Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Brock, Kristy K.; Mutic, Sasa; McNutt, Todd R.; Li, Hua; Kessler, Marc L.

doi:10.1002/mp.12256

Cited by 881Advanced Radiotherapy TechniquesMedical Image Segmentation TechniquesRadiation Dose and Imaging

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

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Brock, Kristy K., Mutic, Sasa, McNutt, Todd R., Li, Hua, Kessler, Marc L.·2017·Medical Physics

DOI 10.1002/mp.12256

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Abstract

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

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A registration is successful if mTRE ≤ 1 mm, following AAPM guidelines.
“Following guidelines from the American Association of Physicists in Medicine (AAPM) [11], a registration is successful if mTRE ≤ 1 mm.”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [11]
Section 4.C.4 specifies a 1 mm tolerance for stereotactic radiosurgery, but not for registration generally. Table III (section 4.D) lists the target registration error tolerance as the maximum voxel dimension, approximately 2–3 mm. The claim omits the condition under which the 1 mm tolerance applies. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Registration is of great significance in radiotherapy; it is performed to calculate the offset of the current target from the planned position, and the offset is used to adjust patient position or radiation beam.
“Moreover, registration is also of great significance in radiotherapy (11, 12); it is performed to calculate the offset of the current target from the planned position, and the offset determined from the registration is used to adjust the patient position or the radiation beam.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [12]
Section 1.B.3 states that registration determines the patient's offset from the planned position and that the resulting transformation is typically used to adjust patient position. Section 6.D.2 also discusses target-focused registration at treatment delivery. The cited passages do not establish that the offset is used to adjust the radiation beam, so that alternative is unsupported. (LLM, conf=0.85)
Experimental — accuracy not yet measured

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Mapping of the prostate in endorectal coil‐based MRI/MRSI and CT: A deformable registration and validation study
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Cardiac motion compensation and resolution modeling in simultaneous PET-MR: a cardiac lesion detection study
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Sliding characteristic and material compressibility of human lung: Parametric study and verification
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Automated generation of a four‐dimensional model of the liver using warping and mutual information
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A strategy for automated multimodality image registration incorporating anatomical knowledge and imager characteristics
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Technical note: A physical phantom for assessment of accuracy of deformable alignment algorithms
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A symmetric nonrigid registration method to handle large organ deformations in cervical cancer patients
Bondar, L., Hoogeman, M. S., Osorio, E. M. V., & Heijmen, B. J. M. (2010) · Medical Physics · doi:10.1118/1.3443436 · Cited by 70 · see citing works
Phantom validation of coregistration of PET and CT for image‐guided radiotherapy
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Toward efficient biomechanical-based deformable image registration of lungs for image-guided radiotherapy
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Technical note: Creating a four‐dimensional model of the liver using finite element analysis
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A material sensitivity study on the accuracy of deformable organ registration using linear biomechanical models.
Chi, Y., Liang, J., & Yan, D. (2006) · Medical Physics (Lancaster) · doi:10.1118/1.2163838 · Cited by 65 · see citing works
Imaging and Image-Guided Radiation Therapy in Liver Cancer
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An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy
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A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy
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A fully four-dimensional, iterative motion estimation and compensation method for cardiac CT
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Mutual‐information‐based image to patient re‐registration using intraoperative ultrasound in image‐guided neurosurgery
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Application of dose compensation in image-guided radiotherapy of prostate cancer
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A feasibility study of mutual information based setup error estimation for radiotherapy
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FEM-based evaluation of deformable image registration for radiation therapy
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A finite element method to correct deformable image registration errors in low-contrast regions
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Validation of a method for measuring the volumetric breast density from digital mammograms
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Automatic registration of pelvic computed tomography data and magnetic resonance scans including a full circle method for quantitative accuracy evaluation
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Feature‐based rectal contour propagation from planning CT to cone beam CT
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Reducing uncertainties in volumetric image based deformable organ registration
Liang, J., & Yan, D. (2003) · Medical Physics · doi:10.1118/1.1587631 · Cited by 54 · see citing works
A semiautomatic method for registration of portal images
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Evaluation of a contour-alignment technique for CT-guided prostate radiotherapy: an intra- and interobserver study
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Deformable image registration for the use of magnetic resonance spectroscopy in prostate treatment planning
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The impact of respiratory motion and treatment technique on stereotactic body radiation therapy for liver cancer
