Iterative static field map estimation for off‐resonance correction in non‐Cartesian susceptibility weighted imaging

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Purpose Patient‐induced inhomogeneities in the static magnetic field cause distortions and blurring (off‐resonance artifacts) during acquisitions with long readouts such as in SWI. Conventional versatile correction methods based on extended Fourier models are too slow for clinical practice in computationally demanding cases such as 3D high‐resolution non‐Cartesian multi‐coil acquisitions. Theory Most reconstruction methods can be accelerated when performing off‐resonance correction by reducing the number of iterations, compressed coils, and correction components. Recent state‐of‐the‐art unrolled deep learning architectures could help but are generally not adapted to corrupted measurements as they rely on the standard Fourier operator in the data consistency term. The combination of correction models and neural networks is therefore necessary to reduce reconstruction times. Methods Hybrid pipelines using UNets were trained stack‐by‐stack over 99 SWI 3D SPARKLING 20‐fold accelerated acquisitions at 0.6 mm isotropic resolution using different off‐resonance correction methods. Target images were obtained using slow model‐based corrections based on self‐estimated normalΔB0$$ \Delta {B}_0 $$ field maps. The proposed strategies, tested over 11 volumes, are compared to model‐only and network‐only pipelines. Results The proposed hybrid pipelines achieved scores competing with two to three times slower baseline methods, and neural networks were observed to contribute both as pre‐conditioner and through inter‐iteration memory by allowing more degrees of freedom over the model design. Conclusion A combination of model‐based and network‐based off‐resonance correction was proposed to significantly accelerate conventional methods. Different promising synergies were observed between acceleration factors (iterations, coils, correction) and model/network that could be expanded in the future.

Section: For Details)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This technique can be applied to any imaging setup but considerably slows down (e.g., 15‐fold) the image reconstruction process. The mandatory

\Delta {B}_0

field map is directly available for multi‐echo acquisitions, 15 but it necessitates to be either externally collected by extending the scan time, or estimated 14,33–38 …”

Section: Introductionmentioning

confidence: 99%

“…More targeted literature invests considerable effort into estimating the

\Delta {B}_0

field map, 44,45 already available in the context of SWI acquisitions (see Ref. 14 for details).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning‐assisted model‐based off‐resonance correction for non‐Cartesian SWI

Daval-Frérot

Massire

Mailhé

et al. 2023

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Iterative static field map estimation for off‐resonance correction in non‐Cartesian susceptibility weighted imaging

Daval-Frérot

Massire

Mailhé

et al. 2022

Patient-induced inhomogeneities in the magnetic field cause distortions and blurring during acquisitions with long readouts such as in susceptibility-weighted imaging (SWI). Most correction methods require collecting an additional ΔB 0 field map to remove these artifacts. Theory:The static ΔB 0 field map can be approximated with an acceptable error directly from a single echo acquisition in SWI. The main component of the observed phase is linearly related to ΔB 0 and the echo time (TE), and the relative impact of non-ΔB 0 terms becomes insignificant with TE >20 ms at 3 T for a well-tuned system. Methods:The main step is to combine and unfold the multi-channel phase maps wrapped many times, and several competing algorithms are compared for this purpose. Four in vivo brain data sets collected using the recently proposed 3D spreading projection algorithm for rapid k-space sampling (SPARKLING) readouts are used to assess the proposed method. Results: The estimated 3D field maps generated with a 0.6 mm isotropic spatial resolution provide overall similar off-resonance corrections compared to reference corrections based on an external ΔB 0 acquisitions, and even improved for 2 of 4 individuals. Although a small estimation error is expected, no aftermath was observed in the proposed corrections, whereas degradations were observed in the references. Conclusion: A static ΔB 0 field map estimation method was proposed to take advantage of acquisitions with long echo times, and outperformed the reference technique based on an external field map. The difference can be attributed to an inherent robustness to mismatches between volumes and external ΔB 0 maps, and diverse other sources investigated.

Improving spreading projection algorithm for rapid k‐space sampling trajectories through minimized off‐resonance effects and gridding of low frequencies

Giliyar Radhakrishna,

Daval‐Frérot,

Massire

et al. 2023

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Non-Cartesian MRI with long arbitrary readout directions are susceptible to off-resonance artifacts due to patient induced B 0 inhomogeneities. This results in degraded image quality with strong signal losses and blurring.Current solutions to address this issue involve correcting the off-resonance artifacts during image reconstruction or reducing inhomogeneities through improved shimming.Theory: The recently developed SPARKLING algorithm is extended to drastically diminish off-resonance artifacts by generating temporally smooth k-space sampling patterns. For doing so, the cost function which is optimized in SPARKLING is modified using a temporal weighting factor. Additionally, oversampling of the center of k-space beyond the Nyquist criteria is prevented through the use of gridded sampling in the region, enforced with affine constraints.Methods: Prospective k-space data was acquired at 3 T on new trajectories, and we show robustness to B 0 inhomogeneities through in silico experiments by adding ΔB 0 through artificial degradation of system B 0 shimming. Later on, in vivo experiments were carried out to optimize parameters of the new improvements and benchmark the gain in performance. Results:The improved trajectories allowed for the recovery of signal dropouts observed on original SPARKLING acquisitions at larger B 0 field inhomogeneities. Furthermore, imposing gridded sampling at the center of k-space provided improved reconstructed image quality with limited artifacts. Conclusion:These advancements allowed us for nearly 4.62× shorter scan time compared to GRAPPA-p4x1, allowing us to reach 600 μm isotropic resolution in 3D T * 2 -w imaging in just 3.3 min at 3 T with negligible degradation in image quality.