Registration methods in radiotherapy

Purpose

The aim of this study is to present a short and comprehensive review of the methods of medical image registration, their conditions and applications in radiotherapy. A particular focus was placed on the methods of deformable image registration.

Methods

To structure and deepen the knowledge on medical image registration in radiotherapy, a medical literature analysis was made using the Google Scholar browser and the medical database of the PubMed library.

Results

Chronological review of image registration methods in radiotherapy based on 34 selected articles. A particular attention was given to show: (i) potential regions of the application of different methods of registration, (ii) mathematical basis of the deformable methods and (iii) the methods of quality control for the registration process.

Conclusions

The primary aim of the medical image registration process is to connect the contents of images. What we want to achieve is a complementary or extended knowledge that can be used for more precise localisation of pathogenic lesions and continuous improvement of patient treatment. Therefore, the choice of imaging mode is dependent on the type of clinical study. It is impossible to visualise all anatomical details or functional changes using a single modality machine. Therefore, fusion of various modality images is of great clinical relevance. A natural problem in analysing the fusion of medical images is geographical errors related to displacement. The registered images are performed not at the same time and, very often, at different respiratory phases.

The tumor shape changes of nasopharyngeal cancer during chemoradiotherapy: the estimated margin to cover the geometrical variation

BACKGROUND

Considerable geometrical change occurs during chemoradiotherapy (CRT) course of nasopharyngeal carcinoma (NPC). This aim of this study was to quantify the volumetric and surface variability of the target volumes (TV) and to estimate the expanded margin to maintain acceptable geometrical coverage.

METHODS

Twenty patients with locally advanced nasopharyngeal cancer underwent one planning CT (pCT) and six weekly repeated CT (rCT) scans during the treatment course of definitive CRT. The TV included the gross tumor volume (GTV) of the primary tumor, large (shortest diameter >3.0 cm) and small (diameter >1 cm and ≤3 cm) positive neck lymph nodes, and low-risk clinical target volume (CTV_Lr) that were delineated manually on the pCT and each rCT. When comparing TV in pCT (V_pCT) and TV in rCT (V_rCT), the overlapping index (OI), Dice similarity coefficient (DSC), shortest perpendicular distance (SPD), and overall standard deviation (overall SD) were calculated to present the geometric changes. An isotropical margin was expanded outward around CTV_Lr in pCT to establish the mimic planning target volume (PTV). An OI ≥0.95 was defined as acceptable geometrical coverage.

RESULTS

For all TV, DSCs decreased, and the SPDs and overall SD increased with the increasing number of fractions delivered. The DSCs of all gross TV were <70% after the third week. The mean SPDs were 1.5-2.5 mm in the first week and 5.2-6.2 mm in the last week. The OI and DSC in concurrent CRT were smaller than those in the sequential therapy; and similarly the SPD and overall SD in the concurrent therapy were larger than those in the sequential one. To maintain >95% geometrical coverage, a 2-mm additional margin could maintain the coverage throughout the treatment course and a 1-mm margin could maintain the desired coverage if there is an adaptive re-planning no later than the third week of the treatment course.

CONCLUSIONS

Both volumetric coverage and surface of the tumour underwent the progressive changes during the treatment course of CRT. One to two mm as the expanded margin to establish the PTV is required to maintain >95% geometrical coverage.

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

PURPOSE

Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.

METHODS

1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(®) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.

RESULTS

Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.

CONCLUSIONS

Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints.

Impact of the Intra- and Inter-observer Variability in the Delineation of Parotid Glands on the Dose Calculation During Head and Neck Helical Tomotherapy

The intra- and inter-observer variability in delineation of the parotids on the kilo-voltage computed tomography (kVCT) and mega-voltage computed tomography (MVCT) were examined to establish their impact on the dose calculation during adaptive head and neck helical tomotherapy (HT). Three observers delineated left and right parotids for ten randomly selected patients with oropharynx cancer treated on HT. The pre-treatment kVCT and the MVCT from the first fraction of irradiation were selected to delineation. The delineation procedure was repeated three times by each observer. The parotids were delineated according to the institutional protocol. The analyses included intra-observer reproducibility and inter-structure, -observer and -modality variability of the volume and dose. The differences between the left and right parotid outlines were not statistically significant (p > 0.3). The reproducibility of the delineation was confirmed for each observer on the kVCT (p > 0.2) and on the MVCT (p > 0.1). The inter-observer variability of the outlines was significant (p < 0.001) as well as the inter-modality variability (p < 0.006). The parotids delineated on the MVCT were 10% smaller than on the kVCT. The inter-observer variability of the parotids delineation did not affect the average dose (p = 0.096 on the kVCT and p = 0.176 on the MVCT). The dose calculated on the MVCT was higher by 3.3% than dose from the kVCT (p = 0.009). Usage of the institutional protocols for the parotids delineation reduces intra-observer variability and increases reproducibility of the outlines. These protocols do not eliminate delineation differences between the observers, but these differences are not clinically significant and do not affect average doses in the parotids. The volumes of the parotids delineated on the MVCT are smaller than on the kVCT, which affects the differences in the calculated doses.