Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study

Loi, G.; Fusella, M.; Vecchi, C.; Menna, S.; Rosica, F.; Gino, Eva; Maffei, N.; Menghi, E.; Savini, A.; Roggio, A.; Radici, Lorenzo; Cagni, E.; Lucio, Francesco; Strigari, Lidia; Strolin, Silvia; Garibaldi, Cristina; Romanò, Chiara; Piovesan, Marina; Franco, Pierfrancesco; Fiandra, C.

doi:10.1016/j.prro.2019.11.011

Cited by 12 publications

(9 citation statements)

References 46 publications

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“…This survey data agrees with the Japanese data in 2018 15 which found single modality DIR (CT‐CT) as the most common DIR image modality pair. Lower DIR usage of CT‐CBCT usage could correspond to additional complexities such as image noise and streaking 32,33 for contour propagation, or image truncation 32,34 for synthetic CT generation 35 which may creep into the implementation of adaptive radiotherapy 24,36,37 …”

Section: Discussionmentioning

confidence: 99%

“…8 In scenarios where DIR is challenging (e.g., tissue appearance or disappearance 6,31 or large deformations 6 ), accurate use requires appropriate management of uncertainties. 4 Data on the use of RIR with multi-modality imaging show the rel- noise and streaking 32,33 for contour propagation, or image truncation 32,34 for synthetic CT generation 35 which may creep into the implementation of adaptive radiotherapy. 24,36,37 Data indicate that more centers have independent DIR software Three different approaches of evaluating IR processes (process satisfaction, staff involvement, key challenges) found that key concerns were registration accuracy classification (satisfactory or not) with appropriate follow-up use.…”

Section: Dir Software Type Dir Software Websitementioning

confidence: 99%

“…The most common quantitative metrics were target registration error and mean distance to agreement (INTL 14% each), followed by dice similarity coefficient (INTL 11%), Jacobian (INTL 8%), and consistency (INTL 7%). Evaluation of image registrationprocess by relative measures of satisfaction (below 1 deemed unsatisfactoryleft side) and staff involvement (below 1 on average having some processes without staff involvement right side); processes grouped by (i) upstream (1-9), (ii) registration(10)(11)(12)(13)(14)(15)(16)(17), (iii) downstream(18)(19)(20)(21)(22)(23)(24)(25), and (iv) management(26)(27)(28)(29)(30)(31)(32).…”

mentioning

confidence: 99%

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An international survey on the clinical use of rigid and deformable image registration in radiotherapy

Yuen

Barber

Ralston

et al. 2020

J Applied Clin Med Phys

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Objectives: Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration. Methods: A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018. Results: There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multimodality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively). Conclusions: This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlasbased segmentation.

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Section: Discussionmentioning

confidence: 99%

Section: Dir Software Type Dir Software Websitementioning

confidence: 99%

mentioning

confidence: 99%

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An international survey on the clinical use of rigid and deformable image registration in radiotherapy

Yuen

Barber

Ralston

et al. 2020

J Applied Clin Med Phys

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“…Other multi-institution studies that have evaluated commercial algorithms include those published by Loi et al 12,13 and Kadoya et al 14 The first study published by Loi et al assessed submissions from 13 centers using six different commercial DIR platforms, including RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM, Velocity, Eclipse, Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). 12 That investigation evaluated three virtual phantoms representing the head and neck region, pelvis, and thorax.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have quantified DIR accuracy from different commercial or research algorithms, using data submitted by multiple institutions. This has been done using either contour-based 12,13 or landmark-based 14,15 analysis. Contour-based methods can be subjective, as they introduce variability based on the observer drawing the contours and contain little information about the accuracy of voxels within the contour.…”

Section: Introductionmentioning

confidence: 99%

Variability in commercially available deformable image registration: A multi‐institution analysis using virtual head and neck phantoms

Kubli

Pukala

Shah

et al. 2021

J Applied Clin Med Phys

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Purpose: The purpose of this study was to evaluate the performance of three common deformable image registration (DIR) packages across algorithms and institutions. Methods and Materials:The Deformable Image Registration Evaluation Project (DIREP) provides ten virtual phantoms derived from computed tomography (CT) datasets of head-and-neck cancer patients over a single treatment course. Using the DIREP phantoms, DIR results from 35 institutions were submitted using either Velocity, MIM, or Eclipse. Submitted deformation vector fields (DVFs) were compared to ground-truth DVFs to calculate target registration error (TRE) for six regions of interest (ROIs). Statistical analysis was performed to determine the variability between each DIR software package and the variability of users within each algorithm.Results: Overall mean TRE was 2.04 ± 0.35 mm for Velocity, 1.10 ± 0.29 mm for MIM, and 2.35 ± 0.15 mm for Eclipse. The MIM mean TRE was significantly different than both Velocity and Eclipse for all ROIs. Velocity and Eclipse mean TREs were not significantly different except for when evaluating the registration of the cord or mandible. Significant differences between institutions were found for the MIM and Velocity platforms. However, these differences could be explained by variations in Velocity DIR parameters and MIM software versions.Conclusions: Average TRE was shown to be <3 mm for all three software platforms. However, maximum errors could be larger than 2 cm indicating that care should be exercised when using DIR. While MIM performed statistically better than the other packages, all evaluated algorithms had an average TRE better than the largest voxel dimension. For the phantoms studied here, significant differences between algorithm users were minimal suggesting that the algorithm used may have more impact on DIR accuracy than the particular registration technique employed. A significant difference in TRE was discovered between MIM versions showing that DIR QA should be performed after software upgrades as recommended by TG-132.

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Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

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Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

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Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study

Cited by 12 publications

References 46 publications

An international survey on the clinical use of rigid and deformable image registration in radiotherapy

An international survey on the clinical use of rigid and deformable image registration in radiotherapy

Variability in commercially available deformable image registration: A multi‐institution analysis using virtual head and neck phantoms

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

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