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On the Need for Comprehensive Validation of Deformable Image Registration, Investigated With a Novel 3-Dimensional Deformable Dosimeter
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Narrow band deformable registration of prostate magnetic resonance imaging, magnetic resonance spectroscopic imaging, and computed tomography studies
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Automatic segmentation of phase‐correlated CT scans through nonrigid image registration using geometrically regularized free‐form deformation
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Accuracy and sensitivity of finite element model‐based deformable registration of the prostate
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A Contour-Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy
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Systematic evaluation of four‐dimensional hybrid depth scanning for carbon‐ion lung therapy
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An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy
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A two‐dimensional deformable phantom for quantitatively verifying deformation algorithms
Kirby, N., Chuang, C., & Pouliot, J. (2011) · Medical Physics · doi:10.1118/1.3597881 · Cited by 45 · see citing works
Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast
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Breathing chest radiography using a dynamic flat-panel detector combined with computer analysis
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Nonrigid registration with tissue-dependent filtering of the deformation field
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Deformable image registration of heterogeneous human lung incorporating the bronchial tree
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Interobserver delineation variation in lung tumour stereotactic body radiotherapy
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Registration of 3-D head surfaces using multiple landmarks
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Comparison of similarity measures for the task of template matching of masses on serial mammograms
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Biomechanical-based image registration for head and neck radiation treatment
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Quantitative evaluation of free‐form deformation registration for dynamic contrast‐enhanced MR mammography
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Adapting liver motion models using a navigator channel technique
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Comparing the accuracy of four‐dimensional photon dose calculations with three‐dimensional calculations using moving and deforming phantoms
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Image‐guided patient positioning: If one cannot correct for rotational offsets in external‐beam radiotherapy setup, how should rotational offsets be managed?
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Deformable structure registration of bladder through surface mapping
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An adaptive irregular grid approach for 3D deformable image registration
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Matching PET and CT scans of the head and neck area: Development of method and validation
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Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis—local affine transformation for improved localization
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A procedure for patient repositioning and compensation for misalignment between transmission and emission data in PET heart studies.
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A feasibility study to investigate the use of thin-plate splines to account for prostate deformation
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Deformable Image Registration of Liver With Consideration of Lung Sliding Motion
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Accurate CT/MR vessel‐guided nonrigid registration of largely deformed livers
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Contour propagation in MRI-guided radiotherapy treatment of cervical cancer: the accuracy of rigid, non-rigid and semi-automatic registrations
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A stochastic approach to estimate the uncertainty of dose mapping caused by uncertainties in b‐spline registration
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Analytic regularization for landmark-based image registration
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Optimized knot placement for B‐splines in deformable image registration
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Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching
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Tissue Feature-Based and Segmented Deformable Image Registration for Improved Modeling of Shear Movement of Lungs
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Comparison of rigid and elastic matching of dynamic magnetic resonance mammographic images by mutual information
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Evaluation of cost functions for gray value matching of two-dimensional images in radiotherapy
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Geometric feature‐based multimodal image registration of contrast‐enhanced cardiac CT with gated myocardial perfusion SPECT
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A comparison of image registration techniques for the correlation of radiolabelled antibody distribution with tumour morphology
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Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment
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Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy
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Digital portal image registration by sequential anatomical matchpoint and image correlations for real‐time continuous field alignment verification
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Surface scanning through a cylindrical tank of coupling fluid for clinical microwave breast imaging exams
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Evaluation of intersession 3D‐TRUS to 3D‐TRUS image registration for repeat prostate biopsies
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Spatially weighted mutual information image registration for image guided radiation therapy.
