A probability‐based multi‐cycle sorting method for 4D‐MRI: A simulation study

Liang, Xiao; Yin, Fang‐Fang; Liu, Yilin; Cai, Jing

doi:10.1118/1.4966705

Cited by 7 publications

(9 citation statements)

References 32 publications

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“…The general method of probability-based sorting method for multi-cycle 4D-MRI reconstruction has been described in our previous study (17). In that study, the method was applied to 2D MR acquisitions (cine and sequential) for 4D-MRI sorting and the results demonstrate that this method not only produces multi-cycle 4D-MRI images that represent the main breathing patterns of the patient, but also reduces breathing variation artifacts in each 4D-MRI image set.…”

Section: Methodsmentioning

confidence: 97%

“…The design and workflow of the probability-based 3D k-space sorting method for multi-cycle 4D-MRI, as shown in Figure 1, is adapted from the probability-based 2D image sorting method for multi-cycle 4D-MRI (17) with two major alterations: (I) sorting is performed on 3D k-space data instead of 2D MR images; (II) MR images are reconstructed using the fast Fourier transform (FFT) after 3D k-space sorting (18), whereas this was unnecessary in the previous study as the MR images had already been reconstructed. A detailed review of the probability-based sorting method can be found in the literature (17) and thus will only be briefly described here. The main breathing cycles of the breathing curves were extracted using principle component analysis (PCA) method: Firstly, the breathing signal is decomposed into individual breathing cycles, which are defined as segments of the breathing signal between two consecutive end-of-exhale (EOE) peaks.…”

Section: Probability-based 3d K-space Sorting For Multi-cycle 4d-mrimentioning

confidence: 99%

“…where, T main cycle, j and w main cycle, j are the period and the weighting (frequency of occurrence) of the jth main cycle; I j, k is the 3D image volume at the kth amplitude bin of the jth main cycle; and µ j, k is the ratio of the time interval at the kth amplitude bin in the jth main cycle to the jth main cycle period, T main cycle, j . Furthermore, the difference maps between the reference AIP (from the original 4D-MRI/4D-XCAT) and the two AIPs under evaluation (from single-cycle 4D-MRI and multi-cycle 4D-MRI) were determined as (17):…”

Section: Digital Phantom Studymentioning

confidence: 99%

“…EOI, end-of-inhale. A B C Figure 5 Simulation results for the patient's breathing curve (A) which is composed of three main breathing patterns determined using the probability-based sorting method (B) (17), and the resulting 4D-MRI images (C) for the two sorting methods. from 0.46 to 0.21 for the patient curve.…”

Section: Figurementioning

confidence: 99%

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Probability-based 3D k-space sorting for motion robust 4D-MRI

Sun

Liang

Yin

et al. 2019

Quant. Imaging Med. Surg.

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Background: Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency. Methods: The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiactorso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signalto-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation. Results: The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve. Conclusions: We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets.

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Section: Methodsmentioning

confidence: 97%

Section: Probability-based 3d K-space Sorting For Multi-cycle 4d-mrimentioning

confidence: 99%

Section: Digital Phantom Studymentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Probability-based 3D k-space sorting for motion robust 4D-MRI

Sun

Liang

Yin

et al. 2019

Quant. Imaging Med. Surg.

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“…In retrospective methods inherited from 4DCT, sorting of slices is usually based on an external surrogate (Hu et al 2013). Different strategies were investigated to improve the performance of the external surrogate, either making use of audio-visual biofeedback (To et al 2016b) or advanced sorting (Liu et al 2015, Liang et al 2016, Tryggestad et al 2013d, Du et al 2015. As previously mentioned however, the use of internal breathing surrogates directly extracted from the acquired 2D images has been shown to increase robustness in organ motion description with respect to external surrogates (Stemkens et al 2015, Liu et al 2016a, Li et al 2017.…”

Section: Respiratory-correlated (4d) Mrimentioning

confidence: 99%

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

et al. 2018

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Multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI): Development and initial evaluation in liver tumor patients

Zhang

Yin

et al. 2021

Medical Physics

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To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. Methods and materials:The MC-4D-MRI technique integrates multiparametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image preprocessing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of -inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. Results: MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV interobserver mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions. Conclusion:We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial 7984

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A probability‐based multi‐cycle sorting method for 4D‐MRI: A simulation study

Cited by 7 publications

References 32 publications

Probability-based 3D k-space sorting for motion robust 4D-MRI

Probability-based 3D k-space sorting for motion robust 4D-MRI

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

Multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI): Development and initial evaluation in liver tumor patients

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