Comparison of intra- and inter-patient intensity standardization methods for multi-parametric whole-body MRI

Abstract

Objective: To test and compare different intensity standardization approaches for whole-body multi-parametric MRI, allowing for the compensation of univocal representation of image intensities between scans, which pose problems in image quantification, assessment of changes between a baseline and follow-up scan and hinder performance of image processing and machine learning algorithms. 

Approach: In this work, we present a comparison on the accuracy of intensity standardization approaches with increasing complexity, for intra- and inter-patient multi-parametric whole-body MRI. Several approaches were used: z-scoring of the intensities, piecewise linear mapping and deformable mapping of intensity distributions into established reference intensity space. Additionally, the use reference image and average population distribution reference; as well as, whole image and region of interest standardization approaches were investigated. All methods were validated on a data set of 18 whole-body anatomical and diffusion-weighted MR scans consisting of baseline and follow-up examinations acquired from advanced prostate cancer patients and healthy volunteers. 

Main results: The piecewise linear intensity standardisation approach provided the best compromise between standardization accuracy and method stability, with average deviations in intensity profile of 0.011-0.027 and mean absolute difference of 0.29-0.37 standard score (intra-patient) and 0.014-0.056 (inter-patient), depending on the type of used MR modality. 

Significance: Linear piecewise approaches showed the overall best performance across multiple validation metrics, mostly because of its robustness. The inter-patient standardization proved to perform better when using population average reference image; in contrary to intra-patient approach, where the best results were achieved by standardizing towards a reference image taken as the baseline scan.

Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times

“…Another limitation of the present work is reliance on data from a single institution and scanner manufacturer. To establish calibration across scanners from different manufacturers and accommodate changes in imaging parameters such as field strength and b-values, more sophisticated techniques like histogram matching [24, 25] or the incorporation of machine learning methodologies [26] would be necessary. The present study is instructive, though, as it reveals that even minor variations of 15ms in TE can result in significant differences in quantitative measurements that require careful calibration and demonstrates physics-based correction for these differences.…”