Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study

Konar, Amaresha Shridhar; Shah, Akash; Paudyal, Ramesh; Fung, Maggie; Banerjee, Suchandrima; Dave, Abhay; Hatzoglou, Vaios; Shukla–Dave, Amita

doi:10.3390/cancers14225606

Cited by 3 publications

(5 citation statements)

References 66 publications

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“…Of note, in both the study by de Blank et al 42 and Konar et al, 43 there was no significant difference found between treated and untreated lesions, possibly due to small sample size, and this remains to be explored. 42,43 Efforts have also been made to differentiate grade and subtypes of gliomas using MRF. Radiomic analysis of 2D MRF and 3D MRF has demonstrated features helpful for glioma grade differentiation, [44][45][46] and MRF radiomics have also shown utility in prediction of IDH1 mutations.…”

Section: Technical Overviewmentioning

confidence: 82%

“…In a similar study of 23 children and young adults by de Blank et al, 42 high-grade gliomas had significantly higher T1 and T2 compared with low-grade gliomas (T1: 1863 ± 70 milliseconds vs 1355 ± 187 milliseconds, P = 0.007; T2: 90 ± 13 milliseconds vs 56 ± 19 milliseconds, P = 0.013), and peritumoral white matter around low-grade gliomas had significantly lower T1 than peritumoral white matter around high-grade gliomas (T1: 1154 ± 253 milliseconds vs 1581 ± 476 milliseconds, P = 0.039). In a study by Konar et al, 43 both treated and untreated brain metastases showed higher T1 and T2 values than normal brain structures. Of note, in both the study by de Blank et al 42 and Konar et al, 43 there was no significant difference found between treated and untreated lesions, possibly due to small sample size, and this remains to be explored 42,43 …”

Section: Current Clinical Applicationsmentioning

confidence: 90%

“…In a study by Konar et al, 43 both treated and untreated brain metastases showed higher T1 and T2 values than normal brain structures. Of note, in both the study by de Blank et al 42 and Konar et al, 43 there was no significant difference found between treated and untreated lesions, possibly due to small sample size, and this remains to be explored. 42,43 Efforts have also been made to differentiate grade and subtypes of gliomas using MRF.…”

Section: Technical Overviewmentioning

confidence: 90%

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Magnetic Resonance Fingerprinting

et al. 2023

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Magnetic Resonance (MR) is an exceptionally powerful and versatile measurement technique. The basic structure of an MR experiment has remained nearly constant for almost 50 years. Here we introduce a novel paradigm, Magnetic Resonance Fingerprinting (MRF) that permits the noninvasive quantification of multiple important properties of a material or tissue simultaneously through a new approach to data acquisition, post-processing and visualization. MRF provides a new mechanism to quantitatively detect and analyze complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to specifically identify the presence of a target material or tissue, which will increase the sensitivity, specificity, and speed of an MR study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern recognition algorithm, MRF inherently suppresses measurement errors and thus can improve accuracy compared to previous approaches.

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Section: Technical Overviewmentioning

confidence: 82%

Section: Current Clinical Applicationsmentioning

confidence: 90%

Section: Technical Overviewmentioning

confidence: 90%

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Magnetic Resonance Fingerprinting

et al. 2023

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“…QAMPER is currently available through a material transfer agreement (MTA) with our center. Our group has experience with MR Fingerprinting (MRF) (refs: phantom, brain metastases [57,58]), which provides simultaneous T 1 and T 2 relaxometry measures in a clinically feasible amount of time. We plan to include the MRF reconstruction algorithm in our next implementation of the QAMPER platform.…”

Section: Discussionmentioning

confidence: 99%

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

LoCastro,

Paudyal,

Konar

et al. 2023

Tomography

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There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines (“MRI-QAMPER”, current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER’s functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test–retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

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“…This issue has been particularly challenging in vitro when small volume samples are required due to the high cost of materials or with the goal of more representative translation to ex vivo/in vivo settings 34,35 . The ISMRM/NIST MRI phantom was successfully developed for clinical applications but requires a sample size that is too large to be suitable for small animal preclinical systems 60 . Thus, there remains a need for a testbed to image small volume samples in preclinical scanners.…”

Section: Introductionmentioning

confidence: 99%

Dynamic platform for magnetic resonance imaging of bioresponsive contrast agents

Perera Gonzalez

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Magnetic Resonance Imaging (MRI) is a powerful non-invasive imaging modality with unlimited penetration depth, conventionally used for visualization of anatomical details. In efforts to better understand our bodies, there is an increasing interest to image chemical signatures in living systems with MRI. Research efforts have worked to overcome limitations of conventional MRI approaches, such as utilizing MRI contrast agents (CA) to improve molecular sensitivity and developing fast, quantitative methods with future potential for in vivo chemical imaging.Our research has focused on the optimization of a protocol to evaluate MRI CA, including hardware, experiment design, MRI data acquisition, and analysis. Here we proposed a reliable method for imaging small volume contrast agents in preclinical MRI scanners via a Microliter Scale Concentric (MiSCo) testbed as a hardware upgrade. We also demonstrated the success of MiSCo testbed to characterize pH-responsive MRI CA and help plan for translation towards ex vivo experiments in a rat brain model. To acquire dynamic data, we have engineered a Spiral Acquisition Matching Based Algorithm (SAMBA) for fast, quantitative preclinical imaging at low field strength (3T). Although spiral acquisitions and matching based algorithms are not novel, this work demonstrated how quantitative T1 and T2 spiral sequences paired with a dictionary matching-based algorithm provided in vitro results comparable to conventional approaches in a fraction of the scanning time. In addition, the insensitivity to motion artifacts characteristic of fingerprinting-like matching-based algorithms, demonstrated by Ma et al (2013), sets the stage for future in vivo work. This proposed platform for preclinical implementation serves as a foundation to image biochemical analytes in live animals in the near future, with the ultimate objective of unlocking the potential of MRI for chemical imaging of living systems.

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Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study

Cited by 3 publications

References 66 publications

Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology

Dynamic platform for magnetic resonance imaging of bioresponsive contrast agents

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