Investigation of the XCAT phantom as a validation tool in cardiac MRI tracking algorithms.

PURPOSE To describe our magnetic resonance imaging (MRI) simulated implementation of the 4D digital extended cardio torso (XCAT) phantom to validate our previously developed cardiac tracking techniques. Real-time tracking will play an important role in the non-invasive treatment of atrial fibrillation with MRI-guided radiosurgery. In addition, to show how quantifiable measures of tracking accuracy and patient-specific physiology could influence MRI tracking algorithm design. METHODS Twenty virtual patients were subjected to simulated MRI scans that closely model the proposed real-world scenario to allow verification of the tracking technique's algorithm. The generated phantoms provide ground-truth motions which were compared to the target motions output from our tracking algorithm. The patient-specific tracking error, ep, was the 3D difference (vector length) between the ground-truth and algorithm trajectories. The tracking errors of two combinations of new tracking algorithm functions that were anticipated to improve tracking accuracy were studied. Additionally, the correlation of key physiological parameters with tracking accuracy was investigated. RESULTS Our original cardiac tracking algorithm resulted in a mean tracking error of 3.7 ± 0.6 mm over all virtual patients. The two combinations of tracking functions demonstrated comparable mean tracking errors however indicating that the optimal tracking algorithm may be patient-specific. CONCLUSIONS Current and future MRI tracking strategies are likely to benefit from this virtual validation method since no time-resolved 4D ground-truth signal can currently be derived from purely image-based studies.

[1]  G Baroni,et al.  Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI , 2016, Physics in medicine and biology.

[2]  Jan J W Lagendijk,et al.  The magnetic resonance imaging-linac system. , 2014, Seminars in radiation oncology.

[3]  W P Segars,et al.  Realistic CT simulation using the 4D XCAT phantom. , 2008, Medical physics.

[4]  Peter Kleine,et al.  Treatment Planning Considerations for Robotic Guided Cardiac Radiosurgery for Atrial Fibrillation , 2016, Cureus.

[5]  Frederik Maes,et al.  Changes in Left Atrial Anatomy Due to Respiration: Impact on Three‐Dimensional Image Integration During Atrial Fibrillation Ablation , 2008, Journal of cardiovascular electrophysiology.

[6]  Xiutao Shi,et al.  Evaluation of template matching for tumor motion management with cine-MR images in lung cancer patients. , 2014, Medical physics.

[7]  Steffen Ringgaard,et al.  Three-dimensional liver motion tracking using real-time two-dimensional MRI. , 2014, Medical physics.

[8]  Erik Tryggestad,et al.  4D tumor centroid tracking using orthogonal 2D dynamic MRI: implications for radiotherapy planning. , 2013, Medical physics.

[9]  Jens Frahm,et al.  Real‐time MRI at a resolution of 20 ms , 2010, NMR in biomedicine.

[10]  Paul Keall,et al.  The impact of breathing guidance and prospective gating during thoracic 4DCT imaging: an XCAT study utilizing lung cancer patient motion , 2016, Physics in medicine and biology.

[11]  K. Camphausen,et al.  Advances in 4D Medical Imaging and 4D Radiation Therapy , 2008, Technology in cancer research & treatment.

[12]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[13]  P B Hoffer,et al.  Computerized three-dimensional segmented human anatomy. , 1994, Medical physics.

[14]  Hiroki Shirato,et al.  Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. , 2013, Radiology.

[15]  P J Keall,et al.  Towards real-time MRI-guided 3D localization of deforming targets for non-invasive cardiac radiosurgery , 2016, Physics in medicine and biology.

[16]  Fang-Fang Yin,et al.  Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition. , 2015, Medical physics.

[17]  David Atkinson,et al.  A study of the motion and deformation of the heart due to respiration , 2002, IEEE Transactions on Medical Imaging.

[18]  Satyapal Rathee,et al.  Evaluation of a lung tumor autocontouring algorithm for intrafractional tumor tracking using low-field MRI: a phantom study. , 2012, Medical physics.

[19]  Stuart Crozier,et al.  The Australian magnetic resonance imaging-linac program. , 2014, Seminars in radiation oncology.

[20]  Stefan Neubauer,et al.  Normal human left and right ventricular and left atrial dimensions using steady state free precession magnetic resonance imaging. , 2005, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[21]  Indrin J Chetty,et al.  Analysis of deformable image registration accuracy using computational modeling. , 2010, Medical physics.

[22]  Alexander I. Veress,et al.  Incorporation of a Left Ventricle Finite Element Model Defining Infarction Into the XCAT Imaging Phantom , 2011, IEEE Transactions on Medical Imaging.

[23]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[24]  Steve B. Jiang,et al.  MRI-guided tumor tracking in lung cancer radiotherapy , 2011, Physics in medicine and biology.

[25]  Fang-Fang Yin,et al.  Four-dimensional magnetic resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study. , 2011, Medical physics.

[26]  Satyapal Rathee,et al.  First demonstration of intrafractional tumor-tracked irradiation using 2D phantom MR images on a prototype linac-MR. , 2013, Medical physics.

[27]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[28]  K. Barrett,et al.  Ganong's Review of Medical Physiology , 2010 .

[29]  Alejandro F Frangi,et al.  Realistic simulation of cardiac magnetic resonance studies modeling anatomical variability, trabeculae, and papillary muscles , 2011, Magnetic resonance in medicine.

[30]  Jing Cai,et al.  Establishing a framework to implement 4D XCAT phantom for 4D radiotherapy research. , 2012, Journal of cancer research and therapeutics.

[31]  Marco Riboldi,et al.  Magnetic resonance imaging-guided versus surrogate-based motion tracking in liver radiation therapy: a prospective comparative study. , 2015, International journal of radiation oncology, biology, physics.

[32]  Rasmus Larsen,et al.  Three-dimensional MRI-linac intra-fraction guidance using multiple orthogonal cine-MRI planes , 2013, Physics in medicine and biology.

[33]  Bjorn Stemkens,et al.  Image-driven, model-based 3D abdominal motion estimation for MR-guided radiotherapy , 2016, Physics in medicine and biology.

[34]  Fang-Fang Yin,et al.  A Technique for Generating Volumetric Cine-Magnetic Resonance Imaging. , 2016, International journal of radiation oncology, biology, physics.

[35]  Jens Frahm,et al.  Real‐time phase‐contrast MRI of cardiovascular blood flow using undersampled radial fast low‐angle shot and nonlinear inverse reconstruction , 2012, NMR in biomedicine.

[36]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[37]  Jens Frahm,et al.  Real-time cardiovascular magnetic resonance at 1.5 T using balanced SSFP and 40 ms resolution , 2013, Journal of Cardiovascular Magnetic Resonance.

[38]  Simon Stewart,et al.  Atrial fibrillation: profile and burden of an evolving epidemic in the 21st century. , 2013, International journal of cardiology.

[39]  Paul Keall,et al.  Quantification of lung tumor rotation with automated landmark extraction using orthogonal cine MRI images , 2015, Physics in medicine and biology.

[40]  Jens Frahm,et al.  Real-time cardiac phase contrast MRI blood flow including Valsalva and Mueller maneuver. Initial experiences , 2013, Journal of Cardiovascular Magnetic Resonance.

[41]  P J Keall,et al.  Radiotherapy beyond cancer: target localization in real-time MRI and treatment planning for cardiac radiosurgery. , 2014, Medical physics.