3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.

Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control

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

Validating and improving CT ventilation imaging by correlating with ventilation 4D-PET/CT using 68Ga-labeled nanoparticles.

PURPOSE: CT ventilation imaging is a novel functional lung imaging modality based on deformable image registration. The authors present the first validation study of CT ventilation using positron emission tomography with (68)Ga-labeled nanoparticles (PET-Galligas). The authors quantify this agreement for different CT ventilation metrics and PET reconstruction parameters. METHODS: PET-Galligas ventilation scans were acquired for 12 lung cancer patients using a four-dimensional (4D) PET/CT scanner. CT ventilation images were then produced by applying B-spline deformable image registration between the respiratory correlated phases of the 4D-CT. The authors test four ventilation metrics, two existing and two modified. The two existing metrics model mechanical ventilation (alveolar air-flow) based on Hounsfield unit (HU) change (VHU) or Jacobian determinant of deformation (VJac). The two modified metrics incorporate a voxel-wise tissue-density scaling (ρVHU and ρVJac) and were hypothesized to better model the physiological ventilation. In order to assess the impact of PET image quality, comparisons were performed using both standard and respiratory-gated PET images with the former exhibiting better signal. Different median filtering kernels (σm = 0 or 3 mm) were also applied to all images. As in previous studies, similarity metrics included the Spearman correlation coefficient r within the segmented lung volumes, and Dice coefficient d20 for the (0 - 20)th functional percentile volumes. RESULTS: The best agreement between CT and PET ventilation was obtained comparing standard PET images to the density-scaled HU metric (ρVHU) with σm = 3 mm. This leads to correlation values in the ranges 0.22 ≤ r ≤ 0.76 and 0.38 ≤ d20 ≤ 0.68, with r = 0.42 ± 0.16 and d20 = 0.52 ± 0.09 averaged over the 12 patients. Compared to Jacobian-based metrics, HU-based metrics lead to statistically significant improvements in r and d20 (p < 0.05), with density scaled metrics also showing higher r than for unscaled versions (p < 0.02). r and d20 were also sensitive to image quality, with statistically significant improvements using standard (as opposed to gated) PET images and with application of median filtering. CONCLUSIONS: The use of modified CT ventilation metrics, in conjunction with PET-Galligas and careful application of image filtering has resulted in improved correlation compared to earlier studies using nuclear medicine ventilation. However, CT ventilation and PET-Galligas do not always provide the same functional information. The authors have demonstrated that the agreement can improve for CT ventilation metrics incorporating a tissue density scaling, and also with increasing PET image quality. CT ventilation imaging has clear potential for imaging regional air volume change in the lung, and further development is warranted

Evolution of surface-based deformable image registration for adaptive radiotherapy of non-small cell lung cancer (NSCLC)

<p>Abstract</p> <p>Background</p> <p>To evaluate the performance of surface-based deformable image registration (DR) for adaptive radiotherapy of non-small cell lung cancer (NSCLC).</p> <p>Methods</p> <p>Based on 13 patients with locally advanced NSCLC, CT images acquired at treatment planning, midway and the end of the radio- (n = 1) or radiochemotherapy (n = 12) course were used for evaluation of DR. All CT images were manually [gross tumor volume (GTV)] and automatically [organs-at-risk (OAR) lung, spinal cord, vertebral spine, trachea, aorta, outline] segmented. Contours were transformed into 3D meshes using the Pinnacle treatment planning system and corresponding mesh points defined control points for DR with interpolation within the structures. Using these deformation maps, follow-up CT images were transformed into the planning images and compared with the original planning CT images.</p> <p>Results</p> <p>A progressive tumor shrinkage was observed with median GTV volumes of 170 cm³(range 42 cm³- 353 cm³), 124 cm³(19 cm³- 325 cm³) and 100 cm³(10 cm³- 270 cm³) at treatment planning, mid-way and at the end of treatment. Without DR, correlation coefficients (CC) were 0.76 ± 0.11 and 0.74 ± 0.10 for comparison of the planning CT and the CT images acquired mid-way and at the end of treatment, respectively; DR significantly improved the CC to 0.88 ± 0.03 and 0.86 ± 0.05 (p = 0.001), respectively. With manual landmark registration as reference, DR reduced uncertainties on the GTV surface from 11.8 mm ± 5.1 mm to 2.9 mm ± 1.2 mm. Regarding the carina and intrapulmonary vessel bifurcations, DR reduced uncertainties by about 40% with residual errors of 4 mm to 6 mm on average. Severe deformation artefacts were observed in patients with resolving atelectasis and pleural effusion, in one patient, where the tumor was located around large bronchi and separate segmentation of the GTV and OARs was not possible, and in one patient, where no clear shrinkage but more a decay of the tumor was observed.</p> <p>Discussion</p> <p>The surface-based DR performed accurately for the majority of the patients with locally advanced NSCLC. However, morphological response patterns were identified, where results of the surface-based DR are uncertain.</p

Personalised Procedures for Thoracic Radiotherapy

This thesis presents the investigation, development, and estimation of two personalised procedures for thoracic cancer therapy in Shenzhen, China and two projects were carried out: (1) respiratory motion management of a lung tumour, and (2) the application of a three-dimensional (3D) printing technique for postmastectomy irradiation. For the first project, all subjects attended sessions of free-breathing (FB) and personalised vocal coaching (VC) for respiratory regulation. Thoracic and abdominal breathing signals were extracted from the subjects’ surface area then estimated as kernel density estimation (KDE) for motion visualisation. The mutual information (MI) and correlation coefficient (CC) calculated from KDEs indicate the variation in the relationship between the two signals. From the 1D signal, through VC, the variation of cycle time and the signal value of end-of-exhale/inhale increased in the patient group but decreased in volunteers. Mixed results were presented on KDE and MI. Compared with FB, VC improves movement consistency between the two signals in eight of eleven subjects by increasing MI. The fixed instruction method showed no improvement for day-to-day variation, while the daily generated instruction enhanced the respiratory regularity in three of five volunteers. VC addresses the variation of the single signal, while the outcome of the two signals, thoracic and abdominal signals, requires further interpretation. The second project aims to address both the enhancement of the skin dose and avoidance of hotspots of critical organs, focusing on improving irradiative treatment for post-mastectomy patients. A 3D-printed bolus was presented as a solution for the air gap between the bolus and skin. The results showed no evidence of significant skin dose enhancement with the printed bolus. Additionally, an air gap larger than 5 mm was evident in all patients. Until a solution for complete bolus adhesion is found, this customised bolus is not suitable for clinical use

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