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Development of Radiofrequency Saturation Amplitude-independent Quantitative Markers for Magnetization Transfer MRI of Prostate Cancer

Author(s):

Xunan Huang, Ryan N. Schurr, Shuzhen Wang, Qiguang Miao, Tanping Li* and Guang Jia*   Pages 1 - 8 ( 8 )

Abstract:


Background: In the United States, prostate cancer has a relatively large impact on men's health. Magnetic resonance imaging (MRI) is useful for the diagnosis and treatment of prostate cancer.

Introduction: The purpose of this study was to develop a quantitative marker for use in prostate cancer magnetization transfer (MT) magnetic resonance imaging (MRI) studies that is independent of radiofrequency (RF) saturation amplitude.

Method: Eighteen patients with biopsy-proven prostate cancer were enrolled in this study. MT-MRI images were acquired using four RF saturation amplitudes at 33 frequency offsets. ROIs were delineated for peripheral zone (PZ), central gland (CG), and tumor. Z-spectral data were collected in each region and fit to a three-parameter equation . The three parameters are the magnitude of the bulk water pool (Aw), the full width at half maximum of the water pool (Gw), and the magnitude of the bound pool (Ab), and the slopes from the linear regressions of Gw and Ab on RF saturation amplitude (called kAb and kGw) were used as quantitative markers.

Result: A pairwise statistically significant difference was found between the PZ and tumor regions for the two saturation amplitude-independent quantitative markers. No pairwise statistically significant differences were found between the CG and tumor regions for any quantitative markers.

Conclusion: The significant differences between the values of the two RF saturation amplitude-independent quantitative markers in the PZ and tumor regions reveal that these markers may be capable of distinguishing healthy PZ tissue from prostate cancer.

Keywords:

Magnetization transfer MRI, quantitative imaging, radiofrequency (RF) saturation, Z-spectrum, magnetization transfer ratio, prostate cancer

Affiliation:

Xi`an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi`an, Shaanxi, 710071, Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, Xi`an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi`an, Shaanxi, 710071, Xi`an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi`an, Shaanxi, 710071, School of Physics and Optoelectronic Engineering, Xidian University, Xi`an, Shaanxi, 710071, Xi`an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi`an, Shaanxi, 710071



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