An MR fingerprinting approach for quantitative inhomogeneous magnetization transfer imaging

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics

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.

Inhomogeneous magnetization transfer imaging: Concepts and directions for further development

Off-resonance radio frequency irradiation can induce the ordering of proton spins in the dipolar fields of their neighbors, in molecules with restricted mobility. This dipolar order decays with a characteristic relaxation time, T_1D , that is very different from the T₁ and T₂ relaxation of the nuclear alignment with the main magnetic field. Inhomogeneous magnetization transfer (ihMT) imaging is a refinement of magnetization transfer (MT) imaging that isolates the MT signal dependence on dipolar order relaxation times within motion-constrained molecules. Because T_1D relaxation is a unique contrast mechanism, ihMT may enable improved characterization of tissue. Initial work has stressed the high correlation between ihMT signal and myelin density. Dipolar order relaxation appears to be much longer in membrane lipids than other molecules. Recent work has shown, however, that ihMT acquisitions may also be adjusted to emphasize different ranges of T_1D . These newer approaches may be sensitive to other microstructural components of tissue. Here, we review the concepts and history of ihMT and outline the requirements for further development to realize its full potential.

Relaxation measurements of an MRI system phantom at low magnetic field strengths

OBJECTIVE

Temperature controlled T₁ and T₂ relaxation times are measured on NiCl₂ and MnCl₂ solutions from the ISMRM/NIST system phantom at low magnetic field strengths of 6.5 mT, 64 mT and 550 mT.

MATERIALS AND METHODS

The T₁ and T₂ were measured of five samples with increasing concentrations of NiCl₂ and five samples with increasing concentrations of MnCl₂. All samples were scanned at 6.5 mT, 64 mT and 550 mT, at sample temperatures ranging from 10 °C to 37 °C.

RESULTS

The NiCl₂ solutions showed little change in T₁ and T₂ with magnetic field strength, and both relaxation times decreased with increasing temperature. The MnCl₂ solutions showed an increase in T₁ and a decrease in T₂ with increasing magnetic field strength, and both T₁ and T₂ increased with increasing temperature.

DISCUSSION

The low field relaxation rates of the NiCl₂ and MnCl₂ arrays in the ISMRM/NIST system phantom are investigated and compared to results from clinical field strengths of 1.5 T and 3.0 T. The measurements can be used as a benchmark for MRI system functionality and stability, especially when MRI systems are taken out of the radiology suite or laboratory and into less traditional environments.

Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions

PURPOSE

To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases.

METHODS

The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T_1D ) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T_2,f , T_2,b , R, and free pool T₁ ). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T₁ .

RESULTS

Bland-Altman analyses demonstrated a systematic underestimation of MPF (-2.5% and -1.3%, on average, in white and gray matter, respectively) and overestimation of T₁ (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = -0.03% and ΔT₁  = -19.0 ms). The single-point methodology yielded consistent MPF and T₁ values with respective maximum relative average bias of -0.15% and -3.5 ms found in white matter.

CONCLUSION

The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.

Primary Multiparametric Quantitative Brain MRI: State-of-the-Art Relaxometric and Proton Density Mapping Techniques

This review on brain multiparametric quantitative MRI (MP-qMRI) focuses on the primary subset of quantitative MRI (qMRI) parameters that represent the mobile ("free") and bound ("motion-restricted") proton pools. Such primary parameters are the proton densities, relaxation times, and magnetization transfer parameters. Diffusion qMRI is also included because of its wide implementation in complete clinical MP-qMRI application. MP-qMRI advances were reviewed over the past 2 decades, with substantial progress observed toward accelerating image acquisition and increasing mapping accuracy. Areas that need further investigation and refinement are identified as follows: (a) the biologic underpinnings of qMRI parameter values and their changes with age and/or disease and (b) the theoretical limitations implicitly built into most qMRI mapping algorithms that do not distinguish between the different spatial scales of voxels versus spin packets, the central physical object of the Bloch theory. With rapidly improving image processing techniques and continuous advances in computer hardware, MP-qMRI has the potential for implementation in a wide range of clinical applications. Currently, three emerging MP-qMRI applications are synthetic MRI, macrostructural qMRI, and microstructural tissue modeling.