DEVELOPMENT OF A PATIENT SPECIFIC IMAGE PLANNING SYSTEM FOR RADIATION THERAPY

A patient specific image planning system (IPS) was developed that can be used to assist in kV imaging technique selection during localization for radiotherapy. The IPS algorithm performs a divergent ray-trace through a three dimensional computed tomography (CT) data set. Energy-specific attenuation through each voxel of the CT data set is calculated and imaging detector response is integrated into the algorithm to determine the absolute values of pixel intensity and image contrast. Phantom testing demonstrated that image contrast resulting from under exposure, over exposure as well as a contrast plateau can be predicted by use of a prospective image planning algorithm. Phantom data suggest the potential for reducing imaging dose by selecting a high kVp without loss of image contrast. In the clinic, image acquisition parameters can be predicted using the IPS that reduce patient dose without loss of useful image contrast

Objective Assessment of Image Quality: Extension of Numerical Observer Models to Multidimensional Medical Imaging Studies

Encompassing with fields on engineering and medical image quality, this dissertation proposes a novel framework for diagnostic performance evaluation based on objective image-quality assessment, an important step in the development of new imaging devices, acquisitions, or image-processing techniques being used for clinicians and researchers. The objective of this dissertation is to develop computational modeling tools that allow comprehensive evaluation of task-based assessment including clinical interpretation of images regardless of image dimensionality. Because of advances in the development of medical imaging devices, several techniques have improved image quality where the format domain of the outcome images becomes multidimensional (e.g., 3D+time or 4D). To evaluate the performance of new imaging devices or to optimize various design parameters and algorithms, the quality measurement should be performed using an appropriate image-quality figure-of-merit (FOM). Classical FOM such as bias and variance, or mean-square error, have been broadly used in the past. Unfortunately, they do not reflect the fact that the average performance of the principal agent in medical decision-making is frequently a human observer, nor are they aware of the specific diagnostic task. The standard goal for image quality assessment is a task-based approach in which one evaluates human observer performance of a specified diagnostic task (e.g. detection of the presence of lesions). However, having a human observer performs the tasks is costly and time-consuming. To facilitate practical task-based assessment of image quality, a numerical observer is required as a surrogate for human observers. Previously, numerical observers for the detection task have been studied both in research and industry; however, little research effort has been devoted toward development of one utilized for multidimensional imaging studies (e.g., 4D). Limiting the numerical observer tools that accommodate all information embedded in a series of images, the performance assessment of a particular new technique that generates multidimensional data is complex and limited. Consequently, key questions remain unanswered about how much the image quality improved using these new multidimensional images on a specific clinical task. To address this gap, this dissertation proposes a new numerical-observer methodology to assess the improvement achieved from newly developed imaging technologies. This numerical observer approach can be generalized to exploit pertinent statistical information in multidimensional images and accurately predict the performance of a human observer over the complexity of the image domains. Part I of this dissertation aims to develop a numerical observer that accommodates multidimensional images to process correlated signal components and appropriately incorporate them into an absolute FOM. Part II of this dissertation aims to apply the model developed in Part I to selected clinical applications with multidimensional images including: 1) respiratory-gated positron emission tomography (PET) in lung cancer (3D+t), 2) kinetic parametric PET in head-and-neck cancer (3D+k), and 3) spectral computed tomography (CT) in atherosclerotic plaque (3D+e). The author compares the task-based performance of the proposed approach to that of conventional methods, evaluated based on a broadly-used signal-known-exactly /background-known-exactly paradigm, which is in the context of the specified properties of a target object (e.g., a lesion) on highly realistic and clinical backgrounds. A realistic target object is generated with specific properties and applied to a set of images to create pathological scenarios for the performance evaluation, e.g., lesions in the lungs or plaques in the artery. The regions of interest (ROIs) of the target objects are formed over an ensemble of data measurements under identical conditions and evaluated for the inclusion of useful information from different complex domains (i.e., 3D+t, 3D+k, 3D+e). This work provides an image-quality assessment metric with no dimensional limitation that could help substantially improve assessment of performance achieved from new developments in imaging that make use of high dimensional data

Deep learning for lung cancer analysis

This thesis describes the development and evaluation of two novel deep learning applications that tackle two cancers that affect the lungs. The first, lung cancer, is the largest cause of cancer-related deaths in the United Kingdom. It accounts for more than 1 in 5 cancer deaths; around 35,000 people every year. Lung cancer is curable providing it is detected very early. Computed tomography (CT) X-ray imaging has been shown to be effective for screening. However, the resulting 3D medical images are laborious for humans to read, and widespread adoption would put a huge strain on already stretched radiology departments. I developed a novel deep learning based approach for the automatic detection of lung nodules, potential early lung cancer, that has potential to reduce human workloads. It was evaluated on two independent datasets, and achieves performance competitive with published state-of-the-art tools, with average sensitivity of 84% to 92% at 8 false positives per scan. I developed a related invention which allows hierarchical relationships to be leveraged to improve the performance of CAD tools like this for detection and segmentation tasks. The second cancer is malignant pleural mesothelioma. It is very different from lung cancer: rather than growing within the lung, mesothelioma grows around the outside of the lung in the pleural cavity, like the rind on an orange. It is a rare cancer, caused by exposure to asbestos fibres. It can take decades from exposure to symptoms developing. In Glasgow many mesothelioma patients worked in the ship-building industry, which relied heavily on asbestos. Although asbestos has been banned in the UK since 1999, its presence in buildings and equipment built before then mean that mesothelioma will remain a problem for years to come. Sadly, asbestos is still being mined and many countries, including the United States, have still not instigated a complete ban. For mesothelioma the main challenge is not detection, but accurate measurement —- without the ability to measure tumour size it is difficult to evaluate potential treatments. We therefore developed a fully automated volumetric assessment of malignant pleural mesothelioma. Performance of the algorithm is shown on a multi-centre test set, where volumetric predictions are highly correlated with an expert annotator (r=0.851, p<0.0001). Region overlap scores between the automated method and an expert annotator exceed those for inter-annotator agreement across a subset of cases. Dice overlap scores of 0.64 and 0.55, by cross-validation and independent testing respectively, were achieved. Future work will progress this algorithm towards clinical deployment for the automated assessment of longitudinal tumour development