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Volume registration using the 3D core
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A pseudoinverse deformation vector field generator and its applications
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Comparison of an image registration technique based on normalized mutual information with a standard method utilizing implanted markers in the staged radiosurgical treatment of large arteriovenous malformations
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Non-rigid consistent registration of 2D image sequences
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Tracking the motion trajectories of junction structures in 4D CT images of the lung
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A new algorithm for the registration of portal images to planning images in the verification of radiotherapy, as validated in prostate treatments
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Voxel-Based Dose Reconstruction for Total Body Irradiation With Helical TomoTherapy
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Classifying geometric variability by dominant eigenmodes of deformation in regressing tumours during active breath-hold lung cancer radiotherapy
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Voxel clustering for quantifying PET‐based treatment response assessment
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Registration of myocardial PET and SPECT for viability assessment using mutual information
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Spatial registration of temporally separated whole breast 3D ultrasound images
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Dynamic estimation of three‐dimensional cerebrovascular deformation from rotational angiography
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A novel four-dimensional image registration method for radionuclide therapy dosimetry
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Sorted by citations

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance
Vandewinckele, L., Claessens, M., Dinkla, A. M., Brouwer, C. L., Crijns, W., Verellen, D., & van Elmpt, W. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.09.008 · Cited by 326 · see citing works
Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology
Glide‐Hurst, C. K., Lee, P. P., Yock, A. D., Olsen, J. R., Cao, M., Siddiqui, F., Parker, W., Doemer, A., Rong, Y., Kishan, A. U., et al. (2020) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2020.10.021 · Cited by 323 · see citing works
“Several studies have explored this approach,(51,79) and TG-132 also provided data sets created from ImSimQA software and recommended commercial DIR software vendors to provide feasible tools for user validation.(80) TG-132 provides guidelines for DIR evaluation metrics such as setting the tolerance of point-wise regis…”
Method
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works
Adaptive Radiotherapy for Anatomical Changes
Sonke, J., Aznar, M. C., & Rasch, C. R. N. (2019) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2019.02.007 · Cited by 305 · see citing works
Online daily adaptive proton therapy
Albertini, F., Matter, M., Nenoff, L., Zhang, Y., & Lomax, A. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190594 · Cited by 168 · see citing works
Applications and limitations of machine learning in radiation oncology
Jarrett, D. P., Stride, E., Vallis, K. A., & Gooding, M. J. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190001 · Cited by 164 · see citing works
“42 Such an approach enables the true registration to be known but would still requires the registration used to be clinically meaningful to train a registration method to generate clinically plausible registrations.”
Method
How rapid advances in imaging are defining the future of precision radiation oncology
Beaton, L., Bandula, S., Gaze, M. N., & Sharma, R. A. (2019) · British Journal of Cancer · doi:10.1038/s41416-019-0412-y · Cited by 152 · see citing works
Deformable image registration for radiation therapy: principle, methods, applications and evaluation
Rigaud, B., Simon, A., Castelli, J., Lafond, C., Acosta, O., Haigron, P., Cazoulat, G., & de Crevoisier, R. (2019) · Acta Oncologica · doi:10.1080/0284186x.2019.1620331 · Cited by 152 · see citing works
“DIR has been extensively studied for radiation therapy (RT) applications, and its integration into clinical practice is currently the object of intensive research effort as shown by the report of the AAPM Task Group 132 [1].”
MethodInfluential
Recent advances in radiation oncology
Garibaldi, C., Jereczek‐Fossa, B. A., Marvaso, G., Dicuonzo, S., Rojas, D. P., Cattani, F., Starzyńska, A., Ciardo, D., Surgo, A., Leonardi, M. C., et al. (2017) · ecancermedicalscience · doi:10.3332/ecancer.2017.785 · Cited by 144 · see citing works
“Most TPSs now support both rigid and deformable image registration and fusion [79].”
BackgroundMethodInfluential
Deformable Registration for Dose Accumulation.
Chetty, I., & Rosu-Bubulac, M. (2019) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2019.02.002 · Cited by 131 · see citing works
Recursive Deformable Pyramid Network for Unsupervised Medical Image Registration
Wang, H., Ni, D., & Wang, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3362968 · Cited by 139 · see citing works
“In such a way, information from mono-/multi-modalities can be fused into a common coordinate system to assist doctors with diagnosis and treatment [4], [5], [6].”
