Synopsis

Respiratory motion during MRI scan causes inconsistencies in the acquired k-space data providing strong blurring artifacts in the reconstructed images. In this work, a new method ( respiratory motion corrected GROG followed by L+S reconstruction for free breathing Golden-Angle Radial DCE-MRI) is presented.The proposed method is tested on 3-T free-breathing Golden angle radial DCE liver MRI data. The proposed method is compared with the conventional L+S reconstruction model. The proposed method provides 90% improvement in Artefact Power and 42% in RMSE as compared to conventional L+S reconstruction at acceleration factor 8.

Introduction:

In free breathing dynamic MRI,k-space data is acquired in different respiratory positions, which results in strong blurring artefacts in the reconstructed images ¹.Simplest approach to avoid respiratory motion is: (i) breath-hold data acquisition (ii)use of navigator signals that increases the patient discomfort and prolongs scan time ^2,3. Low-Rank plus Sparse (L+S) reconstruction with effective separation of the background and dynamic components proposed by Otazo et.al. to reconstruct the unalised dynamic MR images ⁴.The L+S technique combines the idea of parallel MRI(pMRI),Compressed sensing(CS) and Principal Component Analysis(PCA) to reconstruct under-sampled MR images. L+S reconstruction is mathematically formulated as ⁴:

$$ min _L,S 1/2 ||G.E (L+S) -y||₂ + λ_L||L||_* +λ_s||S||₁ $$

where y is the under-sampled k-space data acquired from the MRI scanner, T is the temporal Total-Variation operator, E is the multi coil Encoding operator, λ_L and λ_s are the regularization parameters.G represents the gridding operation which converts non-Cartesian data onto Cartesian grid. Conventional L+S uses Fessler gridding to convert the radial data to a Cartesian grid ⁵.

In the proposed method, Golden-angle radial liver perfusion data is first sorted into time frames. Motion signal is extracted by using self-navigator properties of this Golden angle radial data ⁶. This data is gridded onto a Cartesian grid using SC GROG gridding ^7,8.The resulting gridded data contains incoherent artifacts that are removed using L+S reconstruction.

Methods:

Results and Discussions:

The proposed method is tested on free-breathing Golden angle radial DCE data acquired with 3-T Siemens scanner at New York University ².The data acquisition parameters are: 512 readouts, 1144 radial spokes and 12 receiver coils. In order to generate a dynamic series, 96 adjacent spokes are grouped into one time point, these frames are further divided into 4 respiratory states. This data is gridded onto a Cartesian grid by using SC-GROG, corresponding Cartesian data size is : .The Nyquist sampling rate for this case is 512×π/2 =800,corresponding simulated Acceleration factor (AF) is 8.3 ². Figure 2 shows the reconstruction results. Left, middle and right hepatic veins are not clearly visible in the conventional L+S reconstruction (Figure 2a), while they can be clearly seen in the reconstruction results of the proposed method(Figure 2b).

AP and RMSE values of the reconstructed images for both the standard L+S and proposed methods are given in Table 1. The results show that the proposed method by incorporating motion correction frame work in L+S reconstruction outperforms conventional L+S method. For example there is 90% improvement in AP and 42% improvement in RMSE in the reconstruction results of the proposed method for Golden angle radial data at AF=8.

Conclusion:

Acknowledgements

References

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