A comparative evaluation of 3 different free-form deformable image registration and contour propagation methods for head and neck MRI : the case of parotid changes radiotherapy

Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approachesthe commercial MIM, the open-source Elastix software, and an optimized version of it. Materials and Methods: Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. Results: A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. Conclusion: The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019–April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines

The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials

Mesh-to-raster based non-rigid registration of multi-modal images

Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem where the objective consists of a data term, which involves the signed distance function of the ROI from the reference image, and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The ROI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit (ITK) and Elastix

EQUIPMENT TO ADDRESS INFRASTRUCTURE AND HUMAN RESOURCE CHALLENGES FOR RADIOTHERAPY IN LOW-RESOURCE SETTINGS

Millions of people in low- and middle- income countries (LMICs) are without access to radiation therapy and as rate of population growth in these regions increase and lifestyle factors which are indicative of cancer increase; the cancer burden will only rise. There are a multitude of reasons for lack of access but two themes among them are the lack of access to affordable and reliable teletherapy units and insufficient properly trained staff to deliver high quality care. The purpose of this work was to investigate to two proposed efforts to improve access to radiotherapy in low-resource areas; an upright radiotherapy chair (to facilitate low-cost treatment devices) and a fully automated treatment planning strategy. A fixed-beam patient treatment device would allow for reduced upfront and ongoing cost of teletherapy machines. The enabling technology for such a device is the immobilization chair. A rotating seated patient not only allows for a low-cost fixed treatment machine but also has dosimetric and comfort advantages. We examined the inter- and intra- fraction setup reproducibility, and showed they are less than 3mm, similar to reports for the supine position. The head-and-neck treatment site, one of the most challenging treatment planning, greatly benefits from the use of advanced treatment planning strategies. These strategies, however, require time consuming normal tissue and target contouring and complex plan optimization strategies. An automated treatment planning approach could reduce the additional number of medical physicists (the primary treatment planners) in LMICs by up to half. We used in-house algorithms including mutli-atlas contouring and quality assurance checks, combined with tools in the Eclipse Treatment Planning System®, to automate every step of the treatment planning process for head-and-neck cancers. Requiring only the patient CT scan, patient details including dose and fractionation, and contours of the gross tumor volume, high quality treatment plans can be created in less than 40 minutes

Superiority of deformable image co-registration in the integration of diagnostic positron emission tomography-computed tomography to the radiotherapy treatment planning pathway for oesophageal carcinoma

Aims To investigate the use of image co-registration in incorporating diagnostic positron emission tomography-computed tomography (PET-CT) directly into the radiotherapy treatment planning pathway, and to describe the pattern of local recurrence relative to the PET-avid volume. Materials and methods Fourteen patients were retrospectively identified, six of whom had local recurrence. The accuracy of deformable image registration (DIR) and rigid registration of the diagnostic PET-CT and recurrence CT, to the planning CT, were quantitatively assessed by comparing co-registration of oesophagus, trachea and aorta contours. DIR was used to examine the correlation between PET-avid volumes, dosimetry and site of recurrence. Results Positional metrics including the dice similarity coefficient (DSC) and conformity index (CI), showed DIR to be superior to rigid registration in the co-registration of diagnostic and recurrence imaging to the planning CT. For diagnostic PET-CT, DIR was superior to rigid registration in the transfer of oesophagus (DSC = 0.75 versus 0.65, P < 0.009 and CI = 0.59 versus 0.48, P < 0.003), trachea (DSC = 0.88 versus 0.65, P < 0.004 and CI = 0.78 versus 0.51, P < 0.0001) and aorta structures (DSC = 0.93 versus 0.86, P < 0.006 and CI = 0.86 versus 0.76, P < 0.006). For recurrence imaging, DIR was superior to rigid registration in the transfer of trachea (DSC = 0.91 versus 0.66, P < 0.03 and CI = 0.83 versus 0.51, P < 0.02) and oesophagus structures (DSC = 0.74 versus 0.51, P < 0.004 and CI = 0.61 versus 0.37, P < 0.006) with a non-significant trend for the aorta (DSC = 0.91 versus 0.75, P < 0.08 and CI = 0.83 versus 0.63, P < 0.06) structure. A mean inclusivity index of 0.93 (range 0.79–1) showed that the relapse volume was within the planning target volume (PTVPET-CT); all relapses occurred within the high dose region. Conclusion DIR is superior to rigid registration in the co-registration of PET-CT and recurrence CT to the planning CT, and can be considered in the direct integration of PET-CT to the treatment planning process. Local recurrences occur within the PTVPET-CT, suggesting that this is a suitable target for dose-escalation strategies

Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning

Background: The present study aimed to define the optimal number of atlases for automatic multi-atlas-based brachial plexus (BP) segmentation and to compare Simultaneous Truth and Performance Level Estimation (STAPLE) label fusion with Patch label fusion using the ADMIRE (R) software. The accuracy of the autosegmentations was measured by comparing all of the generated autosegmentations with the anatomically validated gold standard segmentations that were developed using cadavers. Materials and methods: Twelve cadaver computed tomography (CT) atlases were used for automatic multiatlas-based segmentation. To determine the optimal number of atlases, one atlas was selected as a patient and the 11 remaining atlases were registered onto this patient using a deformable image registration algorithm. Next, label fusion was performed by using every possible combination of 2 to 11 atlases, once using STAPLE and once using Patch. This procedure was repeated for every atlas as a patient. The similarity of the generated automatic BP segmentations and the gold standard segmentation was measured by calculating the average Dice similarity (DSC), Jaccard (JI) and True positive rate (TPR) for each number of atlases. These similarity indices were compared for the different number of atlases using an equivalence trial and for the two label fusion groups using an independent sample-t test. Results: DSC's and JI's were highest when using nine atlases with both STAPLE (average DSC = 0,532; JI = 0,369) and Patch (average DSC = 0,530; JI = 0,370). When comparing both label fusion algorithms using 9 atlases for both, DSC and JI values were not significantly different. However, significantly higher TPR values were achieved in favour of STAPLE (p < 0,001). When fewer than four atlases were used, STAPLE produced significantly lower DSC, JI and TPR values than did Patch (p = 0,0048). Conclusions: Using 9 atlases with STAPLE label fusion resulted in the most accurate BP autosegmentations (average DSC = 0,532; JI = 0,369 and TPR = 0,760). Only when using fewer than four atlases did the Patch label fusion results in a significantly more accurate autosegmentation than STAPLE