Evaluation of the performance of deformable image registration between planning CT and CBCT images for the pelvic region: comparison between hybrid and intensity-based DIR

Takayama, Yoshiki; Yamamoto, Takaya; Ito, Kengo; Chiba, Masanori; Fujiwara, K.; Miyasaka, Yoshinori; Dobashi, Suguru; Sato, Kiyokazu; Takeda, Ken; Jingu, Keiichi

doi:10.1093/jrr/rrw123

Cited by 35 publications

(29 citation statements)

References 32 publications

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“…However, deformation and elimination of some structures (e.g., small intestines) were seen on the corresponding i-CBCT slice for some cases as shown in Figure 5 , which may result in the registration error in IGRT. One potential approach to better preserve the anatomical structures on CBCT may be to improve the CBCT/pCT alignment of the training data (e.g., hybrid DIR [ 28 ]). Our future study will demonstrate the correlation between the alignment of the training data and the performance of the network to preserve the anatomical structures on CBCT.…”

Section: Discussionmentioning

confidence: 99%

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

Kida¹,

Nakamoto²,

Nakano³

et al. 2018

Cureus

107

150

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IntroductionCone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality.MethodsCBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCTr). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTr images. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCTr using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM).ResultsThe image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method.ConclusionWe have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner.

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Section: Discussionmentioning

confidence: 99%

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

Kida¹,

Nakamoto²,

Nakano³

et al. 2018

Cureus

107

150

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“…Currently, organ segmentation is classically performed by deformable image registration (DIR) algorithms between the planning CT and daily CBCT scans [7,8]. These algorithms include such clinical software packages as MIM [9] and RayStation [10]. Although the results are better than those of rigid registration, these intensity-based DIR algorithms fail in the presence of large deformations between the registered scans, as is the case in the pelvic region [11,12].…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Data Augmentation for Deep-Learning-Based Male Pelvic Organ Segmentation in Cone Beam CT

et al. 2020

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For prostate cancer patients, large organ deformations occurring between radiotherapy treatment sessions create uncertainty about the doses delivered to the tumor and surrounding healthy organs. Segmenting those regions on cone beam CT (CBCT) scans acquired on treatment day would reduce such uncertainties. In this work, a 3D U-net deep-learning architecture was trained to segment bladder, rectum, and prostate on CBCT scans. Due to the scarcity of contoured CBCT scans, the training set was augmented with CT scans already contoured in the current clinical workflow. Our network was then tested on 63 CBCT scans. The Dice similarity coefficient (DSC) increased significantly with the number of CBCT and CT scans in the training set, reaching 0.874 ± 0.096 , 0.814 ± 0.055 , and 0.758 ± 0.101 for bladder, rectum, and prostate, respectively. This was about 10% better than conventional approaches based on deformable image registration between planning CT and treatment CBCT scans, except for prostate. Interestingly, adding 74 CT scans to the CBCT training set allowed maintaining high DSCs, while halving the number of CBCT scans. Hence, our work showed that although CBCT scans included artifacts, cross-domain augmentation of the training set was effective and could rely on large datasets available for planning CT scans.

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“…Visual inspection yielded a registration uncertainty of 1, indicating that localization was appropriate provided the target was in the locally aligned region . Calculation of the cumulative dose based on the hybrid DIR of RayStation may be very accurate even when there is organ movement . Thus, we considered dose accumulation calculated by the DIR to be reliable.…”

Section: Discussionmentioning

confidence: 99%

Variation in accumulated dose of volumetric‐modulated arc therapy for pancreatic cancer due to different beam starting phases

Sasaki

Nakamura

Mukumoto

et al. 2019

J Applied Clin Med Phys

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Purpose: To assess the effects of different beam starting phases on dosimetric variations in the clinical target volume (CTV) and organs at risk (OARs), and to identify the relationship between plan complexity and the dosimetric impact of interplay effects in volumetric-modulated arc therapy (VMAT) plans for pancreatic cancer.Methods: Single and double full-arc VMAT plans were generated for 11 patients. A dose of 50.4 Gy in 28 fractions was prescribed to cover 50% of the planning target volume. Patient-specific Digital Imaging and Communications in Medicine-Radiation Therapy planfiles were divided into 10 files based on the respiratory phases in four-dimensional computed tomography (4DCT) simulations. The phase-divided VMAT plans were calculated in consideration of the beam starting phase for each arc and were then combined in the mid-ventilation phase of 4DCT (4D plans). The dose-volumetric parameters were compared with the calculated dose distributions without consideration of the interplay effects (3D plans). Additionally, relationships among plan parameters such as modulation complexity scores, monitor units (MUs), and dose-volumetric parameters were evaluated.Results: Dosimetric differences in the median values associated with different beam starting phases were within ± 1.0% and ± 0.2% for the CTV and ± 0.5% and ± 0.9% for the OARs during single and double full-arc VMAT, respectively. Significant differences caused by variations in the beam starting phases were observed only for the dose-volumetric parameters of the CTV during single full-arc VMAT (P < 0.05), associated with moderate or strong correlations between the MUs and the dosimetric differences between the 4D and 3D plans. Conclusions: The beam starting phase affected CTV dosimetric variations of single full-arc VMAT. The use of double full-arc VMAT mitigated this problem. However, variation in the dose delivered to OARs was not dependent on the beam starting phase, even for single full-arc VMAT. K E Y W O R D S beam starting phase, DICOM-RT plan file, interplay effect, pancreatic cancer, VMAT

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Evaluation of the performance of deformable image registration between planning CT and CBCT images for the pelvic region: comparison between hybrid and intensity-based DIR

Cited by 35 publications

References 32 publications

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

Cross-Domain Data Augmentation for Deep-Learning-Based Male Pelvic Organ Segmentation in Cone Beam CT

Variation in accumulated dose of volumetric‐modulated arc therapy for pancreatic cancer due to different beam starting phases

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