 0961
Passcode: 908939

"3D MRI-guided Near-infrared Spectroscopic Tomography for Imaging Breast Cancer"

Abstract

While dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely recognized as the most sensitive modality for breast cancer detection, it has notable limitations, including a high false-positive rate and potential safety concerns associated with gadolinium (Gd) contrast agents. Consequently, there is growing interest in alternatives that rely on endogenous contrast. To address this need, the overall goal of this thesis is to develop and evaluate a concurrent MRI-guided near-infrared spectroscopic tomography (MRg-NIRST) platform capable of achieving reliable diagnostic performance without the use of contrast agents. In this multi-modality imaging approach, MRI provides high-resolution anatomical information about breast tissue composition, while NIRST offers quantitative hemodynamic and physiological biomarkers relevant to breast cancer detection.

In this study, a novel MRg-NIRST imaging system with an MRI-compatible, flexible breast optical interface was developed to provide functional tumor information based on physiologically relevant biomarkers such as oxy-hemoglobin, deoxy-hemoglobin, and water. The system acquires data from up to 2,304 source–detector positions across the entire breast at six wavelengths (660–852 nm) simultaneously with MRI scans, in approximately four minutes, enabling 3D MRg-NIRST reconstruction of the entire breast. Furthermore, a new MRg-NIRST reconstruction method incorporating source–detector coupling was implemented to account for geometric variability across breast curvatures, reducing image artifacts and improving overall image quality.

The system was validated through a series of phantom studies, normal-subject imaging, and a clinical study involving breast cancer patients. Reconstructed images of heterogeneous phantoms demonstrated a sharp contrast between inclusions and background, with accurate recovery of inclusion sizes. With the new reconstruction algorithm, the contrast-to-noise ratio (CNR) improved by up to 136.36% and the peak signal-to-noise ratio (PSNR) by up to 12.73%. Total hemoglobin (HbT) concentration values estimated from normal-subject images were consistent with those reported in previous optical imaging studies. Using MRg-NIRST, a breast cancer lesion measuring 5 × 6 × 9 mm was successfully detected in a heterogeneous dense breast. To our knowledge, this is the first demonstration that NIRST imaging can detect breast cancer lesions smaller than 7 mm, highlighting the potential of MRg-NIRST for early breast cancer detection in dense breast tissue.

Thesis Committee

• Shudong Jiang (Chair)
• Keith Paulsen (co-chair)
• Brian Pogue
• Darren Roblyer (BU)

Contact

For more information, contact Thayer Registrar at thayer.registrar@dartmouth.edu.