Automated analysis and visualization of preclinical whole-body microCT data

In this thesis, several strategies are presented that aim to facilitate the analysis and visualization of whole-body in vivo data of small animals. Based on the particular challenges for image processing, when dealing with whole-body follow-up data, we addressed several aspects in this thesis. The developed methods are tailored to handle data of subjects with significantly varying posture and address the large tissue heterogeneity of entire animals. In addition, we aim to compensate for lacking tissue contrast by relying on approximation of organs based on an animal atlas. Beyond that, we provide a solution to automate the combination of multimodality, multidimensional data.* Advanced School for Computing and Imaging (ASCI), Delft, NL * Bontius Stichting inz Doelfonds Beeldverwerking, Leiden, NL * Caliper Life Sciences, Hopkinton, USA * Foundation Imago, Oegstgeest, NLUBL - phd migration 201

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

Modelling and verification of doses delivered to deformable moving targets in radiotherapy

During the last two decades, advanced treatment techniques have been developed in radiotherapy to achieve more conformal beam targeting of cancerous lesions. The advent of these techniques, such as intensity modulated radiotherapy (IMRT), volumetric modulated arc radiothreapy (VMAT), Tomotherapy etc., allows more precise localisation of higher doses to complex-shaped target volumes, thereby sparing more healthy tissue. In this context, motion management is a critical issue in contemporary radiotherapy (RT). That anatomic structures move during respiration is well known and much research is presently being devoted to strategies to contend with organ motion. However, moving structures are typically regarded as rigid bodies. The fact that many structures deform as a result of motion makes their resultant dose distributions difficult to measure and calculate, and has not been fully accounted for. The potential for ineffective treatments that do not take into account motion and anatomic deformation is self-evident. This thesis addresses the pressing need to investigate dose distributions in targets that deform during and/or between treatments, to ensure robust calculations for dose accumulation and delivery, thus providing the most positive outcomes for patients. This involves the direct measurement of complex and re-distributed dose in deforming objects (an experimental model), as well as calculations of the deformed dose distribution (a mathematical model). The comparison thereof aims to validate the dose deformation technique, thereby to apply the method to a clinical example such as liver stereotactic body radiotherapy. To facilitate four-dimensional deformable dosimetry for both external beam radiotherapy and brachytherapy, methodologies for three-dimensional deformed dose measurements were developed and employed using radiosensitive polymer gel combined with a cone beam optical computed tomography (CT) scanner. This includes the development of a novel prototype deformable target volume using a tissue-equivalent, deformable gel dosimetric phantom, dubbed “defgel”. This can reproducibly simulate targets subject to a range of mass- and density-conserving deformations representative of those observable in anatomical targets. This novel tool was characterised in terms of its suitability for the measurement of dose in deforming geometries. It was demonstrated that planned doses could be delivered to the deformable gel dosimeter in the presence of different deformations and complex spatial re-distributions of dose in all three dimensions could be quantified. For estimating the cumulative dose in different deformed states, deformable image registration (DIR) algorithms were implemented to ‘morph’ a dose distribution calculated by a treatment planning system. To investigate the performance of DIR and dose-warping technique, two key studies were undertaken. The first was to systematically assess the accuracy of a range of different DIR algorithms available in the public domain and quantitatively examine, in particular, low-contrast regions, where accuracy had not previously been established. This work investigates DIR algorithms in 3D via a systematic evaluation process using defgel suitable for verification of mass- and density-conserving deformations. The second study was a full three-dimensional experimental validation of the dose-warping technique using the evaluated DIR algorithm and comparing it to directly measured deformed dose distributions from defgel. It was shown that the dose-warping can be accurate, i.e. over 95% passing rate of 3D-gamma analysis with 3%/3mm criteria for given extents of deformation up to 20 mm For the application of evaluating patient treatment planning involving tumour motion/deformation, two key studies were undertaken in the context of liver stereotactic body radiotherapy. The first was a 4D evaluation of conventional 3D treatment planning, combined with 4D computed tomography, in order to investigate the extent of dosimetric differences between conventional 3D-static and path-integrated 4D-cumulative dose calculation. This study showed that the 3D planning approach overestimated doses to targets by ≤ 9% and underestimated dose to normal liver by ≤ 8%, compared to the 4D methodology. The second study was to assess a consequent reduction of healthy tissue sparing, which may increase risk for surrounding healthy tissues. Estimates for normal tissue complications probabilities (NTCP) based on the two dose calculation schemes are provided. While all NTCP were low for the employed fractionation scheme, analysis of common alternative schemes suggests potentially larger uncertainties exist in the estimation of NTCP for healthy liver and that substantial differences in these values may exist across the different fractionation schemes. These bodies of work have shown the potential to quantify such issues of under- and/or over-dosages which are quite patient dependent in RT. Studies presented in this work consolidate gel dosimetry, image guidance, DIR, dose-warping and consequent dose accumulation calculation to investigate the dosimetric impact and make more accurate evaluation of conventional 3D treatment plans. While liver stereotactic body radiotherapy (SBRT) was primarily concerned for immediate clinical application, the findings of this thesis are also applicable to other organs with various RT techniques. Most importantly, however, it is hoped that the outcomes of this thesis will help to improve treatment plan accuracy. By considering both computation and measurement, it is also hoped that this work will open new windows for future work and hence provide building blocks to further enhance the benefit of radiotherapy treatment

