Simultaneous 13N-Ammonia and gadolinium first-pass myocardial perfusion with quantitative hybrid PET-MR imaging: a phantom and clinical feasibility study

Background Positron emission tomography (PET) is the non-invasive reference standard for myocardial blood flow (MBF) quantification. Hybrid PET-MR allows simultaneous PET and cardiac magnetic resonance (CMR) acquisition under identical experimental and physiological conditions. This study aimed to determine feasibility of simultaneous 13N-Ammonia PET and dynamic contrast-enhanced CMR MBF quantification in phantoms and healthy volunteers. Methods Images were acquired using a 3T hybrid PET-MR scanner. Phantom study: MBF was simulated at different physiological perfusion rates and a protocol for simultaneous PET-MR perfusion imaging was developed. Volunteer study: five healthy volunteers underwent adenosine stress. 13N-Ammonia and gadolinium were administered simultaneously. PET list mode data was reconstructed using ordered subset expectation maximisation. CMR MBF was quantified using Fermi function-constrained deconvolution of arterial input function and myocardial signal. PET MBF was obtained using a one-tissue compartment model and image-derived input function. Results Phantom study: PET and CMR MBF measurements demonstrated high repeatability with intraclass coefficients 0.98 and 0.99, respectively. There was high correlation between PET and CMR MBF (r = 0.98, p < 0.001) and good agreement (bias − 0.85 mL/g/min; 95% limits of agreement 0.29 to − 1.98). Volunteer study: Mean global stress MBF for CMR and PET were 2.58 ± 0.11 and 2.60 ± 0.47 mL/g/min respectively. On a per territory basis, there was moderate correlation (r = 0.63, p = 0.03) and agreement (bias − 0.34 mL/g/min; 95% limits of agreement 0.49 to − 1.18). Conclusion Simultaneous MBF quantification using hybrid PET-MR imaging is feasible with high test repeatability and good to moderate agreement between PET and CMR. Future studies in coronary artery disease patients may allow cross-validation of techniques

Exploiting MRI information for improved kinetic modelling of dynamic PET data

Kinetic analysis of dynamic PET data requires an accurate estimation of the concen- tration of the available tracer in blood plasma, also known as the arterial input function (AIF). The gold standard method to determine the AIF involves serial blood sampling and is avoided in practice due to its invasiveness. An image derived input function (IDIF) can be a blood-free alternative but its accuracy is limited due to partial volume (PV) effects caused by the restricted spatial resolution of PET scanners. Furthermore, IDIFs are not accurate when metabolite products are present in the blood. Magnetic resonance imaging (MRI) can provide complementary information to PET with high spatial resolution and excellent soft tissue contrast. Furthermore, dynamic MRI techniques can be reliably used to measure the AIF, the concentration of contrast agent in plasma, due to their high temporal resolution. The underlying aim of this research is to improve IDIF estimation in PET, utilising spatial and temporal information from MRI. An IDIF measurement method was developed which involves segmentation of carotid arteries from MR angiography images and uses a practical PVC method to correct for PV effects. It was demonstrated that the IDIFs can be used to compute the cerebral metabolic rate of glucose in the brain with no significant difference compared to arterial sampling. The simultaneous estimation method (SIME) is an alternative technique used to estimate the AIF by fitting time activity curves derived from multiple regions. Due to its computational complexity, SIME is usually complemented with blood samples. In this work, we observed that the early part of an image derived blood curve or an MRI derived AIF could provide prior knowledge regarding the AIF. This was incorporated into SIME to make more accurate kinetic parameter estimations and to perform blood-free analysis of tracers with metabolites

Rationale and design of a longitudinal study of cerebral small vessel diseases, clinical and imaging outcomes in patients presenting with mild ischaemic stroke: Mild Stroke Study 3