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Investigation of time-resolved volumetric MRI to enhance MR-guided radiotherapy of moving lung tumors

In photon radiotherapy of lung cancer, respiratory-induced motion introduces systematic and statistical uncertainties in treatment planning and dose delivery. By integrating magnetic resonance imaging (MRI) in the treatment planning process in MR-guided radiotherapy (MRgRT), uncertainties in target volume definition can be reduced with respect to state-of-the-art X-ray-based approaches. Furthermore, MR-guided linear accelerators (MR-Linacs) offer dose delivery with enhanced accuracy and precision through daily treatment plan adaptation and gated beam delivery based on real-time MRI. Today, the potential of MRgRT of moving targets is, however, not fully exploited due to the lack of time-resolved four-dimensional MRI (4D-MRI) in clinical practice. Therefore, the aim of this thesis was to develop and experimentally validate new methods for motion characterization and estimation with 4D-MRI for MRgRT of lung cancer. Different concepts were investigated for all phases of the clinical workflow - treatment planning, beam delivery, and post-treatment analysis. Firstly, a novel internal target volume (ITV) definition method based on the probability-of-presence of moving tumors derived from real-time 4D-MRI was developed. The ability of the ITVs to prospectively account for changes occurring over the course of several weeks was assessed in retrospective geometric analyses of lung cancer patient data. Higher robustness of the probabilistic 4D-MRI-based ITVs against interfractional changes was observed compared to conventional target volumes defined with four-dimensional computed tomography (4D-CT). The study demonstrated that motion characterization over extended times enabled by real-time 4D-MRI can reduce systematic and statistical uncertainties associated with today’s standard workflow. Secondly, experimental validation of a published motion estimation method - the propagation method - was conducted with a porcine lung phantom under realistic patient-like conditions. Estimated 4D-MRIs with a temporal resolution of 3.65 Hz were created based on orthogonal 2D cine MRI acquired at the scanner unit of an MR-Linac. A comparison of these datasets with ground truth respiratory-correlated 4D-MRIs in geometric analyses showed that the propagation method can generate geometrically accurate estimated 4D-MRIs. These could decrease target localization errors and enable 3D motion monitoring during beam delivery at the MR-Linac in the future. Lastly, the propagation method was extended to create continuous time-resolved estimated synthetic CTs (tresCTs). The proposed method was experimentally tested with the porcine lung phantom, successively imaged at a CT scanner and an MR-Linac. A high agreement of the images and corresponding dose distributions of the tresCTs and measured ground truth 4D-CTs was found in geometric and dosimetric analyses. The tresCTs could be used for post-treatment time-resolved reconstruction of the delivered dose to guide treatment adaptations in the future. These studies represent important steps towards a clinical application of time-resolved 4D-MRI methods for enhanced MRgRT of lung tumors in the near future

Radiation dose assessment : measurement, estimation and interpretation

New technologies or methods of image acquisition are driven by the need for increased anatomical information to improve diagnostic accuracy or surgical planning. These new technologies are often accompanied with additional radiation dose yet this can be justified through the consideration of the benefit it brings. Examples include the use of CT colonography instead of double contrast barium enemas, CT urography replacing intravenous urography and, in nuclear medicine imaging the increased use of CT imaging as part of single photon emission tomography and positron emission tomography to correct emission data or localise or characterise identified lesions. Manufacturers are quick to promote their systems as “low-dose” but little independent evaluation of this claim existed. In the context of nuclear medicine, the additional imaging raised questions as to the use of the attenuation correction data specifically. The question of should the cross sectional images be reviewed for pathology was has been the focus of debate. It was recognised that the quality of these images is poor due to the “low-dose” acquisition. The research presented in this thesis and portfolio of published work aimed to establish an accurate method of assessing the radiation dose, initially from the CT attenuation correction acquisition, but later in other imaging modalities. In this thesis eight papers are used to illustrate the methods developed in this work, and how they were applied to other fields of medical imaging. Six of these papers were completed as the first author and the remainder as co-author. Initially, the concepts of radiation dose were critically evaluated. Following identification of sub-optimal techniques, steps were taken to improve the accuracy of dose measurement using thermoluminescent dosimeters, digital dosimeters and simulation through software. These techniques have been analysed critically and where appropriate improvements are recommended. Radiation dose, in particular the associated risk, is a challenging concept to convey to patients and care givers and simply providing a figure of dose does not convey the required information needed to allow consent to be given. Methods by which radiation dose and risk can be interpreted is critiqued with reference to published literature. The thesis concludes with a description of the intellectual contribution illustrating the role played as first author and as a co-author in the works included in the portfolio and a review of impact considering citation metrics and downloads. It was also decided to include citations from within the Diagnostic Imaging Research Programme and PhD theses from The University of Salford to demonstrate how research activities within the portfolio of published works have influenced other methodologies and outputs

Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer

Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice