Neuroimaging Biomarkers in Paediatric Sickle Cell Disease

Sickle Cell Disease (SCD) is a collection of genetic haemoglobinopathies, the most common and severe being homozygous sickle cell anaemia. In the UK, it has been estimated that 1 in 2000 children are born with SCD. The disease is characterised by chronic anaemia, recurrent pain crises and vascular occlusion. Neurologically, there is a high incidence of stroke in childhood, as well as cognitive dysfunction. Newborn screening programmes and preventative treatments have allowed a much longer lifespan; however recently, neurological research has shifted to characterising subtler aspects of brain development and functioning that may be critically important to the individual’s quality of life. This thesis overviews the neurological and neurocognitive complications of SCD, and how magnetic resonance imaging (MRI) can provide biomarkers for severity of disease. During the PhD, retrospective and prospective cognitive and MRI data were collected and analysed. Diagnostic clinical MRI sequences and advanced MRI sequences were applied, as well as a neuropsychological test battery aimed at intelligence and executive function. First, this thesis reviews the intelligence literature in SCD and includes previously unreported data, finding patients, regardless of abnormality seen on conventional MRI, have lowered full-scale intelligence quotient than controls. Then, to determine imaging biomarkers, volumetric differences and diffusion characteristics were identified. Patients were found to have decreased volumes of subcortical structures compared to controls, in groups corresponding to disease severity. Results from a three-year longitudinal clinical trial suggest evidence of atrophy in paediatric patients, with no apparent protective effect of treatment. Diffusion tensor imaging revealed reduced white matter integrity across the brain, correlating with recognised markers of disease severity (i.e. oxygen saturation and haemoglobin from a full blood count). Overall, the four experiments bridge a gap in the cognitive and neuroimaging literature of the extent of neurological injury in children with SCD

3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities

Accurate, automated quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided diagnosis (CADx) systems to support the in- terpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, varying image spatial resolutions resulting from different scanner protocols, and the presence of blurring artefacts. This paper presents a novel computing ap- proach for automated organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learning techniques in a two-part process. (1) a 3D encoder-decoder, Rb-UNet, builds a localisation model and a 3D Tiramisu network generates a boundary-preserving segmentation model for each target structure; (2) the fully trained Rb-UNet predicts a 3D bounding box encapsulating the target structure of interest, after which the fully trained Tiramisu model performs segmentation to reveal organ or muscle boundaries for every protrusion and indentation. The proposed approach is evaluated on six different datasets, including MRI, Dynamic Contrast Enhanced (DCE) MRI and CT scans targeting the pancreas, liver, kidneys and iliopsoas muscles. We achieve quantitative measures of mean Dice similarity coefficient (DSC) that surpasses or are comparable with the state-of-the-art and demonstrate statistical stability. A qualitative evaluation performed by two independent experts in radiology and radiography verified the preservation of detailed organ and muscle boundaries

Investigation of tissue microenvironments using diffusion magnetic resonance imaging

Diffusion-weighted magnetic resonance imaging (DW-MRI) has rapidly become an important part of cancer patient management. In this thesis, challenges in the analysis and interpretation ofDW-MRI data are investigated with focus on the intravoxel incoherent motion (IVIM) model, and its applications to childhood cancers. Using guidelines for validation of potential imaging biomarkers, technical and biological investigation of IVIM was undertaken using a combination of model simulations and in vivo data. To reduce the translational gap between the research and clinical use of IVIM, the model was implemented into an in-house built clinical decision support system. Technical validation was performed with assessment of accuracy, precision and bias of the estimated IVIM parameters. Best performance was achieved with a constrained IVIM fitting approach. The optimal use of b-values was dependent on the tissue characteristics and a compromise between bias and variability. Reliable data analysis was strongly dependent on the data quality and particularly the signal-to-noise ratio. IVIM perfusion fraction (j) was generally found to correlate with dynamic susceptibility contrast imaging derived cerebral blood volume. IVIM-f also presented as a potential diagnostic biomarker in discriminating between malignant retroperitoneal tumour types. Overall, the results encourage the use of IVIM parameters as potential imaging biomarkers

Motion corrected fetal body magnetic resonance imaging provides reliable 3D lung volumes in normal and abnormal fetuses

Objectives: To calculate 3D-segmented total lung volume (TLV) in fetuses with thoracic anomalies using deformable slice-to-volume registration (DSVR) with comparison to 2D-manual segmentation. To establish a normogram of TLV calculated by DSVR in healthy control fetuses. Methods: A pilot study at a single regional fetal medicine referral centre included 16 magnetic resonance imaging (MRI) datasets of fetuses (22–32 weeks gestational age). Diagnosis was CDH (n = 6), CPAM (n = 2), and healthy controls (n = 8). Deformable slice-to-volume registration was used for reconstruction of 3D isotropic (0.85 mm) volumes of the fetal body followed by semi-automated lung segmentation. 3D TLV were compared to traditional 2D-based volumetry. Abnormal cases referenced to a normogram produced from 100 normal fetuses whose TLV was calculated by DSVR only. Results: Deformable slice-to-volume registration-derived TLV values have high correlation with the 2D-based measurements but with a consistently lower volume; bias −1.44 cm3 [95% limits: −2.6 to −0.3] with improved resolution to exclude hilar structures even in cases of motion corruption or very low lung volumes. Conclusions: Deformable slice-to-volume registration for fetal lung MRI aids analysis of motion corrupted scans and does not suffer from the interpolation error inherent to 2D-segmentation. It increases information content of acquired data in terms of visualising organs in 3D space and quantification of volumes, which may improve counselling and surgical planning

