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Kidar HS, Azizi H. Assessing the impact of choosing different deformable registration algorithms on cone-beam CT enhancement by histogram matching. Radiat Oncol 2018; 13:217. [PMID: 30404657 PMCID: PMC6223042 DOI: 10.1186/s13014-018-1162-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 10/22/2018] [Indexed: 11/10/2022] Open
Abstract
Background The aim of this work is to assess the impact of using different deformable registration (DR) algorithms on the quality of cone-beam CT (CBCT) correction with histogram matching (HM). Methods and materials Data sets containing planning CT (pCT) and CBCT images for ten patients with prostate cancer were used. Each pCT image was registered to its corresponding CBCT image using one rigid registration algorithm with mutual information similarity metric (RR-MI) and three DR algorithms with normalized correlation coefficient, mutual information and normalized mutual information (DR-NCC, DR-MI and DR-NMI, respectively). Then, the HM was performed between deformed pCT and CBCT in order to correct the distribution of the Hounsfield Units (HU) in CBCT images. Results The visual assessment showed that the absolute difference between corrected CBCT and deformed pCT was reduced after correction with HM except for soft tissue-air and soft-tissue-bone interfaces due to the improper registration. Furthermore, volumes comparison in terms of average HU error showed that using DR-NCC algorithm with HM yielded the lowest error values of about 55.95 ± 10.43 HU compared to DR-MI and DR-NMI for which the errors were 58.60 ± 10.35 and 56.58 ± 10.51 HU, respectively. Tissue class’s comparison by the mean absolute error (MAE) plots confirmed the performance of DR-NCC algorithm to produce corrected CBCT images with lowest values of MAE even in regions where the misalignment is more pronounced. It was also found that the used method had successfully improved the spatial uniformity in the CBCT images by reducing the root mean squared difference (RMSD) between the pCT and CBCT in fat and muscle from 57 and 25 HU to 8HU, respectively. Conclusion The choice of an accurate DR algorithm before performing the HM leads to an accurate correction of CBCT images. The results suggest that applying DR process based on NCC similarity metric reduces significantly the uncertainties in CBCT images and generates images in good agreement with pCT.
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Affiliation(s)
- Halima Saadia Kidar
- Department of Physics, Ferhat Abbas Setif University, El Bez Compus, 19000, Setif, Algeria.
| | - Hacene Azizi
- Department of Physics, Ferhat Abbas Setif University, El Bez Compus, 19000, Setif, Algeria
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Largent A, Nunes JC, Lafond C, Périchon N, Castelli J, Rolland Y, Acosta O, de Crevoisier R. [MRI-based radiotherapy planning]. Cancer Radiother 2017; 21:788-798. [PMID: 28690126 DOI: 10.1016/j.canrad.2017.02.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/09/2017] [Accepted: 02/27/2017] [Indexed: 12/11/2022]
Abstract
MRI-based radiotherapy planning is a topical subject due to the introduction of a new generation of treatment machines combining a linear accelerator and a MRI. One of the issues for introducing MRI in this task is the lack of information to provide tissue density information required for dose calculation. To cope with this issue, two strategies may be distinguished from the literature. Either a synthetic CT scan is generated from the MRI to plan the dose, or a dose is generated from the MRI based on physical underpinnings. Within the first group, three approaches appear: bulk density mapping assign a homogeneous density to different volumes of interest manually defined on a patient MRI; machine learning-based approaches model local relationship between CT and MRI image intensities from multiple data, then applying the model to a new MRI; atlas-based approaches use a co-registered training data set (CT-MRI) which are registered to a new MRI to create a pseudo CT from spatial correspondences in a final fusion step. Within the second group, physics-based approaches aim at computing the dose directly from the hydrogen contained within the tissues, quantified by MRI. Excepting the physics approach, all these methods generate a synthetic CT called "pseudo CT", on which radiotherapy planning will be finally realized. This literature review shows that atlas- and machine learning-based approaches appear more accurate dosimetrically. Bulk density approaches are not appropriate for bone localization. The fastest methods are machine learning and the slowest are atlas-based approaches. The less automatized are bulk density assignation methods. The physical approaches appear very promising methods. Finally, the validation of these methods is crucial for a clinical practice, in particular in the perspective of adaptive radiotherapy delivered by a linear accelerator combined with an MRI scanner.
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Affiliation(s)
- A Largent
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - J-C Nunes
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - C Lafond
- Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France
| | - N Périchon
- Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France
| | - J Castelli
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - Y Rolland
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Département d'imagerie médicale, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France
| | - O Acosta
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - R de Crevoisier
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France.
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