Sliding motion compensated low-rank plus sparse (SMC-LS) reconstruction for high spatiotemporal free-breathing liver 4D DCE-MRI.

Liver dynamic contrast-enhanced MRI (DCE-MRI) requires high spatiotemporal resolution and large field of view to clearly visualize all relevant enhancement phases and detect early-stage liver lesions. The low-rank plus sparse (L + S) reconstruction outperforms standard sparsity-only-based reconstruction through separation of low-rank background component (L) and sparse dynamic components (S). However, the L + S decomposition is sensitive to respiratory motion so that image quality is compromised when breathing occurs during long time data acquisition. To enable high quality reconstruction for free-breathing liver 4D DCE-MRI, this paper presents a novel method called SMC-LS, which incorporates Sliding Motion Compensation into the standard L + S reconstruction. The global superior-inferior displacement of the internal abdominal organs is inferred directly from the undersampled raw data and then used to correct the breathing induced sliding motion which is the dominant component of respiratory motion. With sliding motion compensation, the reconstructed temporal frames are roughly registered before applying the standard L + S decomposition. The proposed method has been validated using free-breathing liver 4D MRI phantom data, free-breathing liver 4D DCE-MRI phantom data, and in vivo free breathing liver 4D MRI dataset. Results demonstrated that SMC-LS reconstruction can effectively reduce motion blurring artefacts and preserve both spatial structures and temporal variations at a sub-second temporal frame rate for free-breathing whole-liver 4D DCE-MRI.

[1]  Randall K Ten Haken,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations in the liver: sensitivity to variations in motion. , 2003, Medical physics.

[2]  L Axel,et al.  Respiratory effects in two-dimensional Fourier transform MR imaging. , 1986, Radiology.

[3]  Huiqian Du,et al.  Compressed sensing MR image reconstruction using a motion-compensated reference. , 2012, Magnetic resonance imaging.

[4]  N. Codella,et al.  Respiratory and cardiac self‐gated free‐breathing cardiac CINE imaging with multiecho 3D hybrid radial SSFP acquisition , 2010, Magnetic resonance in medicine.

[5]  Michael Lustig,et al.  Fast pediatric 3D free‐breathing abdominal dynamic contrast enhanced MRI with high spatiotemporal resolution , 2015, Journal of magnetic resonance imaging : JMRI.

[6]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[7]  Feng Huang,et al.  k‐t GRAPPA: A k‐space implementation for dynamic MRI with high reduction factor , 2005, Magnetic resonance in medicine.

[8]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[9]  Zhi-Pei Liang,et al.  Improving k‐t SENSE by adaptive regularization , 2007, Magnetic resonance in medicine.

[10]  Yi Wang,et al.  Discontinuity Preserving Liver MR Registration With Three-Dimensional Active Contour Motion Segmentation , 2019, IEEE Transactions on Biomedical Engineering.

[11]  Yi Wang,et al.  Fast 3D contrast enhanced MRI of the liver using temporal resolution acceleration with constrained evolution reconstruction , 2013, Magnetic resonance in medicine.

[12]  Javad Alirezaie,et al.  Nonrigid motion compensation in compressed sensing reconstruction of cardiac cine MRI. , 2018, Magnetic resonance imaging.

[13]  Zhong Chen,et al.  Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI. , 2015, Magnetic resonance imaging.

[14]  Ganesh Adluru,et al.  Compressed sensing for rapid late gadolinium enhanced imaging of the left atrium: A preliminary study. , 2016, Magnetic resonance imaging.

[15]  Cengizhan Ozturk,et al.  Respiratory motion of the heart from free breathing coronary angiograms , 2004, IEEE Transactions on Medical Imaging.

[16]  David L Wilson,et al.  A simple application of compressed sensing to further accelerate partially parallel imaging. , 2013, Magnetic resonance imaging.

[17]  R Sinkus,et al.  CONTINUING EDUCATION PROGRAM : FOCUS . . . Can we justify not doing liver perfusion imaging in 2013 ? , 2013 .

[18]  Javad Alirezaie,et al.  Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI , 2018, Magnetic Resonance Materials in Physics, Biology and Medicine.

[19]  Jeffrey A. Fessler,et al.  Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging , 2016, IEEE Transactions on Medical Imaging.

[20]  G Johnson,et al.  Multiple breath‐hold averaging (mba) method for increased snr in abdominal mri , 1995, Magnetic resonance in medicine.

[21]  Feng Huang,et al.  Cardiac magnetic resonance imaging using radial k-space sampling and self-calibrated partial parallel reconstruction. , 2010, Magnetic resonance imaging.

[22]  Michael Elad,et al.  Patch based reconstruction of undersampled data (PROUD) for high signal‐to‐noise ratio and high frame rate contrast enhanced liver imaging , 2015, Magnetic resonance in medicine.

[23]  Hans-Ulrich Kauczor,et al.  Analysis of intrathoracic tumor mobility during whole breathing cycle by dynamic MRI. , 2004, International journal of radiation oncology, biology, physics.

[24]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[25]  Angshul Majumdar,et al.  Non-convex algorithm for sparse and low-rank recovery: application to dynamic MRI reconstruction. , 2013, Magnetic resonance imaging.

[26]  Daniel K Sodickson,et al.  Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components , 2015, Magnetic resonance in medicine.

[27]  R. Alfidi,et al.  The effect of motion on two-dimensional Fourier transformation magnetic resonance images. , 1984, Radiology.

[28]  Jong Chul Ye,et al.  Motion Adaptive Patch-Based Low-Rank Approach for Compressed Sensing Cardiac Cine MRI , 2014, IEEE Transactions on Medical Imaging.

[29]  Mathews Jacob,et al.  Deformation Corrected Compressed Sensing (DC-CS): A Novel Framework for Accelerated Dynamic MRI , 2014, IEEE Transactions on Medical Imaging.

[30]  A. Majumdar Improved dynamic MRI reconstruction by exploiting sparsity and rank-deficiency. , 2013, Magnetic resonance imaging.

[31]  Abdul Haseeb Ahmed,et al.  Motion correction based reconstruction method for compressively sampled cardiac MR imaging. , 2017, Magnetic resonance imaging.

[32]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[33]  M. Paling,et al.  Respiration artifacts in MR imaging: reduction by breath holding. , 1986, Journal of computer assisted tomography.

[34]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[35]  Sungheon Kim,et al.  Golden‐angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden‐angle radial sampling for fast and flexible dynamic volumetric MRI , 2014, Magnetic resonance in medicine.

[36]  E. Larsen,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations. , 1999, Medical physics.

[37]  Leon Axel,et al.  XD‐GRASP: Golden‐angle radial MRI with reconstruction of extra motion‐state dimensions using compressed sensing , 2016, Magnetic resonance in medicine.

[38]  Jong Chul Ye,et al.  k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI , 2009, Magnetic resonance in medicine.

[39]  Dwight G Nishimura,et al.  Robust self‐navigated body MRI using dense coil arrays , 2016, Magnetic resonance in medicine.