Background: Cerebral small vessel disease is a major cause of dementia and stroke, visible on brain magnetic resonance imaging. Recent data suggest that small vessel disease lesions may be dynamic, damage extends into normal-appearing brain and microvascular dysfunctions include abnormal blood–brain barrier leakage, vasoreactivity and pulsatility, but much remains unknown regarding underlying pathophysiology, symptoms, clinical features and risk factors of small vessel disease. Patients and Methods: The Mild Stroke Study 3 is a prospective observational cohort study to identify risk factors for and clinical implications of small vessel disease progression and regression among up to 300 adults with non-disabling stroke. We perform detailed serial clinical, cognitive, lifestyle, physiological, retinal and brain magnetic resonance imaging assessments over one year; we assess cerebrovascular reactivity, blood flow, pulsatility and blood–brain barrier leakage on magnetic resonance imaging at baseline; we follow up to four years by post and phone. The study is registered ISRCTN 12113543. Summary: Factors which influence direction and rate of change of small vessel disease lesions are poorly understood. We investigate the role of small vessel dysfunction using advanced serial neuroimaging in a deeply phenotyped cohort to increase understanding of the natural history of small vessel disease, identify those at highest risk of early disease progression or regression and uncover novel targets for small vessel disease prevention and therapy

Improved quantification of perfusion in patients with cerebrovascular disease.

In recent years measurements of cerebral perfusion using bolus-tracking MRI have become common clinical practice in the diagnosis and management of patients with stroke and cerebrovascular disease. An active area of research is the development of methods to identify brain tissue that is at risk of irreversible damage, but amenable to salvage using reperfusion treatments, such as thrombolysis. However, the specificity and sensitivity of these methods are limited by the inaccuracies in the perfusion data. Accurate measurements of perfusion are difficult to obtain, especially in patients with cerebrovascular diseases. In particular, if the bolus of MR contrast is delayed and/or dispersed due to cerebral arterial abnormalities, perfusion is likely to be underestimated using the standard analysis techniques. The potential for such underestimation is often overlooked when using the perfusion maps to assess stroke patients. Since thrombolysis can increase the risk of haemorrhage, a misidentification of 'at-risk' tissue has potentially dangerous clinical implications. This thesis presents several methodologies which aim to improve the accuracy and interpretation of the analysed bolus-tracking data. Two novel data analysis techniques are proposed, which enable the identification of brain regions where delay and dispersion of the bolus are likely to bias the perfusion measurements. In this way true hypoperfusion can be distinguished from erroneously low perfusion estimates. The size of the perfusion measurement errors are investigated in vivo, and a parameterised characterisation of the bolus delay and dispersion is obtained. Such information is valuable for the interpretation of in vivo data, and for further investigation into the effects of abnormal vasculature on perfusion estimates. Finally, methodology is presented to minimise the perfusion measurement errors prevalent in patients with cerebrovascular diseases. The in vivo application of this method highlights the dangers of interpreting perfusion values independently of the bolus delay and dispersion

