Novel Magnetic Resonance Acquisition and Processing Strategies for Biological Tissue Characterisation

<p>Proton magnetic resonance techniques have become indispensable for characterising tissues non-invasively. These methods provide abundant information regarding metabolism, morphology and histology of the sample under study. While these techniques were more expensive in the past compared to radioactive methods, modern advances in hardware and methodology provide the potential to use magnetic resonance systems more efficiently and widely. In this context, this thesis explored innovative magnetic resonance technologies from three independent perspectives which are suitable for tissue characterisation, utilising techniques from a wide range of disciplines including physics, engineering, biology and medical sciences. One strategy relates to compressed sensing magnetic resonance imaging, seeking to recover detailed features at high undersampling rates. A data-adaptive sparse transform facilitated by principal component analysis was introduced as an alternative to the conventional pre-defined sparse transform. Moreover, the principal component analysis was used in a recognition algorithm for the reconstruction of undersampled data. The performances of these approaches were studied in cases of localised changes in the acquired images. The results demonstrated that the recognition reconstruction algorithm performed better than wavelet compressed sensing. This progress can be utilised to accelerate current state of the art imaging protocols at high magnetic field strengths. Furthermore, the prior knowledge contained in high resolution databases may enhance imaging capabilities of technologies at low magnetic field strengths. A second approach exploits nuclear magnetic resonance diffusion contrast instead of contrast agents for tissue characterisation. Microstructural information and global fractional anisotropy can be obtained from diffusion-diffusion correlation spectroscopy via a novel multi-dimensional gradient scheme. The concept was validated by random walk simulations and experiments of biological samples. Both correlation maps and global fractional anisotropy of in vitro healthy and tumour-bearing mouse brains were found to be different, thus providing a potential application of the proposed scheme in diffusion oncology. In addition, a threshold algorithm on the selection of a region of interest was implemented to minimise inter-observer variations. This technique was applied to a pilot study of diffusion weighted imaging data which were acquired from patients after x-ray mammography indicated lesions. The statistical analysis revealed an optimal threshold similar to values commonly used in positron emission tomography. Apart from selecting regions automatically, various data processing methods were implemented and compared with each other regarding their diagnostic accuracies. This field study provides opportunities for standardising procedures in diffusion weighted mammography, which may be integrated into clinical analysis in the future.</p>

Novel Magnetic Resonance Acquisition and Processing Strategies for Biological Tissue Characterisation

<p>Proton magnetic resonance techniques have become indispensable for characterising tissues non-invasively. These methods provide abundant information regarding metabolism, morphology and histology of the sample under study. While these techniques were more expensive in the past compared to radioactive methods, modern advances in hardware and methodology provide the potential to use magnetic resonance systems more efficiently and widely. In this context, this thesis explored innovative magnetic resonance technologies from three independent perspectives which are suitable for tissue characterisation, utilising techniques from a wide range of disciplines including physics, engineering, biology and medical sciences. One strategy relates to compressed sensing magnetic resonance imaging, seeking to recover detailed features at high undersampling rates. A data-adaptive sparse transform facilitated by principal component analysis was introduced as an alternative to the conventional pre-defined sparse transform. Moreover, the principal component analysis was used in a recognition algorithm for the reconstruction of undersampled data. The performances of these approaches were studied in cases of localised changes in the acquired images. The results demonstrated that the recognition reconstruction algorithm performed better than wavelet compressed sensing. This progress can be utilised to accelerate current state of the art imaging protocols at high magnetic field strengths. Furthermore, the prior knowledge contained in high resolution databases may enhance imaging capabilities of technologies at low magnetic field strengths. A second approach exploits nuclear magnetic resonance diffusion contrast instead of contrast agents for tissue characterisation. Microstructural information and global fractional anisotropy can be obtained from diffusion-diffusion correlation spectroscopy via a novel multi-dimensional gradient scheme. The concept was validated by random walk simulations and experiments of biological samples. Both correlation maps and global fractional anisotropy of in vitro healthy and tumour-bearing mouse brains were found to be different, thus providing a potential application of the proposed scheme in diffusion oncology. In addition, a threshold algorithm on the selection of a region of interest was implemented to minimise inter-observer variations. This technique was applied to a pilot study of diffusion weighted imaging data which were acquired from patients after x-ray mammography indicated lesions. The statistical analysis revealed an optimal threshold similar to values commonly used in positron emission tomography. Apart from selecting regions automatically, various data processing methods were implemented and compared with each other regarding their diagnostic accuracies. This field study provides opportunities for standardising procedures in diffusion weighted mammography, which may be integrated into clinical analysis in the future.</p>

