A new algebraic method for quantitative proton density mapping using multi-channel coil data

Cordes, Dietmar; Yang, Zhiguo; Zhuang, Xiaowei; Sreenivasan, Karthik; Mishra, Virendra; Hua, Le H.

doi:10.1016/j.media.2017.06.007

Cited by 4 publications

(7 citation statements)

References 34 publications

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“…In this work, the phantom results confirmed the accuracy of T 1 (and thus B 1t ),

{normalT}_{2} *

, and PD maps, and the preliminary ROI analysis on the brain results confirmed the consistency of T 1 , 13

{normalT}_{2} *

, 35 PD, 26 and QSM 30,31 values with literatures, all indicating the reliable quantitative capacity of our method.…”

Section: Discussionsupporting

confidence: 83%

“…of T 1 , 13 T 2 * , 35 PD, 26 and QSM 30,31 values with literatures, all indicating the reliable quantitative capacity of our method.…”

Section: Quantitative Accuracysupporting

confidence: 80%

“…Note that this PD map is in the form of W p M 0 and thus is still coupled with receiver coil sensitivity. This initial PD map is therefore further adjusted with an estimated coil sensitivity map, 26 and then normalized by the mean values in CSF regions and finally converted to percentage units (p.u. ).…”

Section: Methodsmentioning

confidence: 99%

“…estimated coil sensitivity map, 26 and then normalized by the mean values in CSF regions and finally converted to percentage units (p.u. ).…”

Section: Pd Mappingmentioning

confidence: 99%

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MULTI‐parametric MR imaging with fLEXible design (MULTIPLEX)

Lyu

et al. 2021

Magnetic Resonance in Med

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Purpose To introduce a gradient echo (GRE) ‐based method, namely MULTIPLEX, for single‐scan 3D multi‐parametric MRI with high resolution, signal‐to‐noise ratio (SNR), accuracy, efficiency, and acquisition flexibility. Theory With a comprehensive design with dual‐repetition time (TR), dual flip angle (FA), multi‐echo, and optional flow modulation features, the MULTIPLEX signals contain information on radiofrequency (RF) B1t fields, proton density, T1, susceptibility and blood flows, facilitating multiple qualitative images and parametric maps. Methods MULTIPLEX was evaluated on system phantom and human brains, via visual inspection for image contrasts and quality or quantitative evaluation via simulation, phantom scans and literature comparison. Region‐of‐interest (ROI) analysis was performed on parametric maps of the system phantom and brain scans, extracting the mean and SD of the T1, normalT2∗, proton density (PD), and/or quantitative susceptibility mapping (QSM) values for comparison with reference values or literature. Results One MULTIPLEX scan offers multiple sets of images, including but not limited to: composited PDW/T1W/normalT2∗W, aT1W, SWI, MRA (optional), B1t map, T1 map, normalT2∗/normalR2∗ maps, PD map, and QSM. The quantitative error of phantom T1, normalT2∗ and PD mapping were <5%, and those in brain scans were in good agreement with literature. MULTIPLEX scan times for high resolution (0.68 × 0.68 × 2 mm3) whole brain coverage were about 7.5 min, while processing times were <1 min. With flow modulation, additional MRA images can be obtained without affecting the quality or accuracy of other images. Conclusion The proposed MUTLIPLEX method possesses great potential for multi‐parametric MR imaging.

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“…In this work, the phantom results confirmed the accuracy of T 1 (and thus B 1t ),

{normalT}_{2} *

, and PD maps, and the preliminary ROI analysis on the brain results confirmed the consistency of T 1 , 13

{normalT}_{2} *

, 35 PD, 26 and QSM 30,31 values with literatures, all indicating the reliable quantitative capacity of our method.…”

Section: Discussionsupporting

confidence: 83%

“…of T 1 , 13 T 2 * , 35 PD, 26 and QSM 30,31 values with literatures, all indicating the reliable quantitative capacity of our method.…”

Section: Quantitative Accuracysupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

“…estimated coil sensitivity map, 26 and then normalized by the mean values in CSF regions and finally converted to percentage units (p.u. ).…”

Section: Pd Mappingmentioning

confidence: 99%

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MULTI‐parametric MR imaging with fLEXible design (MULTIPLEX)

Lyu

et al. 2021

Magnetic Resonance in Med

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“…This is because, despite its apparent simplicity, several confounding factors, such as inhomogeneity of static, transmit, and receive fields, as well as T * 2 decay, must be considered and corrected for when measuring water content with MRI. In recent years, interest in water content mapping has increased (16,(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54). This is in line with a general growth in quantitative imaging with MRI and is also related to the fact that availability of higherfield systems with parallel imaging capabilities enable higher-SNR and faster data acquisition, respectively-both crucial for quantitative water content mapping.…”

Section: Introductionmentioning

confidence: 80%

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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MR‐based electrical property tomography using a physics‐informed network at 3 and 7 T

Zheng,

Lou,

Huang

et al. 2024

NMR in Biomedicine

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Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non‐invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high‐resolution EPs reconstruction based on water content maps by using a physics‐informed network (PIN‐wEPT). The proposed method utilizes standard clinical protocols and conventional multi‐channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of and by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN‐wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

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A new algebraic method for quantitative proton density mapping using multi-channel coil data

Cited by 4 publications

References 34 publications

MULTI‐parametric MR imaging with fLEXible design (MULTIPLEX)

MULTI‐parametric MR imaging with fLEXible design (MULTIPLEX)

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

MR‐based electrical property tomography using a physics‐informed network at 3 and 7 T

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