• Radiation Oncology Journal
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• v.35 no.2
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• pp.101-111
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• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.303-314
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• 2023

This study aimed to assess the practical feasibility of advanced deformable image registration (DIR) algorithms in radiotherapy by employing two distinct datasets. The first dataset included 14 4D lung CT scans and 31 head and neck CT scans. In the 4D lung CT dataset, we employed the DIR algorithm to register organs at risk and tumors based on respiratory phases. The second dataset comprised pre-, mid-, and post-treatment CT images of the head and neck region, along with organ at risk and tumor delineations. These images underwent registration using the DIR algorithm, and Dice similarity coefficients (DSCs) were compared. In the 4D lung CT dataset, registration accuracy was evaluated for the spinal cord, lung, lung nodules, esophagus, and tumors. The average DSCs for the non-learning-based SyN and NiftyReg algorithms were 0.92±0.07 and 0.88±0.09, respectively. Deep learning methods, namely Voxelmorph, Cyclemorph, and Transmorph, achieved average DSCs of 0.90±0.07, 0.91±0.04, and 0.89±0.05, respectively. For the head and neck CT dataset, the average DSCs for SyN and NiftyReg were 0.82±0.04 and 0.79±0.05, respectively, while Voxelmorph, Cyclemorph, and Transmorph showed average DSCs of 0.80±0.08, 0.78±0.11, and 0.78±0.09, respectively. Additionally, the deep learning DIR algorithms demonstrated faster transformation times compared to other models, including commercial and conventional mathematical algorithms (Voxelmorph: 0.36 sec/images, Cyclemorph: 0.3 sec/images, Transmorph: 5.1 sec/images, SyN: 140 sec/images, NiftyReg: 40.2 sec/images). In conclusion, this study highlights the varying clinical applicability of deep learning-based DIR methods in different anatomical regions. While challenges were encountered in head and neck CT registrations, 4D lung CT registrations exhibited favorable results, indicating the potential for clinical implementation. Further research and development in DIR algorithms tailored to specific anatomical regions are warranted to improve the overall clinical utility of these methods.

• Progress in Medical Physics
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• v.25 no.3
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• pp.167-175
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• 2014

The purpose of this study is to evaluate the variation of the dose which is delivered to the patients with glottis cancer under IMRT (intensity modulated radiation therapy) by using the 3D registration with CBCT (cone beam CT) images and the DIR (deformable image registration) techniques. The CBCT images which were obtained at a one-week interval were reconstructed by using B-spline algorithm in DIR system, and doses were recalculated based on the newly obtained CBCT images. The dose distributions to the tumor and the critical organs were compared with reference. For the change of volume depending on weight at 3 to 5 weeks, there was increased of 1.38~2.04 kg on average. For the body surface depending on weight, there was decreased of 2.1 mm. The dose with transmitted to the carotid since three weeks was increased compared be more than 8.76% planned, and the thyroid gland was decreased to 26.4%. For the physical evaluation factors of the tumor, PITV, TCI, rDHI, mDHI, and CN were decreased to 4.32%, 5.78%, 44.54%, 12.32%, and 7.11%, respectively. Moreover, $D_{max}$, $D_{mean}$, $V_{67.50}$, and $D_{95}$ for PTV were increased or decreased to 2.99%, 1.52%, 5.78%, and 11.94%, respectively. Although there was no change of volume depending on weight, the change of body types occurred, and IMRT with the narrow composure margin sensitively responded to such a changing. For the glottis IMRT, the patient's weight changes should be observed and recorded to evaluate the actual dose distribution by using the DIR techniques, and more the adaptive treatment planning during the treatment course is needed to deliver the accurate dose to the patients.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.83-95
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• 2018