Advancing quantitative imaging of neuroblastoma

Neuroblastoma is an embryonal cancer that exhibits marked heterogeneity of presentation and prognosis, ranging in outcomes from amongst the poorest in paediatric oncology to spontaneous remission. Scintigraphy using ¹²³I-meta-Iodobenzylguanidine (MIBG) provides unique functional information and is commonly used to manage Neuroblastoma alongside MRI. This thesis advances the imaging of neuroblastoma through SPECT/MR integration and quantitative SPECT/CT optimisation. A simulation study of Dixon Magnetic Resonance Attenuation Correction (MRAC) was undertaken using ⁹⁹ᵐTc-MDP SPECT/CT studies. These simulations showed that SPECT MRAC could achieve superior perfomance to PET/MR in cases without signifciant lung coverage. Observer studies were also undertaken to evaluate a novel ¹²³I-MIBG SPECT/MR fusion dataset and the impact of this technique on semiquantitative scoring of Neuroblastoma. The observers identified multiple clinically significant findings when using SPECT/MR versus planar scintigraphy. The ¹²³I imaging performance of Siemens parallel hole collimators was assessed using a custom precision sensitivity phantom. The contributions from photopeak and septal penetration were spectrally decomposed and the medium energy collimator shown to be optimal. Existing methodologies for optimisation were found to be inadequate for quantitative SPECT. A measurement-dependent methodology was proposed and used to optimise reconstruction parameters for ¹²³I-MIBG SPECT/CT. Optimised parameters were applied to wholebody SPECT/CT scans of Neuroblastoma and patterns and ranges of uptake in the liver and brain were evaluated. Right lobe of liver was shown to provide a significantly lower and more statistically consistent physiological reference than the left lobe. Standardised Uptake Values (SUVs) scaled to lean body mass were found to be superior to body weight scaled SUVs and absolute activity concentrations. Normal ranges and limits of variation were recommended as quality control measures for quantitative scans of Neuroblastoma

Investigating Advanced Magnetic Resonance Imaging for Improved Diagnosis and Prediction of Treatment Response in Wilms' Tumour Patients

Wilms’ tumour is the most common paediatric renal tumour. In Europe treatment involves pre-operative chemotherapy followed by surgery. Many patients receive MRI scans which include diffusion weighted imaging (DWI) throughout their diagnosis and treatment. This thesis retrospectively acquired Wilms’ tumour MRI data and prospectively acquired renal DWI data in healthy volunteers. Four models of diffusion were used throughout this thesis; mono-exponential, IVIM (intravoxel incoherent motion), stretched exponential, and kurtosis. In healthy volunteers, models were compared based on the reproducibility of the parameters, when calculated based on different b values and magnetic fields strengths. It was shown that ADC, D (IVIM), f (IVIM), Dk (kurtosis), and α (stretched exponential) had high levels of reproducibility whereas reproducibility was poorer in D* (IVIM), K (kurtosis) and DDC (stretched exponential). Model fits were compared in Wilms’ tumour and contralateral normal kidney data using the Akaike Information Criterion. It was shown that all raw DWI data favoured non- Gaussian models as opposed to a mono-exponential model. DWI data acquired in Wilms’ tumour favoured the stretched exponential model, and DWI data acquired in normal kidneys favoured the IVIM model. The volume of necrotic tissue post-chemotherapy is an important marker of treatment response. However, currently identification of necrosis relies on gadolinium contrast enhancement. It was shown that a combination of T1weighted imaging and ADC could provide an alternative method to visualising and quantifying necrosis, allowing future studies to estimate the volume fraction of necrosis in Wilms’ tumour without gadolinium. Finally, it was shown that certain Wilms’ tumour subtypes could be distinguished in vivo using DWI, whereas currently this relies on histological tissue analysis post- surgery. The parameters D* (IVIM) and K (kurtosis) provided the best stratification between subtypes, however, the earlier study demonstrated that the reproducibility of these parameters was poor, which may limit their clinical utility

Abdominal DCE‐MRI reconstruction with deformable motion correction for liver perfusion quantification

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146361/1/mp13118_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146361/2/mp13118.pd

Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue

Purpose To investigate the robustness of constrained and simultaneous intravoxel incoherent motion (IVIM) fitting methods and the estimated IVIM parameters (D, D* and f) for applications in brain and low‐perfused tissues. Materials and Methods Model data simulations relevant to brain and low‐perfused tumor tissues were computed to assess the accuracy, relative bias, and reproducibility (CV%) of the fitting methods in estimating the IVIM parameters. The simulations were performed at a series of signal‐to‐noise ratio (SNR) levels to assess the influence of noise on the fitting. Results The estimated IVIM parameters from model simulations were found significantly different (P < 0.05) using simultaneous and constrained fitting methods at low SNR. Higher accuracy and reproducibility were achieved with the constrained fitting method. Using this method, the mean error (%) for the estimated IVIM parameters at a clinically relevant SNR = 40 were D 0.35, D* 41.0 and f 4.55 for the tumor model and D 1.87, D* 2.48, and f 7.49 for the gray matter model. The most robust parameters were the IVIM‐D and IVIM‐f. The IVIM‐D* was increasingly overestimated at low perfusion. Conclusion A constrained IVIM fitting method provides more accurate and reproducible IVIM parameters in low‐perfused tissue compared with simultaneous fitting. Level of Evidence:

Rigid‐body motion correction of the liver in image reconstruction for golden‐angle stack‐of‐stars DCE MRI

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141403/1/mrm26782_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141403/2/mrm26782.pd