Image‐based retrospective 4D MRI in external beam radiotherapy: A comparative study with a digital phantom

Purpose Several image‐based retrospective sorting methods of 4D magnetic resonance imaging (4D MRI) have been proposed for respiratory motion reconstruction in external beam radiotherapy. However, the optimal strategy for providing accurate and artifact‐free 4D MRI, ideally corresponding to an average breathing cycle, is not yet defined. This study presents a proactive comparison of three published image‐based sorting methods, to define a groundwork for benchmarking in 4D MRI. Methods Three published 4D MRI methods were selected for image retrospective sorting: body area, mutual information, and navigator slice. The three image‐based methods were compared against a conventional retrospective sorting method based on an external surrogate. Comparisons were performed by means of an MRI digital phantom, derived from the XCAT CT phantom generated with different patient‐derived signals, for a total of 12 cases. Specific multislice MRI acquisitions were simulated for slice sorting and sagittal, coronal, and axial orientations were tested. An average 4D cycle was generated as ground truth. Results Individual and grouped patient analyses showed better performance of the navigator slice and mutual information in amplitude binning with respect to the body area strategy. Binning artifacts were reduced on the diaphragm with the slice navigator method due to the acquired internal information. Tumor motion description accurately matched the ground truth in the mutual information strategy with amplitude binning. The body area method followed the performance of the external surrogate and presented larger errors, since was not correlated with the internal anatomy. Sagittal and coronal orientations reported lower errors than axial slicing. Individual analysis showed the need of a patient‐specific evaluation for the selection of the best method. Conclusions A comparison between three different image‐based retrospective sorting methods for 4D MRI is proposed, providing guidelines for benchmark definition in MRI‐guided radiotherapy.

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

[2]  Chiara Gianoli,et al.  A tool for validating MRI-guided strategies: a digital breathing CT/MRI phantom of the abdominal site , 2017, Medical & Biological Engineering & Computing.

[3]  Rosalind Perrin,et al.  Four-Dimensional Dose Reconstruction for Scanned Proton Therapy Using Liver 4DCT-MRI. , 2016, International journal of radiation oncology, biology, physics.

[4]  P Segars,et al.  TH-EF-BRA-10: High Spatiotemporal Resolution Self-Sorted 4D MRI. , 2016, Medical physics.

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

[6]  Nassir Navab,et al.  Manifold learning for image-based breathing gating in ultrasound and MRI , 2012, Medical Image Anal..

[7]  Hans-Ulrich Kauczor,et al.  Estimation of Pulmonary Motion in Healthy Subjects and Patients with Intrathoracic Tumors Using 3D-Dynamic MRI: Initial Results , 2009, Korean journal of radiology.

[8]  Debiao Li,et al.  Four‐dimensional MRI using three‐dimensional radial sampling with respiratory self‐gating to characterize temporal phase‐resolved respiratory motion in the abdomen , 2016, Magnetic resonance in medicine.

[9]  R. Mohan,et al.  Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. , 2003, Physics in medicine and biology.

[10]  Y D Mutaf,et al.  The impact of temporal inaccuracies on 4DCT image quality. , 2007, Medical physics.

[11]  Fang-Fang Yin,et al.  T2-weighted four dimensional magnetic resonance imaging with result-driven phase sorting. , 2015, Medical physics.

[12]  Fang-Fang Yin,et al.  Four dimensional magnetic resonance imaging with retrospective k-space reordering: a feasibility study. , 2015, Medical physics.

[13]  Fang-Fang Yin,et al.  Investigation of sagittal image acquisition for 4D-MRI with body area as respiratory surrogate. , 2014, Medical physics.

[14]  Erik Tryggestad,et al.  Respiration-based sorting of dynamic MRI to derive representative 4D-MRI for radiotherapy planning. , 2013, Medical physics.

[15]  Marco Riboldi,et al.  Real-time tumour tracking in particle therapy: technological developments and future perspectives. , 2012, The Lancet. Oncology.

[16]  Zarko Celicanin,et al.  Simultaneous acquisition of image and navigator slices using CAIPIRINHA for 4D MRI , 2015, Magnetic resonance in medicine.

[17]  Paul Keall,et al.  Audiovisual Biofeedback Improves Cine-Magnetic Resonance Imaging Measured Lung Tumor Motion Consistency. , 2016, International journal of radiation oncology, biology, physics.

[18]  Joseph O Deasy,et al.  Direct Comparison of Respiration-Correlated Four-Dimensional Magnetic Resonance Imaging Reconstructed Using Concurrent Internal Navigator and External Bellows. , 2017, International journal of radiation oncology, biology, physics.

[19]  Geoffrey D. Hugo,et al.  Advances in 4D radiation therapy for managing respiration: part II - 4D treatment planning. , 2012, Zeitschrift fur medizinische Physik.

[20]  Geoffrey D. Hugo,et al.  Advances in 4D radiation therapy for managing respiration: part I - 4D imaging. , 2012, Zeitschrift fur medizinische Physik.

[21]  Milan Sonka,et al.  Characterization and identification of spatial artifacts during 4D-CT imaging. , 2011, Medical physics.

[22]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.

[23]  Chiara Gianoli,et al.  MRI quantification of pancreas motion as a function of patient setup for particle therapy -a preliminary study. , 2016, Journal of applied clinical medical physics.

[24]  P. Keall 4-dimensional computed tomography imaging and treatment planning. , 2004, Seminars in radiation oncology.

[25]  Martin J Murphy,et al.  Tracking moving organs in real time. , 2004, Seminars in radiation oncology.

[26]  Ken Masamune,et al.  Adaptive 4D MR imaging using navigator‐based respiratory signal for MRI‐guided therapy , 2008, Magnetic resonance in medicine.

[27]  Marco Riboldi,et al.  Liver 4DMRI: A retrospective image-based sorting method. , 2015, Medical physics.

[28]  Christoph Bert,et al.  Respiratory motion management in particle therapy. , 2010, Medical physics.

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

[30]  M. Herk Errors and margins in radiotherapy. , 2004 .

[31]  P Boesiger,et al.  4D MR imaging of respiratory organ motion and its variability , 2007, Physics in medicine and biology.

[32]  B. Fallone,et al.  The rotating biplanar linac-magnetic resonance imaging system. , 2014, Seminars in radiation oncology.

[33]  Fang-Fang Yin,et al.  Four-dimensional magnetic resonance imaging using axial body area as respiratory surrogate: initial patient results. , 2014, International Journal of Radiation Oncology, Biology, Physics.

[34]  Sasa Mutic,et al.  The ViewRay system: magnetic resonance-guided and controlled radiotherapy. , 2014, Seminars in radiation oncology.

[35]  Jürgen Biederer,et al.  Magnetic resonance imaging and computed tomography of respiratory mechanics. , 2010, Journal of magnetic resonance imaging : JMRI.

[36]  R. Mohan,et al.  Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker. , 2003, Medical physics.

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

[38]  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.

[39]  Benedick A Fraass,et al.  Four-Dimensional Magnetic Resonance Imaging With 3-Dimensional Radial Sampling and Self-Gating-Based K-Space Sorting: Early Clinical Experience on Pancreatic Cancer Patients. , 2015, International journal of radiation oncology, biology, physics.

[40]  Ryan G. Price,et al.  Impact of incorporating visual biofeedback in 4D MRI , 2016, Journal of applied clinical medical physics.