Highlights

· Diffusion and DCE-MRI have been used to assess chemotherapy response. However, their potentials as quantitative imaging biomarkers have not been fully utilized.
· Diffusion time dependency of diffusion MRI parameters can be used to measure microstructural properties of cancer, such as cell size and cell membrane permeability.
· DCE-MRI can be used to measure vascular properties as well as cellular properties of cancer during chemotherapy.
· Diffusion and DCE-MRI can provide quantitative measurement of antiangiogenic and cytotoxic effect of chemotherapy.

Problem Summary

Rapid development of fast MRI technologies over last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously. Diffusion MRI (dMRI) and dynamic contrast enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potential applications of quantitative cancer imaging, we discuss basic concepts and recent developments, as well as future directions for further development.

MRI has been widely used for cancer diagnosis and assessment of treatment response (1-9), particularly with a Gadolinium-based contrast agent (GBCA). Contrast enhancement is a common feature of chaotic angiogenic vessels, a hallmark of cancer, which has been a crucial part of MRI-based detection of malignant lesions. It has also been used in assessment of tumor response to therapy for the development of new therapeutic strategies and for patient management. Volumetric assessment of tumors, including the Response Evaluation Criteria in Solid Tumors (RECIST), has wide acceptance, but also severe limitations; volume change may not be appreciable even when therapy successfully halted tumor growth, or measurable volume change may not guarantee favorable response throughout the entire tumor. MRI has been used to map the internal treatment response of a tumor. However, the ultimate potential of MRI, as a unique imaging modality that can probe the microstructural and functional properties of soft tissue noninvasively, has not been fully utilized for cancer imaging. Among many MRI methods, dMRI and DCE-MRI using a GBCA have been two most commonly used advanced quantitative MRI methods. dMRI measures the diffusivity of endogenous water molecules in a tissue which reflects the mean size of the tissue microstructure that restricts and/or hinders Brownian random motion of water molecules. Previous studies have shown that this is in fact a powerful tool to detect densely populated cancer cells and their changes induced by a therapy. On the other hand, DCE-MRI has been the choice of modality to assess the vascular perfusion properties of cancer. DCE-MRI typically uses a fast MRI method to continuously capture the signal intensity change during and after contrast injection into the circulation system, which contains rich information about the tumor vasculature properties and how well blood is delivered to the tumor. In fact, there has been a remarkable increase in the use of these two MRI methods, separately or combined, for cancer diagnosis and monitoring treatment response. However, the recent growth in applications of these advanced MRI methods has also been challenged by the practical limitation in implementing them as quantitative and specific imaging biomarkers. Diffusivity measured by dMRI is a sensitive measure for tissue characteristics, but also a function of multiple measurement conditions, such as diffusion gradient strength and diffusion time. The biophysical meaning of diffusivity can be quite different depending on selection of those parameters during MRI exams. The diffusional movement of water molecules in a tissue has a rich information about the structural properties of the tissue, which can be probed by dMRI. A diffusion weighted image is typically acquired by using a pair of gradients in opposite polarities applied with a delay to allow water molecule diffusion. The MRI signal will be attenuated by diffusion and the amount of attenuation is related to the diffusion coefficient of the tissue (D) and the degree of diffusion weighting (b‑value) which is determined by the diffusion weighting strength (q) and diffusion time (t). A careful selection of these parameters can reveal different properties of the tissue, including vascularity, microstructure, and cellularity. In the case of simple Gaussian diffusion (e.g. in free water), dMRI measurement is characterized by a single parameter, the b-value, such that the dMRI signal decays as S=S0 exp(-bD). In contrast, tissue complexity gives rise to non-Gaussian diffusion, which makes the signal S depend on q and t separately, and is characterized by (i) the presence of the higher-order terms in the cumulant expansion of the signal, such as the kurtosis term K, and (ii) the time-dependence of all the cumulants including D(t) and K(t). Hence, tissue complexity can be probed in two complementary directions: (i) to quantify higher-order cumulants at a given diffusion time, by increasing q (or the b-value at fixed t), and (ii) to probe the time dependence of the cumulants by varying the diffusion time t as cumulants are the signal derivatives at b close to 0. Hence, depending on the microstructural property of interest, a proper combination of q and t needs to be selected in order to make the dMRI experiment most sensitive and specific to the target property of the tissue structure. Similar observation was also made with the contrast kinetic parameters measured by DCE-MRI as the final estimates can be affected by acquisition methods as well as a particular choice of contrast kinetic model. Perfusion is physiologically defined as the steady-state delivery of blood at the capillary level to tissue and it is related to the supply of oxygen and other nutrients to the tissue. Perfusion MRI techniques include T2/T2*-weighted dynamic susceptibility contrast (DSC)-MRI and T1-weighted DCE-MRI, which are acquired with exogenous GBCAs, and the arterial spin-labeling which is acquired without an exogenous contrast agent. Among them, DCE-MRI has been most commonly used to assess the vascular perfusion and permeability of cancer, particularly in clinical imaging studies for lesions outside the brain. DCE-MRI essentially measures the change of tissue longitudinal relaxation rate (R1=1/T1) in a series of images acquired before, during and after the injection of GBCA. The dynamic time course of contrast enhancement in a voxel or region of interest data can be analyzed with semi-quantitative (model-free) or quantitative (model-based) approaches. Furthermore, dMRI and DCE-MRI are sensitive to both cellular structure and perfusion properties, which should be included in the consideration of the experimental design when using these methods even for one aspect of them. Alternatively, one of the methods can be used to measure both cellular structural and vascular perfusion properties, while reducing the scan time, if such potential can be fully exploited. We will review the theoretical concepts as well as the recent advance in both dMRI and DCE-MRI methods toward making them as quantitative tools for assessment of chemotherapy response.

Summary/recap/follow-up:

Diffusion and DCE-MRI have become important techniques with applications in various areas of cancer imaging including diagnosis, tumor grading, and treatment response evaluation and prediction. The rapid development of new diffusion and perfusion techniques as a result of advances in MR hardware and emerging new microstructure models have shown a promising trend to expand the scope of dMRI and DCE-MRI to become a powerful tool for imaging tumor heterogeneity, vascularity, cellularity, and microstructural properties.

Acknowledgements

No acknowledgement found.

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