Constrained reconstructions for 4D intervention guidance.

Image-guided interventions are an increasingly important part of clinical minimally invasive procedures. However, up to now they cannot be performed under 4D (3D + time) guidance due to the exceedingly high x-ray dose. In this work we investigate the applicability of compressed sensing reconstructions for highly undersampled CT datasets combined with the incorporation of prior images in order to yield low dose 4D intervention guidance. We present a new reconstruction scheme prior image dynamic interventional CT (PrIDICT) that accounts for specific image features in intervention guidance and compare it to PICCS and ASD-POCS. The optimal parameters for the dose per projection and the numbers of projections per reconstruction are determined in phantom simulations and measurements. In vivo experiments in six pigs are performed in a cone-beam CT; measured doses are compared to current gold-standard intervention guidance represented by a clinical fluoroscopy system. Phantom studies show maximum image quality for identical overall doses in the range of 14 to 21 projections per reconstruction. In vivo studies reveal that interventional materials can be followed in 4D visualization and that PrIDICT, compared to PICCS and ASD-POCS, shows superior reconstruction results and fewer artifacts in the periphery with dose in the order of biplane fluoroscopy. These results suggest that 4D intervention guidance can be realized with today's flat detector and gantry systems using the herein presented reconstruction scheme.

[1]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[2]  Jin Sung Kim,et al.  Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT. , 2012, Medical physics.

[3]  H. Verkooijen,et al.  Real-Time 3D Fluoroscopy-Guided Large Core Needle Biopsy of Renal Masses: A Critical Early Evaluation According to the IDEAL Recommendations , 2012, CardioVascular and Interventional Radiology.

[4]  Lei Xing,et al.  Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints , 2012, Physics in medicine and biology.

[5]  Rebecca Fahrig,et al.  Dose and image quality for a cone-beam C-arm CT system. , 2006, Medical physics.

[6]  T. Struffert,et al.  Angiographic CT for Intraprocedural Monitoring of Complex Neuroendovascular Procedures , 2013, American Journal of Neuroradiology.

[7]  Jie Tang,et al.  Temporal resolution improvement in cardiac CT using PICCS (TRI-PICCS): performance studies. , 2010, Medical physics.

[8]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

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

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

[11]  Graeme C. Mc Kinnon,et al.  Towards Imaging the Beating Heart Usefully with a Conventional CT Scanner , 1981, IEEE Transactions on Biomedical Engineering.

[12]  A R Cowen,et al.  The design and imaging characteristics of dynamic, solid-state, flat-panel x-ray image detectors for digital fluoroscopy and fluorography. , 2008, Clinical radiology.

[13]  M. Kachelriess,et al.  Improved total variation-based CT image reconstruction applied to clinical data , 2011, Physics in medicine and biology.

[14]  S. Shim,et al.  Combined Fluoroscopy- and CT-Guided Transthoracic Needle Biopsy Using a C-Arm Cone-Beam CT System: Comparison with Fluoroscopy-Guided Biopsy , 2011, Korean journal of radiology.

[15]  Michael Grasruck,et al.  Ultra-high resolution flat-panel volume CT: fundamental principles, design architecture, and system characterization , 2006, European Radiology.

[16]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[17]  Guang-Hong Chen,et al.  Prior image constrained compressed sensing (PICCS) , 2008, SPIE BiOS.

[18]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[19]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[20]  Xiaochuan Pan,et al.  Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. , 2009, Medical physics.

[21]  Cyril Riddell,et al.  Compressed Sensing Based 3D Tomographic Reconstruction for Rotational Angiography , 2011, MICCAI.

[22]  Xiao Han,et al.  Optimization-based reconstruction of sparse images from few-view projections , 2012, Physics in medicine and biology.

[23]  J. Hornegger,et al.  Technical note: RabbitCT--an open platform for benchmarking 3D cone-beam reconstruction algorithms. , 2009, Medical physics.

[24]  Guang-Hong Chen,et al.  Time-Resolved Interventional Cardiac C-arm Cone-Beam CT: An Application of the PICCS Algorithm , 2012, IEEE Transactions on Medical Imaging.

[25]  W A Kalender,et al.  Metal Artifact Reduction for Clipping and Coiling in Interventional C-Arm CT , 2010, American Journal of Neuroradiology.