Effect of spiral undersampling patterns on FISP MRF parameter maps

Körzdörfer, Gregor; Pfeuffer, Josef; Kluge, T.; Gebhardt, Matthias; Hensel, Bernhard; Meyer, Craig H.; Nittka, Mathias

doi:10.1016/j.mri.2019.01.011

Cited by 30 publications

(47 citation statements)

References 20 publications

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“…Relaxation times in the parietal cortex were longer than those in the medial thalamus, caudate nucleus, and putamen, reflecting various mixtures of GM with neighboring WM or CSF. Short mapped T 2 values and frontal–parietal differences in mapped T 1 values, which are due to a spatial artifact from the spiral undersampling pattern, are consistent with previous findings and have been investigated in other MRF studies . For the purpose of this proof‐of‐concept work, they are treated as systematic biases and discussed in detail later in the text.…”

Section: Resultssupporting

confidence: 83%

“…A third artifact in the current implementation of 2D‐MRF is the spatial bias arising from the spiral k ‐space readout . The effects of this bias appeared here in differing frontal and parietal WM relaxation times and the k‐ means clustering results in 2D MRF.…”

Section: Discussionmentioning

confidence: 88%

“…Using the PV dictionary notably reduced tissue fraction errors, which mainly affected outer regions of the brain. Undersampling artifacts also affected mapped T 1 values, indicating that PV‐MRF tissue fractions can be influenced by the accuracy of the relaxation times represented in the PV subdictionary.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, 2D MRF was accelerated using 48‐fold spiral undersampling with a 7.5° rotation of the variable density spiral trajectory between successive TRs. However, this small rotation increment of the trajectory produces a somewhat coherent aliasing pattern which can bias mapped relaxation times, especially T 1 , in a spatially variable way . The noise‐like artifacts and spatial T 1 bias generated large tissue fraction errors using the pseudoinverse calculation.…”

Section: Discussionmentioning

confidence: 99%

“…In simulation and in vivo, noise‐tolerant PV dictionary‐matching restricted errors to those arising from T 1 error alone, the severity of which were predicted in simulations. Changing the spiral interleave ordering can mitigate the spatial T 1 bias . Alternatively, a sliding window or iterative MRF reconstructions can reduce the severity of aliasing artifacts.…”

Section: Discussionmentioning

confidence: 99%

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Partial volume mapping using magnetic resonance fingerprinting

Deshmane

McGivney

et al. 2019

NMR in Biomedicine

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Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction.The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error.Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient.PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T 1 resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRFmapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema.We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.

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Section: Resultssupporting

confidence: 83%

Section: Discussionmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Partial volume mapping using magnetic resonance fingerprinting

Deshmane

McGivney

et al. 2019

NMR in Biomedicine

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Magnetic resonance fingerprinting review part 2: Technique and directions

McGivney

Boyacıoğlu

Jiang

et al. 2019

Magnetic Resonance Imaging

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Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR‐sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993–1007.

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Reconstruction of multi‐phase parametric maps in 4D‐magnetic resonance fingerprinting (4D‐MRF) by optimization of local T1 and T2 sensitivities

Wong,

Li,

Liu

et al. 2024

Medical Physics

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BackgroundTime‐resolved magnetic resonance fingerprinting (MRF), or 4D‐MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D‐MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues’ properties of each phase.PurposeTo develop a technique for the reconstruction of multi‐phase parametric maps in four‐dimensional magnetic resonance fingerprinting (4D‐MRF) through the optimization of local T1 and T2 sensitivities.MethodsThe proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter‐phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi‐phase signal evolution curves were modified by sliding window reconstruction and inter‐phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi‐parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac‐torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner.ResultsIn simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight‐phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in‐vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively.ConclusionsBoth simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi‐phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.

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Effect of spiral undersampling patterns on FISP MRF parameter maps

Cited by 30 publications

References 20 publications

Partial volume mapping using magnetic resonance fingerprinting

Partial volume mapping using magnetic resonance fingerprinting

Magnetic resonance fingerprinting review part 2: Technique and directions

Reconstruction of multi‐phase parametric maps in 4D‐magnetic resonance fingerprinting (4D‐MRF) by optimization of local T1 and T2 sensitivities

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