Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.

PURPOSE To investigate the feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. METHODS Two experiments were designed to investigate the feasibility of using reference image based compressed sensing (RICS) technique in DCE-MRI of the breast. The first experiment examined the capability of RICS to faithfully reconstruct uptake curves using undersampled data sets extracted from fully sampled clinical breast DCE-MRI data. An average approach and an approach using motion estimation and motion compensation (ME/MC) were implemented to obtain reference images and to evaluate their efficacy in reducing motion related effects. The second experiment, an in vitro phantom study, tested the feasibility of RICS for improving temporal resolution without degrading the spatial resolution. RESULTS For the uptake-curve reconstruction experiment, there was a high correlation between uptake curves reconstructed from fully sampled data by Fourier transform and from undersampled data by RICS, indicating high similarity between them. The mean Pearson correlation coefficients for RICS with the ME/MC approach and RICS with the average approach were 0.977 +/- 0.023 and 0.953 +/- 0.031, respectively. The comparisons of final reconstruction results between RICS with the average approach and RICS with the ME/MC approach suggested that the latter was superior to the former in reducing motion related effects. For the in vitro experiment, compared to the fully sampled method, RICS improved the temporal resolution by an acceleration factor of 10 without degrading the spatial resolution. CONCLUSIONS The preliminary study demonstrates the feasibility of RICS for faithfully reconstructing uptake curves and improving temporal resolution of breast DCE-MRI without degrading the spatial resolution.

[1]  Michael Lustig,et al.  k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity , 2006 .

[2]  B. Rosen,et al.  Perfusion imaging with NMR contrast agents , 1990, Magnetic resonance in medicine.

[3]  P. Carmeliet,et al.  Angiogenesis in cancer and other diseases , 2000, Nature.

[4]  Suyash P. Awate,et al.  Temporally constrained reconstruction of dynamic cardiac perfusion MRI , 2007, Magnetic resonance in medicine.

[5]  Anil K. Jain,et al.  Displacement Measurement and Its Application in Interframe Image Coding , 1981, IEEE Trans. Commun..

[6]  J. Folkman.  Angiogenesis in cancer, vascular, rheumatoid and other disease , 1995, Nature Medicine.

[7]  Mark A Horsfield,et al.  Algorithms for calculation of kinetic parameters from T1‐weighted dynamic contrast‐enhanced magnetic resonance imaging , 2004, Journal of magnetic resonance imaging : JMRI.

[8]  A J Sederman,et al.  Quantitative single point imaging with compressed sensing. , 2009, Journal of magnetic resonance.

[9]  P. Tofts,et al.  Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts , 1991, Magnetic resonance in medicine.

[10]  A. Padhani.  Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions , 2002, Journal of magnetic resonance imaging : JMRI.

[11]  Srirama V Swaminathan,et al.  SENSE imaging of the breast. , 2005, AJR. American journal of roentgenology.

[12]  Brian A Hargreaves,et al.  Accelerated bilateral dynamic contrast‐enhanced 3D spiral breast MRI using TSENSE , 2008, Journal of magnetic resonance imaging : JMRI.

[13]  Jong Chul Ye,et al.  Improved k–t BLAST and k–t SENSE using FOCUSS , 2007, Physics in medicine and biology.

[14]  Chen Zhao,et al.  Compressed sensing parallel Magnetic Resonance Imaging , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Peter M. Jakob,et al.  Accelerated Dynamic Imaging by Reconstructing Sparse Differences using Compressed Sensing , 2008 .

[16]  Anne L. Martel,et al.  Removing undersampling artifacts in DCE‐MRI studies using independent components analysis , 2008, Magnetic resonance in medicine.

[17]  Dong Liang,et al.  Accelerating sensitivity encoding using Compressed Sensing , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[19]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[20]  O Henriksen,et al.  Quantitation of blood‐brain barrier defect by magnetic resonance imaging and gadolinium‐DTPA in patients with multiple sclerosis and brain tumors , 1990, Magnetic resonance in medicine.

[21]  Tristan Barrett,et al.  MRI of tumor angiogenesis , 2007, Journal of magnetic resonance imaging : JMRI.

[22]  Hans H Schild,et al.  Dynamic bilateral contrast-enhanced MR imaging of the breast: trade-off between spatial and temporal resolution. , 2005, Radiology.

[23]  Jong Chul Ye,et al.  Radial k‐t FOCUSS for high‐resolution cardiac cine MRI , 2010, Magnetic resonance in medicine.

[24]  Didier Le Gall,et al.  MPEG: a video compression standard for multimedia applications , 1991, CACM.

[25]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[26]  Sean B Fain,et al.  Iterative projection reconstruction of time‐resolved images using highly‐constrained back‐projection (HYPR) , 2008, Magnetic resonance in medicine.

[27]  L. Ying,et al.  Accelerating SENSE using compressed sensing , 2009, Magnetic resonance in medicine.

[28]  J. Folkman.  Role of angiogenesis in tumor growth and metastasis. , 2002, Seminars in oncology.

[29]  Michael H Lev,et al.  Dynamic magnetic resonance perfusion imaging of brain tumors. , 2004, The oncologist.

[30]  Charles A Mistretta,et al.  Undersampled radial MR acquisition and highly constrained back projection (HYPR) reconstruction: Potential medical imaging applications in the post‐Nyquist era , 2009, Journal of magnetic resonance imaging : JMRI.

[31]  Raymond C Boston,et al.  High frame‐rate simultaneous bilateral breast DCE‐MRI , 2007, Magnetic resonance in medicine.

[32]  Albert P. Chen,et al.  Compressed sensing for resolution enhancement of hyperpolarized 13C flyback 3D-MRSI. , 2008, Journal of magnetic resonance.

[33]  D. Ribatti,et al.  The discovery of angiogenic factors: a historical review. , 2000, General pharmacology.

[34]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[35]  A. Bilgin,et al.  Three-Dimensional Compressed Sensing for Dynamic MRI , 2007 .

[36]  Mostafa Atri,et al.  New technologies and directed agents for applications of cancer imaging. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  Jeffrey L Duerk,et al.  Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI , 2003, Journal of magnetic resonance imaging : JMRI.

[38]  J. Gore,et al.  Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. , 2005, Magnetic resonance imaging.

[39]  Thomas E Yankeelov,et al.  Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples. , 2007, Current medical imaging reviews.

[40]  Mitchell D Schnall,et al.  Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Assessing Tumor Vascularity and Vascular Effects of Targeted Therapies in Renal Cell Carcinoma , 2007, Clinical Cancer Research.

[41]  S. Sourbron.  Technical aspects of MR perfusion. , 2010, European journal of radiology.

[42]  Taehoon Shin,et al.  Systolic 3D first‐pass myocardial perfusion MRI: Comparison with diastolic imaging in healthy subjects , 2010, Magnetic resonance in medicine.

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

[44]  C K Kuhl,et al.  Breast neoplasms: T2* susceptibility-contrast, first-pass perfusion MR imaging. , 1997, Radiology.

[45]  Peter Boesiger,et al.  Compressed sensing in dynamic MRI , 2008, Magnetic resonance in medicine.

[46]  K. Thomas,et al.  Dynamic contrast-enhanced MRI measurements of passive permeability through blood retinal barrier in diabetic rats. , 2004, Investigative ophthalmology & visual science.

[47]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[48]  Tao Lang,et al.  Dynamic MRI with compressed sensing imaging using temporal correlations , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[49]  Charles H Cunningham,et al.  Temporal stability of adaptive 3D radial MRI using multidimensional golden means , 2009, Magnetic resonance in medicine.