A retrospective 4D‐MRI based on 2D diaphragm profiles for lung cancer patients

4D‐MRI, compared to 4D‐CT, provides better soft‐tissue contrast for target delineation. However, motion artefacts are often observed due to residual breathing variations. This study is to present a retrospective 4D‐MRI reconstruction method based on 2D diaphragm profiles to improve the quality of 4D‐MR images in the presence of significant breathing variations.

[1]  Bjorn Stemkens,et al.  Optimizing 4-dimensional magnetic resonance imaging data sampling for respiratory motion analysis of pancreatic tumors. , 2015, International journal of radiation oncology, biology, physics.

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

[3]  George Starkschall,et al.  Displacement-based binning of time-dependent computed tomography image data sets. , 2005, Medical physics.

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

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

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

[7]  H. Shiomi,et al.  Evaluation of potential internal target volume of liver tumors using cine-MRI. , 2014, Medical physics.

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

[9]  Sasa Mutic,et al.  Respiratory amplitude guided 4-dimensional magnetic resonance imaging. , 2013, International journal of radiation oncology, biology, physics.

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

[11]  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.

[12]  Mark Rosen,et al.  Comparative assessment of liver tumor motion using cine-magnetic resonance imaging versus 4-dimensional computed tomography. , 2015, International journal of radiation oncology, biology, physics.

[13]  Tinsu Pan,et al.  Phase and amplitude binning for 4D-CT imaging. , 2007 .

[14]  Paul Keall,et al.  Impact of the MLC on the MRI field distortion of a prototype MRI-linac. , 2013, Medical physics.

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

[16]  Paul J Keall,et al.  The first clinical implementation of electromagnetic transponder-guided MLC tracking. , 2014, Medical physics.

[17]  Paul J Keall,et al.  Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients. , 2008, International journal of radiation oncology, biology, physics.

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

[19]  Impact of incorporating visual biofeedback in 4D MRI. , 2016, Journal of applied clinical medical physics.

[20]  Peter Balter,et al.  Implementation of feedback-guided voluntary breath-hold gating for cone beam CT-based stereotactic body radiotherapy. , 2011, International journal of radiation oncology, biology, physics.

[21]  Ben Heijmen,et al.  Reduction of respiratory liver tumor motion by abdominal compression in stereotactic body frame, analyzed by tracking fiducial markers implanted in liver. , 2008, International journal of radiation oncology, biology, physics.

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

[23]  J G M Kok,et al.  Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept , 2009, Physics in medicine and biology.

[24]  Kunwoo Lee,et al.  Quasi-breath-hold Technique Using Personalized Audio-visual Biofeedback for Respiratory Motion Manag , 2011 .

[25]  Paul Keall,et al.  Audiovisual biofeedback improves diaphragm motion reproducibility in MRI. , 2012, Medical physics.

[26]  J. Wong,et al.  The use of active breathing control (ABC) to reduce margin for breathing motion. , 1999, International journal of radiation oncology, biology, physics.

[27]  Kunwoo Lee,et al.  Quasi-breath-hold technique using personalized audio-visual biofeedback for respiratory motion management in radiotherapy. , 2011, Medical physics.

[28]  Eike Rietzel,et al.  Design of 4D treatment planning target volumes. , 2006, International journal of radiation oncology, biology, physics.

[29]  Jan J W Lagendijk,et al.  MRI/linac integration. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[30]  Bjorn Stemkens,et al.  4D MR imaging using robust internal respiratory signal , 2016, Physics in medicine and biology.

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

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

[33]  B. Fallone,et al.  First MR images obtained during megavoltage photon irradiation from a prototype integrated linac-MR system. , 2009, Medical physics.

[34]  Subhabrata Chakraborti,et al.  Nonparametric Statistical Inference , 2011, International Encyclopedia of Statistical Science.

[35]  Patrick A Kupelian,et al.  Real-time tumor tracking in the lung using an electromagnetic tracking system. , 2013, International journal of radiation oncology, biology, physics.

[36]  Masoom A. Haider,et al.  Comparison of liver tumor motion with and without abdominal compression using cine-magnetic resonance imaging. , 2011, International journal of radiation oncology, biology, physics.

[37]  Peter B. Greer,et al.  Audiovisual biofeedback improves image quality and reduces scan time for respiratory-gated 3D MRI , 2014 .

[38]  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.

[39]  J M Brady,et al.  Image filtering techniques for medical image post-processing: an overview. , 2004, The British journal of radiology.

[40]  Minsong Cao,et al.  Respiratory motion‐resolved, self‐gated 4D‐MRI using rotating cartesian k‐space (ROCK) , 2017, Medical physics.

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