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Optimizing Near-Infrared Spectral To...

Zhao, Yan.

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Optimizing Near-Infrared Spectral Tomography for Diagnostic Imaging and Monitoring of Breast Cancer Treatment.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimizing Near-Infrared Spectral Tomography for Diagnostic Imaging and Monitoring of Breast Cancer Treatment./
作者:	Zhao, Yan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	191 p.
附註:	Source: Dissertations Abstracts International, Volume: 79-09, Section: B.
Contained By:	Dissertations Abstracts International79-09B.
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745249
ISBN:	9780355631661

Optimizing Near-Infrared Spectral Tomography for Diagnostic Imaging and Monitoring of Breast Cancer Treatment.
Zhao, Yan.

Optimizing Near-Infrared Spectral Tomography for Diagnostic Imaging and Monitoring of Breast Cancer Treatment. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 191 p.

Source: Dissertations Abstracts International, Volume: 79-09, Section: B.

Thesis (Ph.D.)--Dartmouth College, 2018.

This item is not available from ProQuest Dissertations & Theses.

Near-infrared spectral tomography (NIRST) has been intensively investigated for clinical application in breast imaging, by providing functional information about physiologically related biomarkers such as oxy- and deoxy-hemoglobin, water, lipid and scatter component. In this thesis, a series of studies on system development and reconstruction algorithm were completed to improve the imaging quality of MR-guide NIRST and to predicate breast cancer response to neo-adjuvant chemotherapy. To optimize image recovery which maximizes difference between malignant and benign lesions, non-linear iterative reconstructions of MR-Guided NISRT images were recovered using an L-curve based algorithm, and applied to clinical trial data. The statistical analyses have shown that the new approach dramatically improved the statistical significance for differentiating malignant from benign lesions. While MRI guide NIRST has been utilized to detect breast cancer, NIRST is also used to predicate and monitor breast tumor responses in patients with locally advanced breast cancer undergoing neoadjuvant treatment. Based on an existing hybrid NIRST system developed at Dartmouth, a compact and portable NIRST system has been developed for imaging patients in the infusion unit while patients are awaiting or undergoing infusion. This system can acquire frequency-domain and continuous-wave data simultaneously at 12 wavelengths in the wavelength range of 660nm to 1064nm. Novel soft gel based homogenous and heterogamous tissue-mimicking phantoms with sphere-shape inclusions have been developed, to mimic human breasts. The phantom experiments indicate that the reconstructed optical images highly depend on the position of imaging plane, especially in the case of small inclusions. Tomographic images of breast collagen content have been recovered for the first time, and image reconstruction approaches with and without collagen content included have been validated in simulation studies, which indicate that including collagen content into the reconstruction procedure can significantly reduce the overestimation in total hemoglobin, water and lipid, and underestimates in oxygen saturation. A group of 10 normal subjects were imaged, and significantly higher ( p<0.05) total hemoglobin and water were estimated in the high-density relative to low-density groups. The performance of the NIRST system was validated in an ongoing clinical trial, and the recovered optical biomarkers were correlated with pathologic response to neoadjuvant chemotherapy.

ISBN: 9780355631661Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Breast cancer diagnosis

Optimizing Near-Infrared Spectral Tomography for Diagnostic Imaging and Monitoring of Breast Cancer Treatment.
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