Background
Adaptive radiotherapy for head and neck cancer
Castelli, J., Simon, A., Lafond, C., Périchon, N., Rigaud, B., Chajon, É., Bari, B. D., Özşahin, M., Bourhis, J., & de Crevoisier, R. (2018) · Acta Oncologica · doi:10.1080/0284186x.2018.1505053 · Cited by 136 · see citing works
Task group 284 report: magnetic resonance imaging simulation in radiotherapy: considerations for clinical implementation, optimization, and quality assurance
Glide‐Hurst, C. K., Paulson, E. S., McGee, K. P., Tyagi, N., Hu, Y., Balter, J. M., & Bayouth, J. E. (2021) · Medical Physics · doi:10.1002/mp.14695 · Cited by 135 · see citing works
Mitochondria-targeted smart AIEgens: Imaging and therapeutics
Li, H., Kim, H., Zhang, C., Zeng, S., Chen, Q., Jia, L., Wang, J., Peng, X., & Yoon, J. (2022) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2022.214818 · Cited by 133 · see citing works
Self-citation
LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung
Fu, Y., Lei, Y., Wang, T., Higgins, K. A., Bradley, J. D., Curran, W. J., Liu, T., & Yang, X. (2020) · Medical Physics · doi:10.1002/mp.14065 · Cited by 131 · see citing works
“Deformable image registration (DIR) of 4D-CT lung images is important in multiple radiation therapy applications including lung motion tracking 1 , target definition 2 , image fusion 3 , gated treatment planning 4 and treatment response evaluations 5 .”
Background
Technological quality requirements for stereotactic radiotherapy
Schmitt, D., Blanck, O., Gauer, T., Fix, M. K., Brunner, T. B., Fleckenstein, J., Loutfi-Krauß, B., Manser, P., Werner, R., Wilhelm, M.-L., et al. (2020) · Strahlentherapie und Onkologie · doi:10.1007/s00066-020-01583-2 · Cited by 128 · see citing works
“Patient‐specific validation of deformable image registration in radiation therapy: Overview and caveats”
Paganelli, C., Meschini, G., Molinelli, S., Riboldi, M., & Baroni, G. (2018) · Medical Physics · doi:10.1002/mp.13162 · Cited by 118 · see citing works
The VAMPIRE challenge: A multi‐institutional validation study of CT ventilation imaging
Kipritidis, J., Tahir, B., Cazoulat, G., Hofman, M. S., Siva, S., Callahan, J., Hardcastle, N., Yamamoto, T., Christensen, G. E., Reinhardt, J. M., et al. (2018) · Medical Physics · doi:10.1002/mp.13346 · Cited by 101 · see citing works
“The DIR spatial accuracy is quantified in two ways based on the AAPM Task Group 132 report on the quality assurance of image registration.(34)”
Self-citation
Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century
Naqa, I. E., Haider, M. A., Giger, M. L., & Haken, R. K. T. (2020) · British Journal of Radiology · doi:10.1259/bjr.20190855 · Cited by 101 · see citing works
“Varying algorithms for rigid and defomarable registration have been proposed in the literature using intensity or feature-based approaches 142,143 and more recently ML/DL algorithms.”
Method
A review of dose calculation approaches with cone beam CT in photon and proton therapy
Giacometti, V., Hounsell, A. R., & McGarry, C. K. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.06.017 · Cited by 101 · see citing works
The Special Medical Physics Consult Process for Reirradiation Patients
Paradis, K. C., Mayo, C. S., Owen, D., Spratt, D. E., Hearn, J. W. D., Rosen, B., Kashani, R., Moran, J. M., Tatro, D. S., Beeler, W. H., et al. (2019) · Advances in Radiation Oncology · doi:10.1016/j.adro.2019.05.007 · Cited by 100 · see citing works
4D-CT deformable image registration using multiscale unsupervised deep learning
Lei, Y., Fu, Y., Wang, T., Liu, Y., Patel, P. R., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab79c4 · Cited by 100 · see citing works
“Deformable image registration (DIR) has been used in many medical applications such as image segmentation (Brock et al 2017, Fu et al 2017, Oh and Kim 2017), motion estimation (Christensen et al 2007, Boldea et al 2008, Yang et al 2008), image fusion (El-Gamal et al 2016) and treatment response…”
Method
Commissioning of a 1.5T Elekta Unity MR‐linac: A single institution experience
Snyder, J. E., St‐Aubin, J., Yaddanapudi, S., Boczkowski, A., Dunkerley, D. A. P., Graves, S. A., & Hyer, D. E. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12902 · Cited by 100 · see citing works
Adaptive Radiation Therapy: A Review of CT-based Techniques
Lavrova, E., Garrett, M. D., Wang, Y., Chin, C., Elliston, C. D., Savacool, M. K., Price, M., Kachnic, L. A., & Horowitz, D. P. (2023) · Radiology Imaging Cancer · doi:10.1148/rycan.230011 · Cited by 98 · see citing works
Applicability and usage of dose mapping/accumulation in radiotherapy
Murr, M., Brock, K. K., Fusella, M., Hardcastle, N., Hussein, M., Jameson, M., Wahlstedt, I., Yuen, J., McClelland, J. R., & Osorio, E. M. V. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109527 · Cited by 97 · see citing works
“These include known ground truth features, such as landmarks, DVF and dose distribution [1].”
