Multiparametric MRI

Abstract

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

MRI‐Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning

“…4,5 Computer-aided image analysis for improved diagnosis based on breast mpMRI has been developed recently with the advent of deep learning (DL). 6,7 Recent studies have shown that DL has the potential to outperform subjective assessment of breast MRI by radiologists, can facilitate breast segmentation, lesion segmentation/classification, predicting the likelihood of recurrence, and could support assessing the effect of neoadjuvant chemotherapy. [8][9][10][11][12][13] Most methods used DCE-MRI as the primary sequence because of its high sensitivity for diagnosing malignant breast lesions.…”

Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI

“…Radiomics, the use of high-throughput data mining of medical images for statistical and machine learning methods, has led to considerable growth in quantitative image analysis 1–3 . By providing objective, numerical metrics of image intensity, morphometry, and texture, 1 radiomics offers significant potential value in numerous clinical contexts such as musculoskeletal disease, 4 as well as cancer diagnosis and treatment 5,6 .…”

“…However, recent phantom 7,8 and in vivo 9–11 studies have demonstrated that differences in image acquisition and processing methods may negatively impact the reproducibility of extracted radiomic features 12 and downstream models 13 . This issue is particularly severe in magnetic resonance imaging (MRI) radiomics, as almost all MRI-based radiomic models use weighted image sequences (eg, T1- or T2-weighted), which provide qualitative, relative intensity values that can vary greatly across scan acquisitions 3,11,14,15 …”

“…13 This issue is particularly severe in magnetic resonance imaging (MRI) radiomics, as almost all MRI-based radiomic models use weighted image sequences (eg, T1or T2-weighted), which provide qualitative, relative intensity values that can vary greatly across scan acquisitions. 3,11,14,15 Quantitative MRI can address poor radiomic feature reproducibility stemming from relative MRI intensities by measuring intrinsic tissue properties such as T1 and T2 relaxation times or diffusion. Unlike the relative image intensities in weighted MRI, quantitative MRI values correspond to an absolute physical measurement: for example, T1weighted MRI voxels are unitless, whereas a T1 relaxation time (or T1) map has units of time.…”

“…14,17 Of these, the quantitative MRI framework known as magnetic resonance fingerprinting (MRF) 18 possesses several advantages that make it particularly suitable for reproducible radiomics. The MRF acquisition allows for rapid and robust multiparametric 3 tissue property mapping in a clinically acceptable timeframe. 5,18 Notably, MRF acquisition includes a B1 + field measurement step 19 that corrects intensity variations from field inhomogeneities.…”