Abstract #2436

On the optimal analytic approach to luminal water fraction estimation in prostate cancer MRI: Simulations and in vivo validation

Jingxiang Zhang¹, Qingle Kong¹, Chen Yang^1,2, Jiayu Xiao¹, Fei Han³, Andrew Lim⁴, Monish Aron⁵, Steven Cen¹, and Zhaoyang Fan^1,2,4

¹Department of Radiology, University of Southern California, Los Angeles, CA, United States, ²Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ³Siemens Medical Solutions USA, Los Angeles, CA, United States, ⁴Department of Radiation Oncology, University of Southern California, Los Angeles, CA, United States, ⁵Department of Urology, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Prostate, Relaxometry, Prostate, Luminal Water Imaging, T2 Relaxation, Multi-Component Model, Noise Robustness

Motivation: Accurate luminal water fraction (LWF) estimation in prostate MRI is essential for enhancing diagnostic accuracy, yet traditional NNLS-based model struggle with noise in clinical settings.

Goal(s): To evaluate and compare the accuracy, precision, and contrast of various LWF estimation models, focusing on multi-component (MC) models and NNLS-based models.

Approach: Simulation studies were conducted across a range of signal-to-noise ratios (SNRs) and prior knowledge conditions, and further validated using in vivo MRI data from prostate cancer patients.

Results: MC-G-4 and MC-D-2 models showed superior accuracy and precision, providing reliable LWF estimates and effectively differentiating tissue types.

Impact: The MC-G-4 and MC-D-2 models significantly enhance luminal water fraction estimation in prostate cancer MRI, offering robust, accurate, and noise-resilient tools that improve lesion detection and characterization, thereby potentially leading to better patient outcomes in clinical practice.

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