Evaluation of deformation parameters for deformable image registration-based ventilation imaging using an air-ventilating non-rigid phantom

Impact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom

The purpose of this study was to evaluate the deformable image registration (DIR) accuracy using various CT scan parameters with deformable thorax phantom. Our developed deformable thorax phantom (Dephan, Chiyoda Technol Corp, Tokyo, Japan) was used. The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180 mm, which simulates the chest wall. The inner phantom consists of deformable, 20 mm thick disk-shaped sponges with 48 Lucite beads and 48 nylon cross-wires which simulate the vascular and bronchial bifurcations of the lung. Peak-exhale and peak-inhale images of the deformable phantom were acquired using a CT scanner (Aquilion LB, TOSHIBA). To evaluate the impact of CT scan parameters on DIR accuracy, we used the four tube voltages (80, 100, 120, and 135 kV) and six reconstruction algorithms (FC11, FC13, FC15, FC41, FC44, and FC52). Intensity-based DIR was performed between the two images using MIM Maestro (MIM software, Cleveland, USA). Fiducial markers (beads and cross-wires) based target registration error (TRE) was used for quantitative evaluation of DIR. In case with different tube voltages, the range of average TRE were 4.44-5.69 mm (reconstruction algorithm: FC13). In case with different reconstruction algorithms, the range of average TRE were 4.26-4.59 mm (tube voltage: 120 kV). The TRE were differed by up to 3.0 mm (3.96-6.96 mm) depending on the combination of tube voltage and reconstruction algorithm. Our result indicated that CT scan parameters had moderate impact of TRE, especially for reconstruction algorithms for the deformable thorax phantom.

Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

Development of a dynamic deformable thorax phantom for the quality management of deformable image registration

The purpose of this study was to develop a novel dynamic deformable thorax phantom for deformable image registration (DIR) quality assurance (QA) and to verify as a tool for commissioning and DIR QA. The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180 mm. The inner phantom consists of deformable, 20 mm thick disk-shaped sponges. To evaluate the physical characteristics of the phantom, we evaluated its image quality and deformation. DIR accuracies were evaluated using the three types of commercially DIR software (MIM, RayStation, and Velocity AI) to test the feasibility of this phantom. We used different DIR parameters to test the impact of parameters on DIR accuracy in various phantom settings. To evaluate DIR accuracy, a target registration error (TRE) was calculated using the anatomical landmark points. The three locations (i.e., distal, middle, and proximal positions) had different displacement amounts. This result indicated that the inner phantom was not moved but deformed. In cases with different phantom settings and marker settings, the ranges of the average TRE were 0.63-15.60 mm (MIM). In cases with different DIR parameters settings, the ranges of the average TRE were as follows: 0.73-7.10 mm (MIM), 8.25-8.66 mm (RayStation), and 8.26-8.43 mm (Velocity). These results suggest that our phantom could evaluate the detailed DIR behaviors with TRE. Therefore, this is indicative of the potential usefulness of our phantom in DIR commissioning and QA.

Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom

We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VI_CBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VI_CBCT- and 4D-CT-based ventilation images (VI_CT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VI_CBCT. Correlations between the VI_CBCT and VI_CT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VI_CBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VI_CBCT was greater or equal to that of the HU-based VI_CBCT. In addition, the ventilation accuracy of VI_CBCT increased at low gantry speeds. Thus, the image quality of VI_CBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VI_CBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VI_CBCT imaging into the functional avoidance planning process.

Multimodal imaging in radiotherapy: Focus on adaptive therapy and quality control

Improved computer resources in radiation oncology department have greatly facilitated the integration of multimodal imaging into the workflow of radiation therapy. Nowadays, physicians have highly informative imaging modalities of the anatomical region to be treated. These images contribute to the targeting accuracy with the current treatment device, impacting both segmentation or patient's positioning. Additionally, in a constant effort to deliver personalized care, many teams seek to confirm the benefits of adaptive radiotherapy. The published works highlight the importance of registration algorithms, particularly those of elastic or deformable registration necessary to take into account the anatomical evolutions of the patients during the course of their therapy. These algorithms, often considered as "black boxes", tend to be better controlled and understood by physicists and physicians thanks to the generalization of evaluation and validation methods. Given the still significant development of medical imaging techniques, it is foreseeable that multimodal registration needs require more efficient algorithms well integrated within the flow of data.

Free-to-use DIR solutions in radiotherapy: Benchmark against commercial platforms through a contour-propagation study

PURPOSE

A contour propagation study has been conducted to benchmark three algorithms for Deformable Image Registration (DIR) freely available online against well-established commercial solutions.

METHODS

ElastiX, BRAINS and Plastimach, available as modules in the open source platform 3DSlicer, were tested as the recent AAPM Task group 132 guidelines proposes. The overlap of the DIR-mapped ROIs in four computational anthropomorphic phantoms was measured. To avoid bias every algorithm was left to run without any human interaction nor particular registration strategy. The accuracy of the algorithms was measured using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. The registration quality was compared to the recommended geometrical accuracy suggested by AAPM TG132 and to the results of a large population-based study performed with commercial DIR solutions.

RESULTS

The considered free-to-use DIR solutions generally meet acceptable accuracy and good overlap (DSC > 0.85). Mild failures (DSC < 0.75) were detected only for the smallest structures. In case of extremely severe deformations acceptable accuracy was not met (MDC > 3 mm). The morphing capability of the tested algorithms equals those of commercial systems when the user interaction is avoided. Underperformances were detected only in cases where a specific registration strategy is mandatory to obtain a satisfying match.

CONCLUSIONS

All of the considered algorithms show performances not inferior to previously published data and have the potential to be good candidates for use in the clinical routine. The results and conclusions only apply to the considered phantoms and should not be considered to be generally applicable and extendable to patient cases.

Factual survey of the clinical use of deformable image registration software for radiotherapy in Japan

Deformable image registration (DIR) has recently become commercially available in the field of radiotherapy. However, there was no detailed information regarding the use of DIR software at each medical institution. Thus, in this study, we surveyed the status of the clinical use of DIR software for radiotherapy in Japan. The Japan Society of Medical Physics and the Japanese Society for Radiation Oncology mailing lists were used to announce this survey. The questionnaire was created by investigators working under the research grant of the Japanese Society for Radiation Oncology (2017-2018) and intended for the collection of information regarding the use of DIR in radiotherapy. The survey was completed by 161 institutions in Japan. The survey results showed that dose accumulation was the most frequent purpose for which DIR was used in clinical practice (73%). Various commissioning methods were performed, although they were not standardized. Qualitative evaluation with actual patient images was the most commonly used method (28%), although 30% of the total number of responses (42% of institutions) reported that they do not perform commissioning. We surveyed the current status of clinical use of DIR software for radiotherapy in Japan for the first time. Our results indicated that a certain number of institutions used DIR software for clinical practice, and various commissioning methods were performed, although they were not standardized. Taken together, these findings highlight the need for a technically unified approach for commissioning and quality assurance for the use of DIR software in Japan.

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.