A novel phase-unwrapping method based on pixel clustering and local surface fitting with application to Dixon water-fat MRI

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping

Background

Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline.

Methods

In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods.

Results

Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view.

Conclusion

The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.

Reducing the ambiguity of field inhomogeneity and chemical shift effect for fat-water separation by field factor

PURPOSE

To reduce the ambiguity between chemical shift and field inhomogeneity with flexible TE combinations by introducing a variable (field factor).

THEORY AND METHODS

The ambiguity between chemical shift and field inhomogeneity can be eliminated directly from the multiple in-phase images acquired at different TEs; however, it is only applicable to few echo combinations. In this study, we accommodated such an implementation in flexible TE combinations by introducing a new variable (field factor). The effects of the chemical shift were removed from the field inhomogeneity in the candidate solutions, thus reducing the ambiguity problem. To validate this concept, multi-echo MRI data acquired from various anatomies with different imaging parameters were tested. The derived fat and water images were compared with those of the state-of-the-art fat-water separation algorithms.

RESULTS

Robust fat-water separation was achieved with the accurate solution of field inhomogeneity, and no apparent fat-water swap was observed. In addition to the good performance, the proposed method is applicable to various fat-water separation applications, including different sequence types and flexible TE choices.

CONCLUSION

We propose an algorithm to reduce the ambiguity of chemical shift and field inhomogeneity and achieved robust fat-water separation in various applications.

The PHU-NET: A robust phase unwrapping method for MRI based on deep learning

PURPOSE

This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods.

METHODS

A deep learning network called PHU-NET was designed for MR phase unwrapping. In this network, a novel training data generation method was proposed to simulate the wrapping patterns in MR phase images. The wrapping boundary and wrapping counts were explicitly estimated and used for network training. The proposed method was quantitatively evaluated and compared to other methods using a number of simulated datasets with varying signal-to-noise ratio (SNR) and MR phase images from various parts of the human body.

RESULTS

The results showed that our method performed better in the simulated data even under an extremely low SNR. The proposed method had less residual wrapping in the images from various parts of human body and worked well in the presence of severe anatomical discontinuity. Our method was also advantageous in terms of computational efficiency compared to the traditional methods.

CONCLUSION

This work proposed a robust and computationally efficient MR phase unwrapping method based on a deep learning network, which has promising performance in applications using MR phase information.

Clinical workflow for treating patients with a metallic hip prosthesis using magnetic resonance imaging-guided radiotherapy

Background & purpose

Metallic prostheses distort the magnetic field during magnetic resonance imaging (MRI), leading to geometric distortions and signal loss. The purpose of this work was to develop a method to determine eligibility for MRI-guided radiotherapy (MRIgRT) on a per patient basis by estimating the magnitude of geometric distortions inside the clinical target volume (CTV).

Materials & methods

Three patients with prostate cancer and hip prosthesis, treated using MRIgRT, were included. Eligibility for MRIgRT was based on computed tomography and associated CTV delineations, together with a field-distortion (B0) map and anatomical images acquired during MR simulation. To verify the method, B0 maps made during MR simulation and each MRIgRT treatment fraction were compared.

Results

Estimates made during MR simulation of the magnitude of distortions inside the CTV were 0.43 mm, 0.19 mm and 2.79 mm compared to the average over all treatment fractions of 1.40 mm, 0.32 mm and 1.81 mm, per patient respectively.

Conclusions

B0 map acquisitions prior to treatment can be used to estimate the magnitude of distortions during MRIgRT to guide the decision on eligibility for MRIgRT of prostate cancer patients with metallic hip implants.

A Dual-VENC Four-Dimensional Flow MRI Framework for Analysis of Subject-Specific Heterogeneous Nonlinear Vessel Deformation

Advancement of subject-specific in silico medicine requires new imaging protocols tailored to specific anatomical features, paired with new constitutive model development based on structure/function relationships. In this study, we develop a new dual-velocity encoding coefficient (VENC) 4D flow MRI protocol that provides unprecedented spatial and temporal resolution of in vivo aortic deformation. All previous dual-VENC 4D flow MRI studies in the literature focus on an isolated segment of the aorta, which fail to capture the full spectrum of aortic heterogeneity that exists along the vessel length. The imaging protocol developed provides high sensitivity to all blood flow velocities throughout the entire cardiac cycle, overcoming the challenge of accurately measuring the highly unsteady nonuniform flow field in the aorta. Cross-sectional area change, volumetric flow rate, and compliance are observed to decrease with distance from the heart, while pulse wave velocity (PWV) is observed to increase. A nonlinear aortic lumen pressure-area relationship is observed throughout the aorta such that a high vessel compliance occurs during diastole, and a low vessel compliance occurs during systole. This suggests that a single value of compliance may not accurately represent vessel behavior during a cardiac cycle in vivo. This high-resolution MRI data provide key information on the spatial variation in nonlinear aortic compliance, which can significantly advance the state-of-the-art of in-silico diagnostic techniques for the human aorta.

Seed prioritized unwrapping (SPUN) for MR phase imaging

BACKGROUND

Region-growing-based phase unwrapping methods have the potential for lossless phase aliasing removal, but generally suffer from unwrapping error propagation associated with discontinuous phase and/or long calculation times. The tradeoff point between robustness and efficiency of phase unwrapping methods in the region-growing category requires improvement.

PURPOSE

To demonstrate an accurate, robust, and efficient region-growing phase unwrapping method for MR phase imaging applications.

STUDY TYPE

Prospective. SUBJECTS, PHANTOM: normal human subjects (10) / brain surgery patients (2) / water phantoms / computer simulation.

FIELD STRENGTH/SEQUENCE

3 T/gradient echo sequences (2D and 3D).

ASSESSMENT

A seed prioritized unwrapping (SPUN) method was developed based on single-region growing, prioritizing only a portion (eg, 100 seeds or 1% seeds) of available seed voxels based on continuity quality during each region-growing iteration. Computer simulation, phantom, and in vivo brain and pelvis scans were performed. The error rates, seed percentages, and calculation times were recorded and reported. SPUN unwrapped phase images were visually evaluated and compared with Laplacian unwrapped results.

STATISTICAL TESTS

Monte Carlo simulation was performed on a 3D dipole phase model with a signal-to-noise ratio (SNR) of 1-9 dB, to obtain the mean and standard deviation of calculation error rates and calculation times.

RESULTS

Simulation revealed a very robust unwrapping performance of SPUN, reaching an error rate of <0.4% even with SNR as low as 1 dB. For all in vivo data, SPUN was able to robustly unwrap the phase images of modest SNR and complex morphology with visually minimal errors and fast calculation speed (eg, <4 min for 368 × 312 × 128 data) when using a proper seed priority number, eg, N_sp = 1 or 10 voxels for 2D and N_sp = 1% for 3D data.

DATA CONCLUSION

SPUN offers very robust and fast region-growing-based phase unwrapping, and does not require any tissue masking or segmentation, nor poses a limitation over imaging parameters.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:62-70.