A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT

Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artefacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT images without affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu’s method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation

Automatic Construction of Immobilisation Masks for use in Radiotherapy Treatment of Head-and-Neck Cancer

Current clinical practice for immobilisation for patients undergoing brain or head and neck radiotherapy is normally achieved using Perspex or thermoplastic shells that are moulded to patient anatomy during a visit to the mould room. The shells are “made to measure” and the methods currently employed to make them require patients to visit the mould room. The mould room visit can be depressing and some patients find this process particularly unpleasant. In some cases, as treatment progresses, the tumour may shrink and therefore there may be a need for a further mould room visits. With modern manufacturing and rapid prototyping comes the possibility of determining the shape of the shells from the CT-scan of the patient directly, alleviating the need for making physical moulds from the patients’ head. However, extracting such a surface model remains a challenge and is the focus of this thesis. The aim of the work in this thesis is to develop an automatic pipeline capable of creating physical models of immobilisation shells directly from CT scans. The work includes an investigation of a number of image segmentation techniques to segment the skin/air interface from CT images. To enable the developed pipeline to be quantitatively evaluated we compared the 3D model generated from the CT data to ground truth obtained by 3D laser scans of masks produced by the mould room in the frame of a clinical trial. This involved automatically removing image artefacts due to fixations from CT imagery, automatic alignment (registration) between two meshes, measuring the degree of similarity between two 3D volumes, and automatic approach to evaluate the accuracy of segmentation. This thesis has raised and addressed many challenges within this pipeline. We have examined and evaluated each stage of the pipeline separately. The outcomes of the pipeline as a whole are currently being evaluated by a clinical trial (IRAS ID:209119, REC Ref.:16/YH/0485). Early results from the trial indicate that the approach is viable

Assessment of and improvements to a stereophotogrammetric patient positioning system for proton therapy

Summary in English.Bibliography: pages 125-129.This thesis describes the construction and use of the facemask at the National Accelerator Centre (NAC) as used to both immobilise and position patients for precision proton radiotherapy. The precision achieved using the stereophotogrammetric (SPG) positioning system is measured, and the shortcomings and errors in using the facemask by the SPG system are measured and analysed. The implementation of improvements made to the SPG system is reported upon, and alternative means of both supporting the fiducial markers and immobilising the patient are investigated and evaluated. The accuracy of positioning a facemask using the SPG system is 1.4 mm and of positioning a newly designed frame is 1.6 mm. These measurements were made without using a patient. It is estimated that the total uncertainty of positioning a patient's tumour at the isocentre is 1.6 (1SD) mm using the facemask and it is estimated that the precision using the frame will be less than this value. The largest component of this error (1.39 mm) is due to the error in obtaining the CT scanner co-ordinates. These results are comparable to those obtained by other investigators. The movement of patient bony landmarks within the facemask was measured to be 1.0 ± 0.8 mm. Three main recommendations are that the CT scanner co-ordinating procedure be improved, the SPG computer program be rewritten in parts to achieve greater speed and accuracy, and that the new frame be used. The frame is easier to manufacture than the facemask and allows real time monitoring of the position of the patient's head by the SPG system thus allowing faster throughput of patients and better positioning quality control

Magnetic resonance imaging to improve structural localisation in radiotherapy planning

The purpose of this thesis is to develop the role of magnetic resonance imaging (MRI) in the radiotherapy (RT) planning process. This began by assessing a prototype inline three-dimensional distortion correction algorithm. A number of quality assurance tests were conducted using different test objects and the 3D distortion correction algorithm was compared with the standard two-dimensional version available for clinical use on the MRI system. Scanning patients using MRI in the RT position within an immobilisation mask can be problematic, since the multi-channel head coils typically used in diagnostic imaging, are not compatible with the immobilisation mask. To assess the image quality which can be obtained with MR imaging in the RT position, various MRI quality assurance phantoms were positioned within an immobilisation mask and a series of image quality tests were performed on four imaging coils compatible with the immobilisation mask. It was shown that only the 4-channel cardiac coil delivered comparable image quality to a multi-channel head coil. An investigation was performed to demonstrate how MRI patient position protocols influence registration quality in patients with prostate cancer undergoing radical RT. The consequences for target volume definition and dose coverage with RT planning were also assessed. Twenty patients with prostate cancer underwent a computed tomography (CT) scan in the RT position, a diagnostic MRI scan and an MRI scan in the RT position. The CT datasets were independently registered with the two MRI set-ups and the quality of registration was compared. This study demonstrated that registering CT and MR images in the RT position provides a statistically significant improvement in registration quality, target definition and target volume dose coverage for patients with prostate cancer. A similar study was performed on twenty-two patients with oropharyngeal cancer undergoing radical RT. It was shown that when patients with oropharyngeal cancer undergo an MRI in the RT position there are significant improvements in CT-MR image registration, target definition and target volume dose coverage