High Frequency Ultrasonic Wave Propagation in Anisotropic Materials

<p>The influence of highly regular, anisotropic, microstructured materials on high frequency ultrasonic wave propagation was investigated in this work. Microstructure, often only treated as a source of scattering, significantly influences high frequency ultrasonic waves, resulting in unexpected guided wave modes. Tissues, such as skin or muscle, are treated as homogeneous by current medical ultrasound systems, but actually consist of highly anisotropic micron-sized fibres. As these systems increase towards 100 MHz, these fibres will significantly influence propagating waves leading to guided wave modes. The effect of these modes on image quality must be considered. However, before studies can be undertaken on fibrous tissues, wave propagation in more ideal structures must be first understood. After the construction of a suitable high frequency ultrasound experimental system, finite element modelling and experimental characterisation of high frequency (20-200 MHz) ultrasonic waves in ideal, collinear, nanostructured alumina was carried out. These results revealed interesting waveguiding phenomena, and also identified the potential and significant advantages of using a microstructured material as an alternative acoustic matching layer in ultrasonic transducer design. Tailorable acoustic impedances were achieved from 4-17 MRayl, covering the impedance range of 7-12 MRayl most commonly required by transducer matching layers. Attenuation coefficients as low as 3.5 dBmm-1 were measured at 100 MHz, which is excellent when compared with 500 dBmm-1 that was measured for a state of the art loaded epoxy matching layer at the same frequency. Reception of ultrasound without the restriction of critical angles was also achieved, and no dispersion was observed in these structures (unlike current matching layers) until at least 200 MHz. In addition, to make a significant step forward towards high frequency tissue characterisation, novel microstructured poly(vinyl alcohol) tissue-mimicking phantoms were also developed. These phantoms possessed acoustic and microstructural properties representative of fibrous tissues, much more realistic than currently used homogeneous phantoms. The attenuation coefficient measured along the direction of PVA alignment in an example phantom was 8 dBmm-1 at 30 MHz, in excellent agreement with healthy human myocardium. This method will allow the fabrication of more realistic and repeatable phantoms for future high frequency tissue characterisation studies.</p>

High Frequency Ultrasonic Wave Propagation in Anisotropic Materials

<p>The influence of highly regular, anisotropic, microstructured materials on high frequency ultrasonic wave propagation was investigated in this work. Microstructure, often only treated as a source of scattering, significantly influences high frequency ultrasonic waves, resulting in unexpected guided wave modes. Tissues, such as skin or muscle, are treated as homogeneous by current medical ultrasound systems, but actually consist of highly anisotropic micron-sized fibres. As these systems increase towards 100 MHz, these fibres will significantly influence propagating waves leading to guided wave modes. The effect of these modes on image quality must be considered. However, before studies can be undertaken on fibrous tissues, wave propagation in more ideal structures must be first understood. After the construction of a suitable high frequency ultrasound experimental system, finite element modelling and experimental characterisation of high frequency (20-200 MHz) ultrasonic waves in ideal, collinear, nanostructured alumina was carried out. These results revealed interesting waveguiding phenomena, and also identified the potential and significant advantages of using a microstructured material as an alternative acoustic matching layer in ultrasonic transducer design. Tailorable acoustic impedances were achieved from 4-17 MRayl, covering the impedance range of 7-12 MRayl most commonly required by transducer matching layers. Attenuation coefficients as low as 3.5 dBmm-1 were measured at 100 MHz, which is excellent when compared with 500 dBmm-1 that was measured for a state of the art loaded epoxy matching layer at the same frequency. Reception of ultrasound without the restriction of critical angles was also achieved, and no dispersion was observed in these structures (unlike current matching layers) until at least 200 MHz. In addition, to make a significant step forward towards high frequency tissue characterisation, novel microstructured poly(vinyl alcohol) tissue-mimicking phantoms were also developed. These phantoms possessed acoustic and microstructural properties representative of fibrous tissues, much more realistic than currently used homogeneous phantoms. The attenuation coefficient measured along the direction of PVA alignment in an example phantom was 8 dBmm-1 at 30 MHz, in excellent agreement with healthy human myocardium. This method will allow the fabrication of more realistic and repeatable phantoms for future high frequency tissue characterisation studies.</p>

Reliability of ultrasound in evaluating the plantar skin and fat pad of the foot in the setting of diabetes