Purpose : After planning the Respiratory Gated Radiotherapy for Lung cancer, the movement and volume change of sparing normal structures nearby target are not often considered during dose evaluation. This study carried out 4-D dose evaluation which reflects the movement of normal structures at certain phase of Respiratory Gated Radiotherapy, by using Deformable Image Registration that is well used for Adaptive Radiotherapy. Moreover, the study discussed the need of analysis and established some recommendations, regarding the normal structures's movement and volume change due to Patient's breathing pattern during evaluation of treatment plans. Materials and methods : The subjects were taken from 10 lung cancer patients who received Respiratory Gated Radiotherapy. Using Eclipse(Ver 13.6 Varian, USA), the structures seen in the top phase of CT image was equally set via Propagation or Segmentation Wizard menu, and the structure's movement and volume were analyzed by Center-to Center method. Also, image from each phase and the dose distribution were deformed into top phase CT image, for 4-dimensional dose evaluation, via VELOCITY Program. Also, Using $QUASAR^{TM}$ Phantom(Modus Medical Devices) and $GAFCHROMIC^{TM}$ EBT3 Film(Ashland, USA), verification carried out 4-D dose distribution for 4-D gamma pass rate. Result : The movement of the Inspiration and expiration phase was the most significant in axial direction of right lung, as $0.989{\pm}0.34cm$, and was the least significant in lateral direction of spinal cord, as -0.001 cm. The volume of right lung showed the greatest rate of change as 33.5 %. The maximal and minimal difference in PTV Conformity Index and Homogeneity Index between 3-dimensional dose evaluation and 4-dimensional dose evaluation, was 0.076, 0.021 and 0.011, 0.0 respectfully. The difference of 0.0045~2.76 % was determined in normal structures, using 4-D dose evaluation. 4-D gamma pass rate of every patients passed reference of 95 % gamma pass rate. Conclusion : PTV Conformity Index was more significant in all patients using 4-D dose evaluation, but no significant difference was observed between two dose evaluations for Homogeneity Index. 4-D dose distribution was shown more homogeneous dose compared to 3D dose distribution, by considering the movement from breathing which helps to fill out the PTV margin area. There was difference of 0.004~2.76 % in 4D evaluation of normal structure, and there was significant difference between two evaluation methods in all normal structures, except spinal cord. This study shows that normal structures could be underestimated by 3-D dose evaluation. Therefore, 4-D dose evaluation with Deformable Image Registration will be considered when the dose change is expected in normal structures due to patient's breathing pattern. 4-D dose evaluation with Deformable Image Registration is considered to be a more realistic dose evaluation method by reflecting the movement of normal structures from patient's breathing pattern.

• Journal of Radiation Protection and Research
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• v.44 no.3
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• pp.103-109
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• 2019

Background: Four-dimensional computed tomographic (4DCT) images are increasingly used in clinic with the growing need to account for the respiratory motion of the patient during radiation treatment. One of the reason s that makes the dose evaluation using 4DCT inaccurate is a change of the patient respiration during the treatment session, i.e., intrafractional uncertainty. Especially, when the amplitude of the patient respiration is greater than the respiration range during the 4DCT acquisition, such an organ motion from the larger respiration is difficult to be represented with the 4DCT. In this paper, the method to generate images expecting the organ motion from a respiration with extended amplitude was proposed and examined. Materials and Methods: We propose a method to generate extra-phase images from a given set of the 4DCT images using deformable image registration (DIR) and linear extrapolation. Deformation vector fields (DVF) are calculated from the given set of images, then extrapolated according to respiratory surrogate. The extra-phase images are generated by applying the extrapolated DVFs to the existing 4DCT images. The proposed method was tested with the 4DCT of a physical 4D phantom. Results and Discussion: The tumor position in the generated extra-phase image was in a good agreement with that in the gold-standard image which is separately acquired, using the same 4DCT machine, with a larger range of respiration. It was also found that we can generate the best quality extra-phase image by using the maximum inhalation phase (T0) and maximum exhalation phase (T50) images for extrapolation. Conclusion: In the present study, a method to construct extra-phase images that represent expanded respiratory motion of the patient has been proposed and tested. The movement of organs from a larger respiration amplitude can be predicted by the proposed method. We believe the method may be utilized for realistic simulation of radiation therapy.

• Journal of radiological science and technology
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• v.40 no.1
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• pp.57-62
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• 2017

The purpose of this study is to reconstruct the treatment plan by applying CBCT and DIR to dose changes according to the change of the patient's motion and breast shape in the large breast cancer patients and to compare the doses using TWF, FIF and IMRT. CT and CBCT were performed with MIM6 to create DIRCT and each treatment plan was made. The patient underwent computed tomography simulation in both prone and supine position. The homogeneity index (HI), conformity index (CI), coverage index (CVI) to the left breast as planning target volume (PTV) were determined and the doses to the lung, heart, and right breast as organ at risk (OAR) were compared by using dose-volume histogram and the unique property of each organ. The value of HI of the PTV breast increased in all treatment planning methods using DIRCT, and CVI and CI were decreased in the treatment planning methods using DIRCT.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.173-181
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• 2012

The aim of this study is to compare the geometric characteristics of the lung tumor, such as tumor centroid, HU change relative to breath phase, depending on tumor location and adhesion using 4DCT and deformable image registration program (MIMVista). The Y axis change was most significant and the mean Y axis centroid fluctuation was $7.32{\pm}6.88mm$ in lower lung tumor. The mean HU variation in lower lung mass has changed more than other locations, and its mean HU variation was $7.7{\pm}4.97%$ and non-adhered mass was more changed. Correlation for the mass volume between 3DCT and MIP was very high and its coefficient was 0.998. The effect of tumor location, adhesion and diaphragm excursion to geometric uncertainties was analyzed by linear regression model, it was influenced to mass deformation and geometrical variation so much except diaphragm excursion. but intra-fractional and inter-patient's uncertainties were great, so it couldn't find any exact deformation trend.