BackgroundMethodInfluentialSelf-citation
Review and recommendations on deformable image registration uncertainties for radiotherapy applications
Nenoff, L., Amstutz, F., Murr, M., Archibald‐Heeren, B. R., Fusella, M., Hussein, M., Lechner, W., Zhang, Y., Sharp, G., & Osorio, E. M. V. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad0d8a · Cited by 99 · see citing works
A review of deep learning-based deformable medical image registration
Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91 · see citing works
Consensus opinion on MRI simulation for external beam radiation treatment planning
Paulson, E. S., Crijns, S. P. M., Keller, B. M., Wang, J., Schmidt, M. A., Coutts, G. A., & van der Heide, U. A. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.09.018 · Cited by 87 · see citing works
A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy
Mackay, K., Bernstein, D., Glocker, B., Kamnitsas, K., & Taylor, A. (2023) · Clinical Oncology · doi:10.1016/j.clon.2023.01.016 · Cited by 86 · see citing works
“This was based on image registration and fusion algorithm recommendations by the American Association of Physicists in Medicine (AAPM) task group, but ignores the AAPM recommendation to consider the size of the structure if using Dice [138].”
Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician
Heukelom, J., & Fuller, C. D. (2019) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2019.02.008 · Cited by 84 · see citing works
“planning simulation, previous daily on-line (44, 130)”
BackgroundMethodInfluential
STereotactic Arrhythmia Radioablation (STAR): the Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary consortium (STOPSTORM.eu) and review of current patterns of STAR practice in Europe
Grehn, M., Mandija, S., Miszczyk, M., Krug, D. A., Tomasik, B., Stickney, K., Alcántara, P., Alongi, F., Anselmino, M., Salgado‐Aranda, R., et al. (2022) · EP Europace · doi:10.1093/europace/euac238 · Cited by 84 · see citing works
“47”
Quality Assurance for AI-Based Applications in Radiation Therapy
Claessens, M., Oria, C. S., Brouwer, C. L., Ziemer, B. P., Scholey, J., Lin, H., Witztum, A., Morin, O., Naqa, I. E., van Elmpt, W., et al. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.011 · Cited by 86 · see citing works
Initial Feasibility and Clinical Implementation of Daily MR-Guided Adaptive Head and Neck Cancer Radiation Therapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation
McDonald, B. A., Vedam, S. S., Yang, J., Wang, J., Castillo, P., Lee, B., Sobremonte, A., Ahmed, S., Ding, Y., Mohamed, A., et al. (2020) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2020.12.015 · Cited by 80 · see citing works
Standardizing Normal Tissue Contouring for Radiation Therapy Treatment Planning: An ASTRO Consensus Paper
Wright, J. L., Yom, S. S., Awan, M. J., Dawes, S. L., Fischer-Valuck, B. W., Kudner, R., Vega, R. B. M., & Rodrigues, G. (2018) · Practical Radiation Oncology · doi:10.1016/j.prro.2018.12.003 · Cited by 80 · see citing works
Voxel-based analysis in radiation oncology: A methodological cookbook
Palma, G., Monti, S., & Cella, L. (2020) · Physica Medica · doi:10.1016/j.ejmp.2019.12.013 · Cited by 77 · see citing works
Positron Emission Tomography (PET)/Computed Tomography (CT) Imaging in Radiation Therapy Treatment Planning: A Review of PET Imaging Tracers and Methods to Incorporate PET/CT
Trotter, J. W., Pantel, A. R., Teo, B.-K. K., Escorcia, F. E., Li, T., Pryma, D. A., & Taunk, N. K. (2023) · Advances in Radiation Oncology · doi:10.1016/j.adro.2023.101212 · Cited by 79 · see citing works
Performance of commercially available deformable image registration platforms for contour propagation using patient‐based computational phantoms: A multi‐institutional study
