Phantom-based evaluation of isotropic reconstruction of 4-D MRI volumes using super-resolution

This article investigates the feasibility of isotropic super-resolution reconstruction on 4-D (3-D + time) thoracic MRI data. 4-D MRI sequences generally have high temporal resolution to characterize dynamic phenomena but poor spatial resolution, creating highly anisotropic voxels elongated in the slice-select dimension. Isotropic post-acquisition reconstruction can be obtained using super-resolution algorithms. A new MRI compatible phantom design that simulates lung tumour motion is introduced to evaluate the feasibility and performance of the proposed super-resolution algorithm in the context of 4-D MRI. Several orthogonal low-resolution acquisitions of the phantom are performed through time using a fast true 3-D gradient echo based sequence. The acquired volumes are then registered and combined using a total-variation based regularizer super-resolution algorithm to obtain the high-resolution volume. The quality of the reconstruction is evaluated by measuring the mutual information between the reconstructed volume and a direct isotropic 3-D acquisition. Subjective and objective evaluations show the superiority of our approach compared to the averaging method. This article also discusses the influence of various parameters such as the number of low-resolution scans used and the influence of automatic motion estimation versus known displacement.

[1]  Steve B Jiang,et al.  Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients. , 2003, Physics in medicine and biology.

[2]  S Peled,et al.  Superresolution in MRI: Application to human white matter fiber tract visualization by diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[3]  Simon K. Warfield,et al.  Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions , 2012, Medical Image Anal..

[4]  Eric Van Reeth,et al.  Super-resolution in magnetic resonance imaging: A review , 2012 .

[5]  Chueh Loo Poh,et al.  Visualization of Lung using 4D Magnetic Resonance Imaging , 2011 .

[6]  D R McKenzie,et al.  Dynamic modeling of lung tumor motion during respiration , 2011, Physics in medicine and biology.

[7]  Ralf Tetzlaff,et al.  4D-MRI analysis of lung tumor motion in patients with hemidiaphragmatic paralysis. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[8]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[9]  Colin Studholme,et al.  A Novel Approach to High Resolution Fetal Brain MR Imaging , 2005, MICCAI.

[10]  Klaus Scheffler Superresolution in MRI? , 2002, Magnetic resonance in medicine.

[11]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.

[12]  Hayit Greenspan,et al.  Super-Resolution in Medical Imaging , 2009, Comput. J..

[13]  Viktor Vegh,et al.  MRI demodulation frequency changes provide different information , 2011 .

[14]  Gregory S Mayer,et al.  Measuring information gain for frequency-encoded super-resolution MRI. , 2007, Magnetic resonance imaging.

[15]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[16]  Ying Bai,et al.  Super-resolution reconstruction for tongue MR images , 2012, Medical Imaging.

[17]  Daniel Rueckert,et al.  Diffusion tensor imaging (DTI) of the brain in moving subjects: Application to in‐utero fetal and ex‐utero studies , 2009, Magnetic resonance in medicine.

[18]  A. Webb,et al.  Introduction to biomedical imaging , 2002 .