• 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.

• Progress in Medical Physics
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• v.22 no.1
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• pp.28-34
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• 2011

To perform the Adaptive Radiation Therapy (ART), a high degree of deformable registration accuracy is essential. The purpose of this study is to identify whether the change of MV CBCT intensity can improve registration accuracy using predefined modification level and filtering process. To obtain modification level, the cheese phantom images was acquired from both kilovoltage CT (kV CT), megavoltage cone-beam CT (MV CBCT). From the cheese phantom images, the modification level of MV CBCT was defined from the relationship between Hounsfield Units (HUs) of kV CT and MV CBCT images. 'Gaussian smoothing filter' was added to reduce the noise of the MV CBCT images. The intensity of MV CBCT image was changed to the intensity of the kV CT image to make the two images have the same intensity range as if they were obtained from the same modality. The demon deformable registration which was efficient and easy to perform the deformable registration was applied. The deformable lung phantom which was intentionally created in the laboratory to imitate the changes of the breathing period was acquired from kV CT and MV CBCT. And then the deformable lung phantom images were applied to the proposed method. As a result of deformable image registration, the similarity of the correlation coefficient was used for a quantitative evaluation of the result was increased by 6.07% in the cheese phantom, and 18% in the deformable lung phantom. For the additional evaluation of the registration of the deformable lung phantom, the centric coordinates of the mark which was inserted into the inner part of the phantom were measured to calculate the vector difference. The vector differences from the result were 2.23, 1.39 mm with/without modification of intensity of MV CBCT images, respectively. In summary, our method has quantitatively improved the accuracy of deformable registration and could be a useful solution to improve the image registration accuracy. A further study was also suggested in this paper.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.63-66
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• 2019

Due to the development of medical imaging technology, different imaging technologies provide a large amount of effective information. However, different imaging method caused the limitations of information integrity by using single type of image. Combining different image together so that doctor can obtain the information from medical image comprehensively. Image registration algorithm based on mutual information has become one of the hotspots in the field of image registration with its high registration accuracy and wide applicability. Because the information theory-based registration technology is not dependent on the gray value difference of the image, and it is very suitable for multimodal medical image registration. However, the method based on mutual information has a robustness problem. The essential reason is that the mutual information itself is not have enough information between the pixel pairs, so that the mutual information is unstable during the registration process. A large number of local extreme values are generated, which finally cause mismatch. In order to overcome the shortages of mutual information registration method, this paper proposes a registration method combined with image spatial structure information and mutual information.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.700-707
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• 2013

In this paper, we propose a non-rigid registration method of lung parenchyma in temporal chest CT scans using region binarization modeling and locally deformable model. To cope with intensity differences between CT scans, we segment the lung vessel and parenchyma in each scan and perform binarization modeling. Then, we match them without referring any intensity information. We globally align two lung surfaces. Then, locally deformable transformation model is developed for the subsequent non-rigid registration. Subtracted quantification results after non-rigid registration are visualized by pre-defined color map. Experimental results showed that proposed registration method correctly aligned lung parenchyma in the full inspiration and expiration CT images for ten patients. Our non-rigid lung registration method may be useful for the assessment of various lung diseases by providing intuitive color-coded information of quantification results about lung parenchyma.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.64-64
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• 2006

• 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.

• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.149-155
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• 2019

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

• Journal of radiological science and technology
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• v.36 no.3
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• pp.227-235
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• 2013

In adaptive radiotherapy(ART), generated composite dose of surrounding normal tissue on overall treatment course which is using deformable image registration from multistage images. Also, compared with doses summed by each treatment plan and clinical significance is considered. From the first of May, 2011 to the last of July, 2012. Patients who were given treatment and had the head and neck cancer with 3-dimension conformal radiotherapy or intensity modulated radiotherapy, those who were carried out adaptive radiotherapy cause of tumor shrinkage and weight loss. Generated composite dose of surrounding normal tissue using deformable image registration was been possible, statistically significant difference was showed to mandible($48.95{\pm}3.89$ vs $49.10{\pm}3.55$ Gy), oral cavity($36.93{\pm}4.03$ vs $38.97{\pm}5.08$ Gy), parotid gland($35.71{\pm}6.22$ vs $36.12{\pm}6.70$ Gy) and temporomandibular joint($18.41{\pm}9.60$ vs $20.13{\pm}10.42$ Gy) compared with doses summed by each treatment plan. The results of this study show significant difference between composite dose by deformable image registration and doses summed by each treatment plan, composite dose by deformable image registration may generate more exact evaluation to surrounding normal tissue in adaptive radiotherapy.

• 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.