Loi, G., Fusella, M., Lanzi, E., Cagni, E., Garibaldi, C., Iacoviello, G., Lucio, F., Menghi, E., Miceli, R., Orlandini, L. C., et al. (2017) · Medical Physics · doi:10.1002/mp.12737 · Cited by 77 · see citing works
Biomechanically constrained non-rigid mr-trus prostate registration using deep learning based 3d point cloud matching
Fu, Y., Lei, Y., Wang, T., Patel, P., Jani, A. B., Mao, H., Curran, W. J., Liu, T., & Yang, X. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101845 · Cited by 75 · see citing works
“To assess the uncertain levels of registration results, we have followed the guidelines recommended by AAPM Task Group 132 (Brock et al., 2017), which defines uncertainty level of 0 to 4 as ‘whole scan aligned’, ‘locally aligned’, ‘usable with risk of deformation’, ‘usable for diagnosis only’,…”
Method
HaN‐Seg: The head and neck organ‐at‐risk CT and MR segmentation dataset
Podobnik, G., Strojan, P., Peterlin, P., Ibragimov, B., & Vrtovec, T. (2023) · Medical Physics · doi:10.1002/mp.16197 · Cited by 75 · see citing works
Deformable image registration uncertainty for inter-fractional dose accumulation of lung cancer proton therapy
Nenoff, L., Ribeiro, C. O., Matter, M., Hafner, L., Josipović, M., Langendijk, J. A., Persson, G. F., Walser, M., Weber, D. C., Lomax, A., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.04.046 · Cited by 74 · see citing works
“Modern algorithms however, have different ways of implementing these, which are reviewed elsewhere [29].”
Background
Additive manufacturing in radiation oncology: a review of clinical practice, emerging trends and research opportunities
Tino, R., Leary, M., Yeo, A. U., Kyriakou, E., Kron, T., & Brandt, M. (2020) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ab70af · Cited by 71 · see citing works
Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy
Rosen, B., Hawkins, P. G., Polan, D. F., Balter, J. M., Brock, K. K., Kamp, J. D., Lockhart, C., Eisbruch, A., Mierzwa, M. L., Haken, R. K. T., et al. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.06.048 · Cited by 69 · see citing works
Self-citation
A method of using deep learning to predict three‐dimensional dose distributions for intensity‐modulated radiotherapy of rectal cancer
Zhou, J., Peng, Z. M., Song, Y., Chang, Y., Pei, X., Sheng, L., & Xu, X. G. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12849 · Cited by 64 · see citing works
Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system
Velec, M., Moseley, J. L., Svensson, S., Hårdemark, B., Jaffray, D. A., & Brock, K. K. (2017) · Medical Physics · doi:10.1002/mp.12307 · Cited by 63 · see citing works
Self-citation
Radiation Oncology Physics
Bourland, J. D. (2015) · Elsevier eBooks · doi:10.1016/b978-0-323-24098-7.00006-x · Cited by 61 · see citing works
Including anatomical variations in robust optimization for head and neck proton therapy can reduce the need of adaptation
Cubillos-Mesías, M., Troost, E. G. C., Lohaus, F., Agolli, L., Rehm, M., Richter, C., & Stützer, K. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.12.008 · Cited by 62 · see citing works
A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI
Iglesias, J. E. (2023) · Scientific Reports · doi:10.1038/s41598-023-33781-0 · Cited by 64 · see citing works
Different Medical Image Registration Techniques: A Comparative Analysis
Karthick, S., & Maniraj, S. P. (2019) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405614666180905094032 · Cited by 60 · see citing works
The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning
Netherton, T., Cardenas, C. E., Rhee, D. J., Court, L. E., & Beadle, B. M. (2020) · Oncology · doi:10.1159/000512172 · Cited by 59 · see citing works
“For DL-based tools, periodic performance upon datasets could be used for initial validation and QA/QC programs, similar in principle to recommendations by Brock et al. [50] in AAPM TG 132 for image and registration and fusion algorithms.”
