A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Fully Automatic Danger Zone Determination for SBRT in NSCLC

Lung cancer is the major cause of cancer death worldwide. The most common form of lung cancer is non-small cell lung cancer(NSCLC). Stereotactic body radiation therapy (SBRT) has emerged as a good alternative to surgery in patients with peripheralstage I NSCLC, demonstrating favorable tumor control and low toxicity. Due to spatial relationship to several critical organs atrisk, SBRT of centrally located lesions is associated with more severe toxicity and requires modification in dose application andfractionation, which is currently evaluated in clinical trials. Therefore a classification of lung tumors into central or peripheralis required. In this work we present a novel, highly versatile, mulitmodality tool for tumor classification which requires no userinteraction. Furthermore the tool can automatically segment the trachea, proximal bronchial tree, mediastinum, gross target volumeand internal target volume. The proposed work is evaluated on 19 cases with different image modalities assessing segmentationquality as well as classification accuracy. Experiments showed a good segmentation quality and a classification accuracy of 95 %.These results suggest the use of the proposed tool for clinical trials to assist clinicians in their work and to fasten up the workflowin NSCLC patients treatment

Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT

Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures

Automatic 3D extraction of pleural plaques and diffuse pleural thickening from lung MDCT images

Pleural plaques (PPs) and diffuse pleural thickening (DPT) are very common asbestos related pleural diseases (ARPD). They are currently identified non-invasively using medical imaging techniques. A fully automatic algorithm for 3D detection of calcified pleura in the diaphragmatic area and thickened pleura on the costal surfaces from multi detector computed tomography (MDCT) images has been developed and tested. The algorithm for detecting diaphragmatic pleura includes estimation of the diaphragm top surface in 3D and identifying those voxels at a certain vertical distance from the estimated diaphragm, and with intensities close to that of bone, as calcified pleura. The algorithm for detecting thickened pleura on the costal surfaces includes: estimation of the pleural costal surface in 3D, estimation of the centrelines of ribs and costal cartilages and the surfaces that they lie on, calculating the mean distance between the two surfaces, and identifying any space between the two surfaces whose distance exceeds the mean distance as thickened pleura. The accuracy and performance of the proposed algorithm was tested on 20 MDCT datasets from patients diagnosed with existing PPs and/or DPT and the results were compared against the ground truth provided by an experienced radiologist. Several metrics were employed and evaluations indicate high performance of both calcified pleura detection in the diaphragmatic area and thickened pleura on the costal surfaces. This work has made significant contributions to both medical image analysis and medicine. For the first time in medical image analysis, the approach uses other stable organs such as the ribs and costal cartilage, besides the lungs themselves, for referencing and landmarking in 3D. It also estimates fat thickness between the rib surface and pleura (which is usually very thin) and excludes it from the detected areas, when identifying the thickened pleura. It also distinguishes the calcified pleura attached to the rib(s), separates them in 3D and detects calcified pleura on the lung diaphragmatic surfaces. The key contribution to medicine is effective detection of pleural thickening of any size and recognition of any changes, however small. This could have a significant impact on managing patient risks

Virtual Reality Simulation of Liver Biopsy with a Respiratory Component

International audienceThe field of computer-based simulators has grown exponentially in the last few decades, especially in Medicine. Advantages of medical simulators include: (1) provision of a platform where trainees can practice procedures without risk of harm to patients; (2) anatomical fidelity; (3) the ability to train in an environment wherein physiological behaviour is observed, something that is not permitted where in-vitro phantoms are used; (4) flexibility regarding anatomical and pathological variation of test cases that is valuable in the acquisition of experience; (5) quantification of metrics relating to task performance that can be used to monitor trainee performance throughout the learning curve; and (6) cost effectiveness. In this chapter, we will focus on the current state of the art of medical simulators, the relevant parameters required to design a medical simulator, the basic framework of the simulator, methods to produce a computer-based model of patient respiration and finally a description of a simulator for ultrasound guided for liver biopsy. The model that is discussed presents a framework that accurately simulates respiratory motion, allowing for the fine tuning of relevant parameters in order to produce a patient-specific breathing pattern that can then be incorporated into a simulation with real-rime haptic interaction. Thus work was conducted as part CRaIVE collaboration [1], whose aim is to develop simulators specific to interventional radiology