An information theory model for optimizing quantitative magnetic resonance imaging acquisitions

Mitchell, Drew; Hwang, Ken Pin; Bankson, James A.; Stafford, R. Jason; Banerjee, Suchandrima; Takei, Naoyuki; Fuentes, David

doi:10.1088/1361-6560/abb9f6

Cited by 4 publications

(5 citation statements)

References 171 publications

(234 reference statements)

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“…Here the problem structure is used to accelerate computations and facilitate tractable numerical integration. Following 45 , Gauss-Hermite quadrature is applied in each dimension of mutual information integrals defined in ( 16 ) to numerically integrate multi-variate Gaussian random variables. Quadrature order is increased until convergence is observed.…”

Section: Methodsmentioning

confidence: 99%

Mutual-information based optimal experimental design for hyperpolarized $$^{13}$$C-pyruvate MRI

Jha,

Walker,

Mitchell

et al. 2023

Sci Rep

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A key parameter of interest recovered from hyperpolarized (HP) MRI measurements is the apparent pyruvate-to-lactate exchange rate, $$k_{{\textrm PL}}$$ k P L , for measuring tumor metabolism. This manuscript presents an information-theory-based optimal experimental design approach that minimizes the uncertainty in the rate parameter, $$k_{{\textrm PL}}$$ k P L , recovered from HP-MRI measurements. Mutual information is employed to measure the information content of the HP measurements with respect to the first-order exchange kinetics of the pyruvate conversion to lactate. Flip angles of the pulse sequence acquisition are optimized with respect to the mutual information. A time-varying flip angle scheme leads to a higher parameter optimization that can further improve the quantitative value of mutual information over a constant flip angle scheme. However, the constant flip angle scheme, 35 and 28 degrees for pyruvate and lactate measurements, leads to an accuracy and precision comparable to the variable flip angle schemes obtained from our method. Combining the comparable performance and practical implementation, optimized pyruvate and lactate flip angles of 35 and 28 degrees, respectively, are recommended.

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Section: Methodsmentioning

confidence: 99%

Mutual-information based optimal experimental design for hyperpolarized $$^{13}$$C-pyruvate MRI

Jha,

Walker,

Mitchell

et al. 2023

Sci Rep

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“…83 Since motion is less of a concern in the brain, optimization methods may be applied to the sequence without constraints on delay timings. 84 Since voxel sizes are small, the 3D parameter maps and synthetic images may be reformatted into multiple planes, and their geometry are also more ideal for cortical thickness measurements. 79 By using a deep learning approach, 3D MR angiograms have also been synthesized from the data without the aid of any additional sequences, 85 potentially offering another type of synthetic image contrast (Figure 7).…”

Section: Future Directionsmentioning

confidence: 99%

“…As with the 2D technique, 3D‐QALAS is being explored for pediatric imaging, brain cancer, 80 and multiple sclerosis, 83 and is compatible with the previously developed tissue classification and myelin model in the SyMRI software 83 . Since motion is less of a concern in the brain, optimization methods may be applied to the sequence without constraints on delay timings 84 . Since voxel sizes are small, the 3D parameter maps and synthetic images may be reformatted into multiple planes, and their geometry are also more ideal for cortical thickness measurements 79 .…”

Section: Future Directionsmentioning

confidence: 99%

Synthetic MR: Physical principles, clinical implementation, and new developments

Hwang

Fujita

2022

Medical Physics

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Current clinical MR imaging practices rely on the qualitative assessment of images for diagnosis and treatment planning. While contrast in MR images is dependent on the spin parameters of the imaged tissue, pixel values on MR images are relative and are not scaled to represent any tissue properties. Synthetic MR is a fully featured imaging workflow consisting of efficient multiparameter mapping acquisition, synthetic image generation, and volume quantitation of brain tissues. As the application becomes more widely available on multiple vendors and scanner platforms, it has also gained widespread adoption as clinicians begin to recognize the benefits of rapid quantitation. This review will provide details about the sequence with a focus on the physical principles behind its relaxometry mechanisms. It will present an overview of the products in their current form and some potential issues when implementing it in the clinic. It will conclude by highlighting some recent advances of the technique, including a 3D mapping method and its associated applications.

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“…Although many promising multiparametric mapping techniques have been proposed, 8–13 only few have demonstrated compatibility across vendors or provided reproducible values across MRI systems from different manufacturers. One cross‐vendor multiparametric technique is three‐dimensional quantification using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (3D‐QALAS), allowing for rapid eD T1 and T2 relaxation times and proton density (PD) mapping of the brain 26–28 …”

Section: Introductionmentioning

confidence: 99%

“…One cross-vendor multiparametric technique is three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS), allowing for rapid eD T1 and T2 relaxation times and proton density (PD) mapping of the brain. [26][27][28] Therefore, this study aimed to evaluate a vendor-agnostic, whole-brain, multiparametric mapping scheme based on 3D-QALAS. First, we evaluated the linearity and bias 29 of this technique using a standardized MRI system phantom.…”

Section: Introductionmentioning

confidence: 99%

Cross‐vendor multiparametric mapping of the human brain using 3D‐QALAS: A multicenter and multivendor study

Fujita,

Gagoski,

Hwang

et al. 2024

Magnetic Resonance in Med

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PurposeTo evaluate a vendor‐agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look–Locker acquisition sequence with a T2 preparation pulse (3D‐QALAS) for whole‐brain T1, T2, and proton density (PD) mapping.MethodsThis prospective, multi‐institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra‐scanner repeatability and inter‐vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan‐rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D‐QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers.ResultsQuantitative maps obtained with 3D‐QALAS showed high linearity (R2 = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra‐scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure‐wise test–retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter‐vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis.ConclusionThe vendor‐agnostic multiparametric mapping technique 3D‐QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

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An information theory model for optimizing quantitative magnetic resonance imaging acquisitions

Cited by 4 publications

References 171 publications

Mutual-information based optimal experimental design for hyperpolarized $$^{13}$$C-pyruvate MRI

Mutual-information based optimal experimental design for hyperpolarized $$^{13}$$C-pyruvate MRI

Synthetic MR: Physical principles, clinical implementation, and new developments

Cross‐vendor multiparametric mapping of the human brain using 3D‐QALAS: A multicenter and multivendor study

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