Method
Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models
Urago, Y., Okamoto, H., Kaneda, T., Murakami, N., Kashihara, T., Takemori, M., Nakayama, H., Iijima, K., Chiba, T., Kuwahara, J., et al. (2021) · Radiation Oncology · doi:10.1186/s13014-021-01896-1 · Cited by 59 · see citing works
Patient selection, ventricular tachycardia substrate delineation, and data transfer for stereotactic arrhythmia radioablation: a clinical consensus statement of the European Heart Rhythm Association of the European Society of Cardiology and the Heart Rhythm Society
Zeppenfeld, K., Rademaker, R., Al‐Ahmad, A., Carbucicchio, C., de Chillou, C., Cvek, J., Ebert, M., Ho, G., Kautzner, J., Lambiase, P. D., et al. (2024) · EP Europace · doi:10.1093/europace/euae214 · Cited by 55 · see citing works
“231 When the planning CT is equivalent to the input angio-CT in terms of respiratory, cardiac, or both motions, registration uncertainty determining the accuracy of target volume transfer onto planning CT should be reduced or even eliminated.”
The impact of technology on the changing practice of lung SBRT
Aznar, M. C., Warren, S., Hoogeman, M. S., & Josipović, M. (2018) · Physica Medica · doi:10.1016/j.ejmp.2017.12.020 · Cited by 54 · see citing works
Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours
Ribeiro, C. O., Knopf, A. C., Langendijk, J. A., Weber, D. C., Lomax, A., & Zhang, Y. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.03.001 · Cited by 54 · see citing works
Image registration, contour propagation and dose accumulation of external beam and brachytherapy in gynecological radiotherapy
Swamidas, J., Kirisits, C., Brabandere, M. D., Hellebust, T. P., Siebert, F., & Tanderup, K. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.08.023 · Cited by 53 · see citing works
Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy
Zeng, Q., Fu, Y., Tian, Z., Lei, Y., Zhang, Y., Wang, T., Mao, H. Z., Liu, T., Curran, W. J., Jani, A. B., et al. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab8cd6 · Cited by 53 · see citing works
“In addition to the difficulties of designing accurate and efficient methods, the great challenges of implementing robust and reliable evaluation metrics exist for MRI-TRUS image registration (Brock et al 2017, Paganelli et al 2018).”
BackgroundMethod
Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
Santoro, M., Strolin, S., Paolani, G., Gala, G. D., Bartoloni, A., Giacometti, C., Ammendolia, I., MORGANTI, A. G., & Strigari, L. (2022) · Applied Sciences · doi:10.3390/app12073223 · Cited by 51 · see citing works
“The validation of the DIR-based algorithms against expert contours is necessary, as recommended by the American Association of Physicists in Medicine’s Task Group (TG) 132 report [15] and as applied in several papers [16–23].”
Method
Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation
McDonald, B. A., Zachiu, C., Christodouleas, J. P., Naser, M. A., Ruschin, M., Sonke, J., Thorwarth, D., Létourneau, D., Tyagi, N., Tadic, T., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1086258 · Cited by 49 · see citing works
Self-citation
Classification of rotator cuff tears in ultrasound images using deep learning models
Ho, T. T., Kim, G.-T., Kim, T., Choi, S., & Park, E. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02502-6 · Cited by 49 · see citing works
“The approach also has been used for a diagnosis of muscle state in CT and MRI datasets [20–27], detection of large RCTs from conventional shoulder radiographs [28], and segmentation RCTs for diseased regions in ultrasound images [29].”
Method
Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering
Gopalakrishnan, V., Dey, N., & Golland, P. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01108 · Cited by 48 · see citing works
“Following guidelines from the American Association of Physicists in Medicine (AAPM) [11], a registration is successful if mTRE  1 mm.”