Ultrasound can be used to assess injury and structural changes to the soft-tissue structure of the foot. It may be useful to assess the feet of people with diabetes who are at increased risk of plantar soft-tissue pathological changes. The aim of this study was to determine if ultrasound measurements of plantar soft-tissue thickness and assessments of tissue acoustic characteristics are reliable in people with and without diabetes mellitus. A repeated measures design was used to determine intra-observer reliability for ultrasound measurements of plantar skin and fat pad thickness and intra- and inter-observer reliability of plantar skin and fat pad tissue characterisation assessments made at foot sites which are at risk of tissue injury in people with diabetes. Thickness measurements and tissue characterisation assessments were obtained at the heel and forefoot in both the unloaded and compressed states and included discrete layers of the plantar tissues: skin, microchamber, horizontal fibrous band, macrochamber and total soft-tissue depth. At each site, relative intra-observer reliability was achieved for the measurement of at least one plantar tissue layer. The total soft-tissue thickness measured in the unloaded state (ICC 0.925–0.976) demonstrated intra-observer reliability and is the most sensitive for detecting small change on repeated measures. Intra-observer agreement was demonstrated for tissue characteristic assessments of the skin at the heel (k = 0.70), fat pad at the lateral sesamoid region (k = 0.70) and both skin and fat pad at the second (k = 0.80, k = 0.70 respectively) and third metatarsal heads (k = 0.90, k = 0.79 respectively). However, acceptable inter-observer agreement was not demonstrated for any tissue characteristic assessment, therefore the use of multiple observers should be avoided when making these assessments.

A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours

Multi-parametric tissue characterisation is demonstrated using a 4-minute protocol based on diffusion trace acquisitions. Three diffusion regimes are covered simultaneously: pseudo-perfusion, Gaussian, and non-Gaussian diffusion. The clinical utility of this method for fast multi-parametric mapping for brain tumours is explored. A cohort of 17 brain tumour patients was measured on a 3T hybrid MR-PET scanner with a standard clinical MRI protocol, to which the proposed multi-parametric diffusion protocol was subsequently added. For comparison purposes, standard perfusion and a full diffusion kurtosis protocol were acquired. Simultaneous amino-acid (18F-FET) PET enabled the identification of active tumour tissue. The metrics derived from the proposed protocol included perfusion fraction, pseudo-diffusivity, apparent diffusivity, and apparent kurtosis. These metrics were compared to the corresponding metrics from the dedicated acquisitions: cerebral blood volume and flow, mean diffusivity and mean kurtosis. Simulations were carried out to assess the influence of fitting methods and noise levels on the estimation of the parameters. The diffusion and kurtosis metrics obtained from the proposed protocol show strong to very strong correlations with those derived from the conventional protocol. However, a bias towards lower values was observed. The pseudo-perfusion parameters showed very weak to weak correlations compared to their perfusion counterparts. In conclusion, we introduce a clinically applicable protocol for measuring multiple parameters and demonstrate its relevance to pathological tissue characterisation.

Clinical Translation of Three-Dimensional Scar, Diffusion Tensor Imaging, Four-Dimensional Flow, and Quantitative Perfusion in Cardiac MRI: A Comprehensive Review

Cardiovascular magnetic resonance (CMR) imaging is a versatile tool that has established itself as the reference method for functional assessment and tissue characterisation. CMR helps to diagnose, monitor disease course and sub-phenotype disease states. Several emerging CMR methods have the potential to offer a personalised medicine approach to treatment. CMR tissue characterisation is used to assess myocardial oedema, inflammation or thrombus in various disease conditions. CMR derived scar maps have the potential to inform ablation therapy—both in atrial and ventricular arrhythmias. Quantitative CMR is pushing boundaries with motion corrections in tissue characterisation and first-pass perfusion. Advanced tissue characterisation by imaging the myocardial fibre orientation using diffusion tensor imaging (DTI), has also demonstrated novel insights in patients with cardiomyopathies. Enhanced flow assessment using four-dimensional flow (4D flow) CMR, where time is the fourth dimension, allows quantification of transvalvular flow to a high degree of accuracy for all four-valves within the same cardiac cycle. This review discusses these emerging methods and others in detail and gives the reader a foresight of how CMR will evolve into a powerful clinical tool in offering a precision medicine approach to treatment, diagnosis, and detection of disease.

Incidental detection of prostate cancer with computed tomography scans

Prostate cancer (PCa) is the second most frequent type of cancer found in men worldwide, with around one in nine men being diagnosed with PCa within their lifetime. PCa often shows no symptoms in its early stages and its diagnosis techniques are either invasive, resource intensive, or has low efficacy, making widespread early detection onerous. Inspired by the recent success of deep convolutional neural networks (CNN) in computer aided detection (CADe), we propose a new CNN based framework for incidental detection of clinically significant prostate cancer (csPCa) in patients who had a CT scan of the abdomen/pelvis for other reasons. While CT is generally considered insufficient to diagnose PCa due to its inferior soft tissue characterisation, our evaluations on a relatively large dataset consisting of 139 clinically significant PCa patients and 432 controls show that the proposed deep neural network pipeline can detect csPCa patients at a level that is suitable for incidental detection. The proposed pipeline achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.88 (95% Confidence Interval: 0.86–0.90) at patient level csPCa detection on CT, significantly higher than the AUCs achieved by two radiologists (0.61 and 0.70) on the same task.