Automatic analysis of medical images for change detection in prostate cancer

Prostate cancer is the most common cancer and second most common cause of cancer death in men in the UK. However, the patient risk from the cancer can vary considerably, and the widespread use of prostate-specific antigen (PSA) screening has led to over-diagnosis and over-treatment of low-grade tumours. It is therefore important to be able to differentiate high-grade prostate cancer from the slowly- growing, low-grade cancer. Many of these men with low-grade cancer are placed on active surveillance (AS), which involves constant monitoring and intervention for risk reclassification, relying increasingly on magnetic resonance imaging (MRI) to detect disease progression, in addition to TRUS-guided biopsies which are the routine clinical standard method to use. This results in a need for new tools to process these images. For this purpose, it is important to have a good TRUS-MR registration so corresponding anatomy can be located accurately between the two. Automatic segmentation of the prostate gland on both modalities reduces some of the challenges of the registration, such as patient motion, tissue deformation, and the time of the procedure. This thesis focuses on the use of deep learning methods, specifically convolutional neural networks (CNNs), for prostate cancer management. Chapters 4 and 5 investigated the use of CNNs for both TRUS and MRI prostate gland segmentation, and reported high segmentation accuracies for both, Dice Score Coefficients (DSC) of 0.89 for TRUS segmentations and DSCs between 0.84-0.89 for MRI prostate gland segmentation using a range of networks. Chapter 5 also investigated the impact of these segmentation scores on more clinically relevant measures, such as MRI-TRUS registration errors and volume measures, showing that a statistically significant difference in DSCs did not lead to a statistically significant difference in the clinical measures using these segmentations. The potential of these algorithms in commercial and clinical systems are summarised and the use of the MRI prostate gland segmentation in the application of radiological prostate cancer progression prediction for AS patients are investigated and discussed in Chapter 8, which shows statistically significant improvements in accuracy when using spatial priors in the form of prostate segmentations (0.63 ± 0.16 vs. 0.82 ± 0.18 when comparing whole prostate MRI vs. only prostate gland region, respectively)

Feature extraction to aid disease detection and assessment of disease progression in CT and MR colonography

Computed tomographic colonography (CTC) is a technique employed to examine the whole colon for cancers and premalignant adenomas (polyps). Oral preparation is taken to fully cleanse the colon, and gas insufflation maximises the attenuation contrast between the enoluminal colon surface and the lumen. The procedure is performed routinely with the patient both prone and supine to redistribute gas and residue. This helps to differentiate fixed colonic pathology from mobile faecal residue and also helps discover pathology occluded by retained fluid or luminal collapse. Matching corresponding endoluminal surface locations with the patient in the prone and supine positions is therefore an essential aspect of interpretation by radiologists; however, interpretation can be difficult and time consuming due to the considerable colonic deformations that occur during repositioning. Hence, a method for automated registration has the potential to improve efficiency and diagnostic accuracy. I propose a novel method to establish correspondence between prone and supine CT colonography acquisitions automatically. The problem is first simplified by detecting haustral folds which are elongated ridgelike endoluminal structures and can be identified by curvature based measurements. These are subsequently matched using appearance based features, and their relative geometric relationships. It is shown that these matches can be used to find correspondence along the full length of the colon, but may also be used in conjunction with other registration methods to achieve a more robust and accurate result, explicitly addressing the problem of colonic collapse. The potential clinical value of this method has been assessed in an external clinical validation, and the application to follow-up CTC surveillance has been investigated. MRI has recently been applied as a tool to quantitatively evaluate the therapeutic response to therapy in patients with Crohn's disease, and is the preferred choice for repeated imaging. A primary biomarker for this evaluation is the measurement of variations of bowel wall thickness on changing from the active phase of the disease to remission; however, a poor level of interobserver agreement of measured thickness is reported and therefore a system for accurate, robust and reproducible measurements is desirable. I propose a novel method which will automatically track sections of colon, by estimating the positions of elliptical cross sections. Subsequently, estimation of the positions of the inner and outer bowel walls are made based on image gradient information and therefore a thickness measurement value can be extracted

Ultrasound Elastography

The comparison between methods, evaluation of portal hypertension and many other questions are still open issues in liver elastography. New elastographic applications are under evaluation and close to being used in clinical practice. Strain imaging has been incorporated into many disciplines and EFSUMB guidelines are under preparation. More research is necessary for improved evidence for clinical applications in daily practice. The Special Issue published papers on recent advances in development and application of Ultrasound Elastography