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Motion correction in medical imaging.

Smith, Rhodri (2017) Motion correction in medical imaging. Doctoral thesis, University of Surrey.

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It is estimated that over half of current adults within Great Britain under the age of 65 will be diagnosed with cancer at some point in their lifetime. Medical Imaging forms an essential part of cancer clinical protocols and is able to furnish morphological, metabolic and functional information. The imaging of molecular interactions of biological processes in vivo with Positron Emission Tomography (PET) is informative not only for disease detection but also therapeutic response. The qualitative and quantitative accuracy of imaging is thus vital in the extraction of meaningful and reproducible information from the images, allowing increased sensitivity and specificity in the diagnosis and precision of image guided treatment. Furthermore the utilization of complementary information obtained via Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) in integrated PET-CT and PET-MR devices offers the potential for the synergistic effects of hybrid imaging to provide increased detection and precision of diagnosis with reduced radiation dose in a fully comprehensive single imaging examination. With the increasing sophistication in imaging technology respiratory organ motion during imaging has demonstrated itself to be a major degrading factor of PET image resolution. A modest estimate of respiratory motion amplitude of 5mm, results in PET system resolution degrading from ≈ 5mm to ≈8.5mm. This evidently has an impact on cancer lesion detectability. Therefore accurate and robust methods for respiratory motion correction are required for both clinical effectiveness and economic justification for purchasing state of the art hybrid PET scanners with high resolution capabilities. In addition the judicious use of imaging resources from hybrid imaging devices coupled with advanced image processing / acquisition protocols will allow optimization of data used for improving quantitative accuracy of PET images and those used for clinical interpretation. In essence it would prove impractical to use the MR scanner purely for monitoring respiratory motion. Numerous methods exist to attempt to correct PET imaging for respiratory motion. As presented in this thesis many methods demonstrate themselves to be ineffective in the clinical setting where the patients breathing patterns appear irregular in comparison to the idealized situation of regular periodic motion. Advanced respiratory motion correction techniques utilize hybrid PET/CT, PET/MR scanners coupled with an external source of information which serves as a surrogate to build a static correspondence to the estimated internal respiratory motion. Static models however are unable to adapt to their external environment and do not consider time dependent changes in the state of a system. A further confounding factor in the development and assessment of motion correction schemes for medical imaging data is the inability to acquire volumetric data with high contrast and high spatial and temporal resolution which serves as a ground truth for quantifying model accuracy and confidence. This thesis addresses both problems by analysing respiratory motion correspondence modelling under a manifold learning and alignment paradigm which may be used to consolidate many of the respiratory motion estimation models that exist today. A Bayesian approach is adopted in this work to incorporate a-priori information into the model building stage for a more robust, flexible adaptive respiratory motion estimation / correction framework. This thesis constructs and tests the first proposed adaptive motion model to correlate a surrogate signal with internal motion. This adaptive approach allows the relationship between external surrogate signal and internal motion to change dependent upon breathing pattern and system noise. The adaptive model was compared to a state-of the-art static model and allows more accurate motion estimates to be made when the patient is breathing with an irregular pattern. Testing performed on MRI data from 9 volunteers demonstrated the adaptive model was statistically more significant (p < 0.001) in the presence of irregular motion in comparison to a static model. The adaptive Kalman model on average reduced the error in motion by 30% in comparison to the static model. Utilizing the adaptive model during a typical PET study would theoretically result in ≈ 10% increase in PET resolution in comparison to relying on a static model alone for motion correction. The adaptive Kalman model has the capability to increase the performance of PET system resolution from ≈ 8.5mm to ≈ 5.8mm, ≈ 30%. A simulated PET study also demonstrated ≈ 30% increase in tumour uptake when using motion correction. Also demonstrated in the thesis is the first method to acquire volumetric imaging data from sparse MR samples during free breathing to allow the realization of high contrast, high resolution 4D models of respiratory motion using limited acquired data. The developed framework facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities as well as informing the development of generalizable models of human respiration. It is shown that the developed approach can provide equivalent motion vector fields in comparison to fully sampled 4D dynamic data. The incorporation of the manifold alignment step into the sparse motion model reduces the error in motion estimates by ≈ 16%. Example images of propagated motion are also presented as supplementary information. The thesis concludes by generalizing the concepts in this work and looking to utilize the developed methods to other problems in the medical imaging arena.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors :
Date : 29 September 2017
Funders : ESPRC
Projects : Respiratory Motion Correction in Medical Imaging
Contributors :
Related URLs :
Depositing User : Rhodri Smith
Date Deposited : 02 Oct 2017 07:53
Last Modified : 02 Oct 2017 07:53

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