Tumor likelihood estimation on MRI prostate data by utilizing k-Space information

TL;DR

This paper introduces a novel MRI prostate cancer classification method leveraging k-Space data, demonstrating improved accuracy and reduced processing time compared to traditional image domain approaches.

Contribution

The study presents a new pipeline utilizing k-Space information and PCA-based coil compression, enabling faster prostate cancer likelihood estimation with maintained accuracy.

Findings

k-Space based approach outperforms image domain methods (AUROC 86.1%)

Effective with high undersampling rates, AUROC 71.4% at factor 16

Reduces reconstruction time by avoiding complex algorithms like GRAPPA

Abstract

We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Space. Using a publicly available MRI raw dataset with 312 subject and a total of 9508 slices, we show the advantage of utilizing the k-Space for better prostate cancer likelihood estimation in comparison to just using the magnitudinal information in the image domain, with an AUROC of $86.1\%\pm1.8\%$. Additionally, by using high undersampling rates and a simple principal component analysis (PCA) for coil compression, we reduce the time needed for reconstruction by avoiding the time intensive GRAPPA reconstruction algorithm. By using digital undersampling for our experiments, we show that scanning and reconstruction time could be reduced. Even with an undersampling factor of 16, our approach achieves meaningful results, with an AUROC of $71.4\%\pm2.9\%$, using the PCA coil combination and taking into account the k-Space information. With this study, we were able to show the feasibility of preserving phase and k-Space information, with consistent results. Besides preserving valuable information for further diagnostics, this approach can work without the time intensive ADC and reconstruction calculations, greatly reducing the post processing, as well as potential scanning time, increasing patient comfort and allowing a close to real-time prediction.