A generative adversarial network approach to synthetic-CT creation for MRI-based radiation therapy

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia), Universidade de Lisboa, Faculdade de Ciências, 2019This project presents the application of a generative adversarial network (GAN) to the creation of synthetic computed tomography (sCT) scans from volumetric T1-weighted magnetic resonance imaging (MRI), for dose calculation in MRI-based radio therapy workflows. A 3-dimensional GAN for MRI-to-CT synthesis was developed based on a 2-dimensional architecture for image-content transfer. Co-registered CT and T1 –weighted MRI scans of the head region were used for training. Tuning of the network was performed with a 7-foldcross-validation method on 42 patients. A second data set of 12 patients was used as the hold out data set for final validation. The performance of the GAN was assessed with image quality metrics, and dosimetric evaluation was performed for 33 patients by comparing dose distributions calculated on true and synthetic CT, for photon and proton therapy plans. sCT generation time was <30s per patient. The mean absolute error (MAE) between sCT and CT on the cross-validation data set was69 ± 10 HU, corresponding to a 20% decrease in error when compared to training on the original 2D GAN. Quality metric results did not differ statistically for the hold out data set (p = 0.09). Higher errors were observed for air and bone voxels, and registration errors between CT and MRI decreased performance of the algorithm. Dose deviations at the target were within 2% for the photon beams; for the proton plans, 21 patients showed dose deviations under 2%, while 12 had deviations between 2% and 8%. Pass rates (2%/ 2mm) between dose distributions were higher than 98% and 94% for photon and proton plans respectively. The results compare favorably with published algorithms and the method shows potential for MRI-guided clinical workflows. Special attention should be given when beams cross small structures and airways, and further adjustments to the algorithm should be made to increase performance for these regions

Imaging in neurological and vascular brain diseases (SPECT and SPECT/CT)

Since the first in vivo studies of cerebral function with radionuclides by Ingvar and Lassen, nuclear medicine (NM) brain applications have evolved dramatically, with marked improvements in both methods and tracers. Consequently it is now possible to assess not only cerebral blood flow and energy metabolism but also neurotransmission. Planar functional imaging was soon substituted by single-photon emission computed tomography (SPECT) and positron emission tomography (PET); it now has limited application in brain imaging, being reserved for the assessment of brain death

Development, Validation and Applications of MRI-Only Treatment Planning in Radiotherapy

Magnetic resonance imaging (MRI) has superior soft tissue visualization to guide radiotherapy treatment planning but does not provide the electron density information required for the dose calculation. Thus, MRI has been used in a complementary way, registering to the gold standard computed tomography (CT) scan. Development of methods to allow accurate planning from the MRI images would remove the requirement for additional (CT) scans as well as improve clinical workflow and remove potential registration errors. Various methods have been reported to generate datasets with electron density information from MRI data, with these being termed substitute, synthetic or pseudo CT (sCT) datasets. This thesis explores the potential variation in planning and optimization error from MRI-only treatment planning for a range of situations. sCT generation was explored with a deep learning methodology applied to a set of retrospective H&N patient data. A lung MRI sequence was investigated for its potential application for sCT generation, with various methods trialed and assessed for clinical suitability. For an existing sCT generation method used clinically for prostate cancer treatment planning, a time-reduced MRI sequence was investigated, optimizing scan parameters for this by initial assessment in a volunteer cohort, followed by clinical validation in a patient cohort. A pancreas MRI volunteer study was also conducted to investigate internal organ motion effects on treatment planning and potential treatment delivery to assess the suitability of treatment regimes for pancreatic cancer patients. This work provides evidence that MRI-only treatment planning is achievable and acceptably accurate. This has led to current and future implementations of findings into clinical practice locally, and potentially more widely. MRI-only treatment planning in radiotherapy could lead to improved patient outcomes, via both better target delineation and reduced normal tissue toxicity