Four-Dimensional Magnetic Resonance Imaging Using Axial Body Area as Respiratory Surrogate: Initial Patient Results

Yang, Juan; Cai, Jing; Wang, Hongjun; Chang, Zheng; Czito, Brian G.; Bashir, Mustafa R.; Yin, Fang‐Fang

doi:10.1016/j.ijrobp.2013.11.245

Cited by 42 publications

(53 citation statements)

References 19 publications

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“…The feasibility of this technique has been validated in our previous publication 9 , 10 and we will briefly describe this technique here.…”

Section: Methodsmentioning

confidence: 98%

“…Currently, we also developed a retrospective 4D‐MRI technique using body area (BA) as an internal respiratory surrogate (9) . Preliminary results in liver cancer patients have demonstrated the feasibility and fidelity of this technique (10) . However, unavoidable artifacts in the reconstructed 4D MR images were observed.…”

Section: Introductionmentioning

confidence: 91%

“…Currently, many 4D‐MRI techniques have been proposed mainly containing two approaches: 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 1) using a 3D MR sequence to acquire real‐time volumetric images (i.e., real‐time 4D MRI); and 2) using a fast 2D MR sequence to continuously acquire images from all respiratory phases and retrospectively sort these images according to the respiratory phases (i.e., retrospective 4D MRI). However, to acquire high‐resolution 4D MR images using the first approach is difficult due to the limitations of current available hardware and software.…”

Section: Introductionmentioning

confidence: 99%

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Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model

Yang

Wang

Yin

et al. 2015

J Applied Clin Med Phys

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We have previously developed a retrospective 4D‐MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases (5/7) in the liver, as well as a patient with non‐small cell lung cancer (NSCLC), were enrolled in an IRB‐approved prospective study. Both CT and MR simulations were performed for each patient for treatment planning. Multiple‐slice, multiple‐phase, cine‐MRI images were acquired in the axial plane for 4D‐MRI reconstruction. Single‐slice 2D cine‐MR images were acquired across the center of the tumor in axial, coronal, and sagittal planes. For a 4D MR image dataset, the DVFs in three orthogonal direction (inferior–superior (SI), anterior–posterior (AP), and medial–lateral (ML)) relative to a specific reference phase were calculated using an in‐house DIR algorithm. The DVFs were preprocessed in three temporal and spatial dimensions using a polynomial fitting model, with the goal of correcting the potential registration errors introduced by three‐dimensional DIR. Then PCA was used to decompose each fitted DVF into a linear combination of three principal motion bases whose spanned subspaces combined with their projections had been validated to be sufficient to represent the regular respiratory motion. By wrapping the reference MR image using the remodeled DVFs, ‘synthetic’ MR images with reduced motion artifacts were generated at selected phase. Tumor motion trajectories derived from cine‐MRI, 4D CT, original 4D MRI, and ‘synthetic’ 4D MRI were analyzed in the SI, AP, and ML directions, respectively. Their correlation coefficient (CC) and difference (D) in motion amplitude were calculated for comparison. Of all the patients, the means and standard deviations (SDs) of CC comparing ‘synthetic’ 4D MRI and cine‐MRI were 0.98±0.01,0.98±0,01, and 0.99±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.59±0.09 mm,0.29±0.10 mm, and 0.15±0.05 mm in SI, AP and ML directions, respectively. The means and SDs of CC comparing ‘synthetic’ 4D MRI and 4D CT were 0.96±0.01,0.95±0.01, and 0.95±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.76±0.20 mm,0.33±0.14 mm, and 0.19±0.07 mm in SI, AP, and ML directions, respectively. The means and SDs of CC comparing ‘synthetic’ 4D MRI and original 4D MRI were 0.98±0.01,0.98±0.01, and 0.97±0.01 in SI, AP, and ML directions, respectively. The mean±SD Ds were 0.58±0.10 mm...

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“…The feasibility of this technique has been validated in our previous publication 9 , 10 and we will briefly describe this technique here.…”

Section: Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model

Yang

Wang

Yin

et al. 2015

J Applied Clin Med Phys

Self Cite

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“…For example, we have previously demonstrated that a T2 * /T1-weighted retrospective 4D-MRI technique using a TrueFISP/FIESTA pulse sequence 15 can achieve adequate temporal resolution (∼3 frames/s), acceptable spatial resolution (1.8×1.8×5 mm), but suffered from suboptimal and inconsistent tumor contrast. In our preliminary patient study, we have observed large degree of intersubject variations in tumor contrast of the 4D-MRI (range: 4.57-45.00; standard deviation: 14.63), 16 presumably due to the admittedly suboptimal T2*/T1 weighting tumor contrast of TrueFISP/FIESTA. On the contrary, some MR sequences can provide better tumor contrast but are not fast enough for retrospective 4D-MRI.…”