Method
Rigid and Deformable Image Registration for Radiation Therapy: A Self-Study Evaluation Guide for NRG Oncology Clinical Trial Participation
Rong, Y., Rosu‐Bubulac, M., Benedict, S., Cui, Y., Ruo, R., Connell, T., Kashani, R., Latifi, K., Chen, Q., Geng, H., et al. (2021) · Practical Radiation Oncology · doi:10.1016/j.prro.2021.02.007 · Cited by 48 · see citing works
Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis
Fung, N. T. C., Hung, W. M., Sze, C.-K., Lee, M. C. H., & Ng, W. T. (2019) · Medical dosimetry · doi:10.1016/j.meddos.2019.06.002 · Cited by 47 · see citing works
Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients
Alam, S. R., Veeraraghavan, H., Tringale, K. R., Amoateng, E. J., Subashi, E., Wu, A. J.-C., Crane, C. H., & Tyagi, N. (2022) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2022.02.007 · Cited by 47 · see citing works
“93, which is within the range of AAPM TG-132 recommended DSC values to consider a DIR algorithm sufficiently reliable for clinical deployment [31,32].”
Background
ICRU REPORT 97: MRI-Guided Radiation Therapy Using MRI-Linear Accelerators
Keall, P., Glide‐Hurst, C. K., Cao, M., Lee, P. P., Murray, B., Raaymakers, B. W., Tree, A. C., & van der Heide, U. A. (2022) · Journal of the ICRU · doi:10.1177/14736691221141950 · Cited by 46 · see citing works
Assessment of the accuracy and stability of frameless gamma knife radiosurgery
Chung, H., Park, W., Kim, T. H., Kim, Y. K., & Chun, K. J. (2018) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12365 · Cited by 46 · see citing works
Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge
McKenzie, E. M., Santhanam, A. P., Ruan, D., O'Connor, D., Cao, M., & Sheng, K. (2019) · Medical Physics · doi:10.1002/mp.13976 · Cited by 46 · see citing works
The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective
Press, R. H., Shu, H. G., Shim, H., Mountz, J. M., Kurland, B. F., Wahl, R. L., Jones, E. F., Hylton, N. M., Gerstner, E. R., Nordstrom, R. J., et al. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.06.023 · Cited by 45 · see citing works
“[51] Differences in image acquisition, treatment position, respiratory motion,[54] image registration,[55] and technical factors with individual scanners also affect eventual SUV-based contours.”
Usefulness of hybrid deformable image registration algorithms in prostate radiation therapy
Motegi, K., Tachibana, H., Motegi, A., Hotta, K., Baba, H., & Akimoto, T. (2018) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12515 · Cited by 45 · see citing works
“and may indicate an error in the registration or a limitation in the algorithm to handle complex deformation.(23) To assess the statistical significance of the mean DSC, TRE, and JD between the intensity‐based DIR and hybrid DIR, P‐values by two‐tailed paired t‐test were evaluated.”
The commissioning and validation of Monaco treatment planning system on an Elekta Versa HD linear accelerator
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Authors: Brock, K. K., Mutic, S., McNutt, T. R., Li, H., & Kessler, M. L.
Affiliations: The University of Texas MD Anderson Cancer Center (US)
Washington University in St. Louis (US)
Johns Hopkins University (US)
University of Michigan (US)
Corresponding author: Brock, Kristy K.
ORCID: Brock, Kristy K.
Mutic, Sasa
McNutt, Todd R.
Li, Hua
Venue: Medical Physics
Publisher: Wiley
Published: 2017-05-23
Volume: 44
Issue: 7
Pages: e43-e76
ISSN: 0094-2405 (ISSN-L)
DOI: 10.1002/mp.12256
OpenAlex: W2605239868
Cited by: 881
References: 175
Citations by year: 2026: 113 · 2025: 108 · 2024: 105 · 2023: 101 · 2022: 101 · 2021: 92 · 2020: 114 · 2019: 72 · 2018: 56 · 2017: 16 · 2016: 1 · 2015: 1 · 2014: 1
FWCI: 66.54
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: doi.wiley.com/10.1002/tdm_license_1 (tdm)
onlinelibrary.wiley.com/termsAndConditions (vor)
Locations: Medical Physics (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
Keywords: image registration, image fusion, radiotherapy, rigid registration, deformable image registration, adaptive radiotherapy, quality assurance, quality control, registration uncertainty, multimodal imaging, patient positioning, dose accumulation, target volume delineation, software validation
MeSH terms: Algorithms, Humans, Radiotherapy Planning, Computer-Assisted, Software, Tomography, X-Ray Computed

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Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

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