Quantitative Magnetization Transfer in Monitoring Glioblastoma (GBM) Response to Therapy

Mehrabian, Hatef; Myrehaug, Sten; Soliman, Hany; Sahgal, Arjun; Stanisz, Greg J.

doi:10.1038/s41598-018-20624-6

Cited by 33 publications

(36 citation statements)

References 34 publications

(43 reference statements)

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“…The macromolecular content of tissue can thus be estimated from water content without any need for further modeling (16,69). It is interesting to point out that this is not the case for the bound proton fraction derived with quantitative magnetization transfer (qMT), where simplifications are needed (90)(91)(92)(93)(94). For example, the properties of different types of macromolecules need to be averaged, resulting in an "effective bound pool."…”

Section: Distribution In White and Gray Mattermentioning

confidence: 99%

“…We propose that the method could be used for clinical and neurological applications where changes in the macromolecular proton fraction are of interest. Although quantitative magnetization transfer is showing promise in clinical applications (93,94), water content mapping as offered by this method is faster, with direct biological significance and is much less affected by model hypotheses and simplifications than qMT.…”

Section: Applicationsmentioning

confidence: 99%

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A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping With Neurobiological Implications

Loução

Abbas

et al. 2019

Front. Neurol.

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Water concentration is tightly regulated in the healthy human brain and changes only slightly with age and gender in healthy subjects. Consequently, changes in water content are important for the characterization of disease. MRI can be used to measure changes in brain water content, but as these changes are usually in the low percentage range, highly accurate and precise methods are required for detection. The method proposed here is based on a long-TR (10 s) multiple-echo gradient-echo measurement with an acquisition time of 7:21 min. Using such a long TR ensures that there is no T 1 weighting, meaning that the image intensity at zero echo time is only proportional to the water content, the transmit field, and to the receive field. The receive and transmit corrections, which are increasingly large at higher field strengths and for highly segmented coil arrays, are multiplicative and can be approached heuristically using a bias field correction. The method was tested on 21 healthy volunteers at 3T field strength. Calibration using cerebral-spinal fluid values (∼100% water content) resulted in mean values and standard deviations of the water content distribution in white matter and gray matter of 69.1% (1.7%) and 83.7% (1.2%), respectively. Measured distributions were coil-independent, as seen by using either a 12-channel receiver coil or a 32-channel receiver coil. In a test-retest investigation using 12 scans on one volunteer, the variation in the mean value of water content for different tissue types was ∼0.3% and the mean voxel variability was ∼1%. Robustness against reduced SNR was assessed by comparing results for 5 additional volunteers at 1.5T and 3T. Furthermore, water content distribution in gray matter is investigated and regional contrast reported for the first time. Clinical applicability is illustrated with data from one stroke patient and one brain tumor patient. It is anticipated that this fast, stable, easyto-use, high-quality mapping method will facilitate routine quantitative MR imaging of water content.

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Section: Distribution In White and Gray Mattermentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping With Neurobiological Implications

Loução

Abbas

et al. 2019

Front. Neurol.

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“…Unfortunately, neither of these methods allows validation of the MT effect with existing qMT literature; there is either no parameter to investigate the MT effect (MTR asym ) or the parameter describing the MT effect is derived using different methodologies (AREX), meaning it has a different parameter range than what has already been established in the literature. By harmonizing the estimates derived from a CEST analysis with established methods in the qMT literature, we can better correlate the structural (qMT) and chemical (CEST) environments, particularly in pathologies such as multiple sclerosis, and cancer …”

Section: Discussionmentioning

confidence: 99%

Does the magnetization transfer effect bias chemical exchange saturation transfer effects? Quantifying chemical exchange saturation transfer in the presence of magnetization transfer

Smith

Ray

Larkin

et al. 2020

Magnetic Resonance in Med

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Purpose: Chemical exchange saturation transfer (CEST) is an MRI technique sensitive to the presence of low-concentration solute protons exchanging with water. However, magnetization transfer (MT) effects also arise when large semisolid molecules interact with water, which biases CEST parameter estimates if quantitative models do not account for macromolecular effects. This study establishes under what conditions this bias is significant and demonstrates how using an appropriate model provides more accurate quantitative CEST measurements. Methods: CEST and MT data were acquired in phantoms containing bovine serum albumin and agarose. Several quantitative CEST and MT models were used with the phantom data to demonstrate how underfitting can influence estimates of the CEST effect. CEST and MT data were acquired in healthy volunteers, and a twopool model was fit in vivo and in vitro, whereas removing increasing amounts of CEST data to show biases in the CEST analysis also corrupts MT parameter estimates. Results: When all significant CEST/MT effects were included, the derived parameter estimates for each CEST/MT pool significantly correlated (P < .05) with bovine serum albumin/agarose concentration; minimal or negative correlations were found with underfitted data. Additionally, a bootstrap analysis demonstrated that significant biases occur in MT parameter estimates (P < .001) when unmodeled CEST data are included in the analysis. Conclusions: These results indicate that current practices of simultaneously fitting both CEST and MT effects in model-based analyses can lead to significant bias in all 1360 | SMITH eT al. | THEORYSimilar to the analytical solution for a two-pool model described in the various papers by Yarnykh et al 36,37 and Zhou et al,4,38 we parameter estimates unless a sufficiently detailed model is utilized. Therefore, care must be taken when quantifying CEST and MT effects in vivo by properly modeling data to minimize these biases. K E Y W O R D S amide, CEST, MT, NOE, qMT, quantitative 1366 | SMITH eT al. F I G U R E 3 Semisolid (PSR), and amide and NOE (MTR*) maps for the variable agarose phantom experiment. The top row illustrates the full, four-pool model estimation, the middle displays the fixed-qMT estimation, and the bottom row shows the same fit assuming the NOE and MT pools are a single pool, similar to Tee et al 34 F I G U R E 4 Semisolid (PSR), and amide and NOE (MTR*) maps for the variable BSA phantom experiment. The top row illustrates the full, four-pool model estimation, the middle displays the fixed-qMT estimation, and the bottom row shows the same fit assuming the NOE and MT pools are a single pool, similar to similar to Tee et al 34

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“…An early oncological application performed MT‐MRI of excised breast tissue to identify malignant tissue . The technique has since been performed in clinical studies, including the use of qMT to assess response of glioblastoma response to chemoradiation . The repeatability and reproducibility of MT‐MRI in tumors has not been established, but repeatability has been characterized in healthy breast tissue (Fig.…”

Section: Quantitatively Interrogating Biologymentioning

confidence: 99%

Translating preclinical MRI methods to clinical oncology

Hormuth

Sorace

Virostko

et al. 2019

Magnetic Resonance Imaging

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The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft‐tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377–1392.

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Quantitative Magnetization Transfer in Monitoring Glioblastoma (GBM) Response to Therapy

Cited by 33 publications

References 34 publications

A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping With Neurobiological Implications

A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping With Neurobiological Implications

Does the magnetization transfer effect bias chemical exchange saturation transfer effects? Quantifying chemical exchange saturation transfer in the presence of magnetization transfer

Translating preclinical MRI methods to clinical oncology

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