Section: Introductionmentioning

confidence: 82%

Four dimensional magnetic resonance imaging with retrospective k‐space reordering: A feasibility study

et al. 2015

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Purpose: Current four dimensional magnetic resonance imaging (4D-MRI) techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of a new strategy for 4D-MRI which is based on retrospective k-space reordering. Methods: We simulated a k-space reordered 4D-MRI on a 4D digital extended cardiac-torso (XCAT) human phantom. A 2D echo planar imaging MRI sequence [frame rate (F) = 0.448 Hz; image resolution (R) = 256 × 256; number of k-space segments (N KS ) = 4] with sequential image acquisition mode was assumed for the simulation. Image quality of the simulated "4D-MRI" acquired from the XCAT phantom was qualitatively evaluated, and tumor motion trajectories were compared to input signals. In particular, mean absolute amplitude differences (D) and cross correlation coefficients (CC) were calculated. Furthermore, to evaluate the data sufficient condition for the new 4D-MRI technique, a comprehensive simulation study was performed using 30 cancer patients' respiratory profiles to study the relationships between data completeness (C p ) and a number of impacting factors: the number of repeated scans (N R ), number of slices (N S ), number of respiratory phase bins (N P ), N KS , F, R, and initial respiratory phase at image acquisition (P 0 ). As a proof-of-concept, we implemented the proposed k-space reordering 4D-MRI technique on a T2-weighted fast spin echo MR sequence and tested it on a healthy volunteer. Results: The simulated 4D-MRI acquired from the XCAT phantom matched closely to the original XCAT images. Tumor motion trajectories measured from the simulated 4D-MRI matched well with input signals (D = 0.83 and 0.83 mm, and CC = 0.998 and 0.992 in superior-inferior and anterior-posterior directions, respectively). The relationship between C p and N R was found best represented by an exponential function (C P = 100 1 − e −0.18N R , when N S = 30, N P = 6). At a C P value of 95%, the relative error in tumor volume was 0.66%, indicating that N R at a C P value of 95% (N R,95% ) is sufficient. It was found that N R,95% is approximately linearly proportional to N P (r = 0.99), and nearly independent of all other factors. The 4D-MRI images of the healthy volunteer clearly demonstrated respiratory motion in the diaphragm region with minimal motion induced noise or aliasing. Conclusions: It is feasible to generate respiratory correlated 4D-MRI by retrospectively reordering k-space based on respiratory phase. This new technology may lead to the next generation 4D-MRI with high spatiotemporal resolution and optimal tumor contrast, holding great promises to improve the motion management in radiotherapy of mobile cancers. C 2015 American Association of Physicists in Medicine. [http://dx

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“…The acquisition mode of that sequence is in sequential-mode. 25 Although cine-mode 4D-MRI is achieved by using TrueFISP/FIESTA sequences, 27,28 the same image intensity assignment is used in the cine-mode 4D-MRI XCAT simulation for image quality comparison purpose. The HASTE/SSFSE MRI image intensities were selected for both simulations also because it provided T2-weighted signals, which have a high tumor-to-tissue contrast.…”

Section: A Digital Phantom Simulation Studymentioning

confidence: 99%

Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition

et al. 2015

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Purpose: The authors have recently developed a cine-mode T2*/T1-weighted 4D-MRI technique and a sequential-mode T2-weighted 4D-MRI technique for imaging respiratory motion. This study aims at investigating which 4D-MRI image acquisition mode, cine or sequential, provides more accurate measurement of organ motion during respiration. Methods: A 4D digital extended cardiac-torso (XCAT) human phantom with a hypothesized tumor was used to simulate the image acquisition and the 4D-MRI reconstruction. The respiratory motion was controlled by the given breathing signal profiles. The tumor was manipulated to move continuously with the surrounding tissue. The motion trajectories were measured from both sequential-and cine-mode 4D-MRI images. The measured trajectories were compared with the average trajectory calculated from the input profiles, which was used as references. The error in 4D-MRI tumor motion trajectory (E) was determined. In addition, the corresponding respiratory motion amplitudes of all the selected 2D images for 4D reconstruction were recorded. Each of the amplitude was compared with the amplitude of its associated bin on the average breathing curve. The mean differences from the average breathing curve across all slice positions (D) were calculated. A total of 500 simulated respiratory profiles with a wide range of irregularity (Ir) were used to investigate the relationship between D and Ir. Furthermore, statistical analysis of E and D using XCAT controlled by 20 cancer patients' breathing profiles was conducted. Wilcoxon Signed Rank test was conducted to compare two modes. Results: D increased faster for cine-mode (D = 1.17 × Ir + 0.23) than sequential-mode (D = 0.47 × Ir + 0.23) as irregularity increased. For the XCAT study using 20 cancer patients' breathing profiles, the median E values were significantly different: 0.12 and 0.10 cm for cine-and sequential-modes, respectively, with a p-value of 0.02. The median D values were significantly different: 0.47 and 0.24 cm for cine-and sequential-modes, respectively, with a p-value < 0.001. Conclusions: Respiratory motion measurement may be more accurate and less susceptible to breathing irregularity in sequential-mode 4D-MRI than that in cine-mode 4D-MRI. C 2016 American Association of Physicists in Medicine. [http://dx

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Four-Dimensional Magnetic Resonance Imaging Using Axial Body Area as Respiratory Surrogate: Initial Patient Results

Cited by 42 publications

References 19 publications

Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model

Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model

Four dimensional magnetic resonance imaging with retrospective k‐space reordering: A feasibility study

Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition

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