Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences.

PURPOSE To validate automatic substitute computed tomography CT (sCT) scans generated from standard T2-weighted (T2w) magnetic resonance (MR) pelvic scans for MR-Sim prostate treatment planning. PATIENTS AND METHODS A Siemens Skyra 3T MR imaging (MRI) scanner with laser bridge, flat couch, and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole-pelvis MRI scan (1.6 mm 3-dimensional isotropic T2w SPACE [Sampling Perfection with Application optimized Contrasts using different flip angle Evolution] sequence) was acquired. Three additional small field of view scans were acquired: T2w, T2*w, and T1w flip angle 80° for gold fiducials. Patients received a routine planning CT scan. Manual contouring of the prostate, rectum, bladder, and bones was performed independently on the CT and MR scans. Three experienced observers contoured each organ on MRI, allowing interobserver quantification. To generate a training database, each patient CT scan was coregistered to their whole-pelvis T2w using symmetric rigid registration and structure-guided deformable registration. A new multi-atlas local weighted voting method was used to generate automatic contours and sCT results. RESULTS The mean error in Hounsfield units between the sCT and corresponding patient CT (within the body contour) was 0.6 ± 14.7 (mean ± 1 SD), with a mean absolute error of 40.5 ± 8.2 Hounsfield units. Automatic contouring results were very close to the expert interobserver level (Dice similarity coefficient): prostate 0.80 ± 0.08, bladder 0.86 ± 0.12, rectum 0.84 ± 0.06, bones 0.91 ± 0.03, and body 1.00 ± 0.003. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same dose prescription was found to be 0.3% ± 0.8%. The 3-dimensional γ pass rate was 1.00 ± 0.00 (2 mm/2%). CONCLUSIONS The MR-Sim setup and automatic sCT generation methods using standard MR sequences generates realistic contours and electron densities for prostate cancer radiation therapy dose planning and digitally reconstructed radiograph generation.

[1]  Carlos Ortiz-de-Solorzano,et al.  Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.

[2]  J. Jonsson,et al.  Treatment planning of intracranial targets on MRI derived substitute CT data. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[3]  Tiina Seppälä,et al.  A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer. , 2013, Medical physics.

[4]  Jurgen Fripp,et al.  Robust inverse-consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy , 2015, Medical Image Anal..

[5]  Jan J W Lagendijk,et al.  MR guidance in radiotherapy , 2014, Physics in medicine and biology.

[6]  Ninon Burgos,et al.  Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies , 2014, IEEE Transactions on Medical Imaging.

[7]  L Xing,et al.  MRI-based Treatment Planning with Electron Density Information Mapped from CT Images: A Preliminary Study , 2008, Technology in cancer research & treatment.

[8]  Peter B. Greer,et al.  Structure-Guided Nonrigid Registration of CT-MR Pelvis Scans with Large Deformations in MR-Based Image Guided Radiation Therapy , 2013, CLIP.

[9]  Ciprian Catana,et al.  Toward Implementing an MRI-Based PET Attenuation-Correction Method for Neurologic Studies on the MR-PET Brain Prototype , 2010, The Journal of Nuclear Medicine.

[10]  Jidi Sun,et al.  MRI simulation: end-to-end testing for prostate radiation therapy using geometric pelvic MRI phantoms. , 2015, Physics in medicine and biology.

[11]  L Chen,et al.  Dosimetric evaluation of MRI-based treatment planning for prostate cancer , 2004, Physics in medicine and biology.

[12]  Stefan Klein,et al.  Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. , 2008, Medical physics.

[13]  Carri Glide-Hurst,et al.  Implementation of a novel algorithm for generating synthetic CT images from magnetic resonance imaging data sets for prostate cancer radiation therapy. , 2015, International journal of radiation oncology, biology, physics.

[14]  Peter B. Greer,et al.  MR simulation for prostate radiation therapy: effect of coil mounting position on image quality. , 2014, The British journal of radiology.

[15]  S. Vandenberghe,et al.  MRI-Based Attenuation Correction for PET/MRI Using Ultrashort Echo Time Sequences , 2010, Journal of Nuclear Medicine.

[16]  Ilja Bezrukov,et al.  MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods , 2011, The Journal of Nuclear Medicine.

[17]  Peter B. Greer,et al.  Inverse-consistent rigid registration of CT and MR for MR-based planning and adaptive prostate radiation therapy , 2014 .

[18]  F. Khan The physics of radiation therapy , 1985 .

[19]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[20]  A. Lin,et al.  Feasibility and limitations of bulk density assignment in MRI for head and neck IMRT treatment planning , 2014, Journal of applied clinical medical physics.

[21]  B. Zackrisson,et al.  Dedicated magnetic resonance imaging in the radiotherapy clinic. , 2009, International journal of radiation oncology, biology, physics.

[22]  Benjamin Movsas,et al.  Initial clinical experience with a radiation oncology dedicated open 1.0T MR‐simulation , 2015, Journal of applied clinical medical physics.

[23]  Mary Feng,et al.  Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy , 2013, Physics in medicine and biology.

[24]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[25]  J. Sonke,et al.  Feasibility of MRI-based reference images for image-guided radiotherapy of the pelvis with either cone-beam computed tomography or planar localization images , 2015, Acta oncologica.

[26]  Arne Skretting,et al.  A simulation of MRI based dose calculations on the basis of radiotherapy planning CT images , 2008, Acta oncologica.

[27]  Olivier Salvado,et al.  An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. , 2012, International journal of radiation oncology, biology, physics.

[28]  Steve Webb,et al.  Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone. , 2003, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[29]  T. Ganesh,et al.  Feasibility of using MRI alone for 3D radiation treatment planning in brain tumors. , 2007, Japanese journal of clinical oncology.

[30]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[31]  H. Kjer,et al.  A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times , 2014, Physics in medicine and biology.

[32]  B G Fallone,et al.  A study on the magnetic resonance imaging (MRI)-based radiation treatment planning of intracranial lesions , 2008, Physics in medicine and biology.

[33]  Lei Xing,et al.  A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning , 2014, Physics in medicine and biology.

[34]  J. Jonsson,et al.  Counterpoint: Opportunities and challenges of a magnetic resonance imaging-only radiotherapy work flow. , 2014, Seminars in radiation oncology.

[35]  Olivier Salvado,et al.  A magnetic resonance imaging‐based workflow for planning radiation therapy for prostate cancer , 2011, The Medical journal of Australia.

[36]  Olivier Salvado,et al.  MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[37]  C. Kuhl,et al.  MRI-Based Attenuation Correction for Hybrid PET/MRI Systems: A 4-Class Tissue Segmentation Technique Using a Combined Ultrashort-Echo-Time/Dixon MRI Sequence , 2012, The Journal of Nuclear Medicine.

[38]  A W Beavis,et al.  Radiotherapy treatment planning of brain tumours using MRI alone. , 1998, The British journal of radiology.

[39]  Alan Pollack,et al.  MRI-based treatment planning for radiotherapy: dosimetric verification for prostate IMRT. , 2004, International journal of radiation oncology, biology, physics.

[40]  Josien P. W. Pluim,et al.  Patient Specific Prostate Segmentation in 3-D Magnetic Resonance Images , 2012, IEEE Transactions on Medical Imaging.

[41]  D. Low,et al.  A technique for the quantitative evaluation of dose distributions. , 1998, Medical physics.