Breast Cancer Analysis in DCE-MRI

Breast cancer is the most common women tumour worldwide, about 2 million new cases diagnosed each year (second most common cancer overall). This disease represents about 12% of all new cancer cases and 25% of all cancers in women. Early detection of breast cancer is one of the key factors in determining the prognosis for women with malignant tumours. The standard diagnostic tool for the detection of breast cancer is x-ray mammography. The disadvantage of this method is its low specificity, especially in the case of radiographically dense breast tissue (young or under-forty women), or in the presence of scars and implants within the breast. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has demonstrated a great potential in the screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. However, due to the large amount of information, DCE-MRI manual examination is error prone and can hardly be inspected without the use of a Computer-Aided Detection and Diagnosis (CAD) system. Breast imaging analysis is made harder by the dynamical characteristics of soft tissues since any patient movements (such as involuntary due to breathing) may affect the voxel-by-voxel dynamical analysis. Breast DCE-MRI computer-aided analysis needs a pre-processing stage to identify breast parenchyma and reduce motion artefacts. Among the major issues in developing CAD for breast DCE-MRI, there is the detection and classification of lesions according to their aggressiveness. Moreover, it would be convenient to determine those subjects who are likely to not respond to the treatment so that a modification may be applied as soon as possible, relieving them from potentially unnecessary or toxic treatments. In this thesis, an automated CAD system is presented. The proposed CAD aims to support radiologist in lesion detection, diagnosis and therapy assessment after a suitable preprocessing stage. Segmentation of breast parenchyma has been addressed relying on fuzzy binary clustering, breast anatomical priors and morphological refinements. The breast mask extraction module combines three 2D Fuzzy C-Means clustering (executed from the three projection, axial, coronal and transversal) and geometrical breast anatomy characterization. In particular, seven well-defined key-points have been considered in order to accurately segment breast parenchyma from air and chest-wall. To diminish the effects of involuntary movement artefacts, it is usual to apply a motion correction of the DCE-MRI volumes before of any data analysis. However, there is no evidence that a single Motion Correction Technique (MCT) can handle different deformations - small or large, rigid or non-rigid - and different patients or tissues. Therefore, it would be useful to develop a quality index (QI) to evaluate the performance of different MCTs. The existent QI might not be adequate to deal with DCE-MRI data because of the intensity variation due to contrast media. Therefore, in developing a novel QI, the underlying idea is that once DCE-MRI data have been realigned using a specific MCT, the dynamic course of the signal intensity should be as close as possible to physiological models, such as the currently accepted ones (e.g. Tofts-Kermode, Extended Tofts-Kermode, Hayton-Brady, Gamma Capillary Transit Time, etc.). The motion correction module ranks all the MCTs, using the QI, selects the best MCT and applies a correction before of further data analysis. The proposed lesion detection module performs the segmentation of lesions in Regions of Interest (ROIs) by means of classification at a pixel level. It is based on a Support Vector Machine (SVM) trained with dynamic features, extracted from a suitably pre-selected area by using a pixel-based approach. The pre-selection mask strongly improves the final result. The lesion classification module evaluates the malignity of each ROI by means of 3D textural features. The Local Binary Patterns descriptor has been used in the Three Orthogonal Planes (LBP-TOP) configuration. A Random Forest has been used to achieve the final classification into a benignant or malignant lesion. The therapy assessment stage aims to predict the patient primary tumour recurrence to support the physician in the evaluation of the therapy effects and benefits. For each patient which has at least a malignant lesion, the recurrence of the disease has been evaluated by means of a multiple classifiers system. A set of dynamic, textural, clinicopathologic and pharmacokinetic features have been used to assess the probability of recurrence for the lesions. Finally, to improve the usability of the proposed work, we developed a framework for tele-medicine that allows advanced medical image remote analysis in a secure and versatile client-server environment, at a low cost. The benefits of using the proposed framework will be presented in a real-case scenario where OsiriX, a wide-spread medical image analysis software, is allowed to perform advanced remote image processing in a simple manner over a secure channel. The proposed CAD system have been tested on real breast DCE-MRI data for the available protocols. The breast mask extraction stage shows a median segmentation accuracy and Dice similarity index of 98% (+/-0,49) and 93% %(+/-1,48) respectively and 100% of neoplastic lesion coverage. The motion correction module is able to rank the MCTs with an accordance of 74% with a 'reference ranking'. Moreover, by only using 40% of the available volume, the computational load is reduced selecting always the best MCT. The automatic detection maximises the area of correctly detected lesions while minimising the number of false alarms with an accuracy of 99% and the lesions are, then, diagnosed according to their stage with an accuracy of 85%. The therapy assessment module provides a forecasting of the tumour recurrence with an accuracy of 78% and an AUC of 79%. Each module has been evaluated by a leave-one-patient-out approach, and results show a confidence level of 95% (p<0.05). Finally, the proposed remote architecture showed a very low transmission overhead which settles on about 2.5% for the widespread 10\100 Mbps. Security has been achieved using client-server certificates and up-to-date standards

Development of novel imaging biomarkers using positron emission tomography for characterization of malignant phenotype and response evaluation

Positron emission tomography (PET) enables noninvasive tumour imaging, as changes in metabolic activity secondary to therapy can be measured before changes in tumour size are evident on standard anatomic imaging. Two imaging approaches representing proliferation dependent and independent technologies are evolving as potential methods for assessing growth signalling and, thus, treatment response: [18F]3’-deoxy-3’-fluorothymidine (FLT) and [11C]choline. The validity of the former in patients with pancreatic cancer is unproven and likewise, the role of the latter in response to androgen deprivation/radiotherapy in prostate cancer (PCa) remains unexplored. Using a variety of approaches, the aim of this thesis was to provide an understanding of the role of these tracers in lesion detection and response assessment in patients by PET/computed tomography (PET/CT). Given the high physiological hepatic localisation of FLT, a recently reported kinetic spatial filtering (KSF) algorithm was evaluated as a way to de-noise abdominal FLT-PET data from patients with advanced pancreatic cancer. Application of KSF led to improved lesion detection. FLT uptake (SUV60,max) significantly increased in mid-treatment (gemcitabine based) progressors (p=0.04). In this limited number of patients, reduction in FLT uptake did not predict overall survival. The role of [11C]choline PET/CT in lesion detection and response in prostate cancer (PCa) was also investigated using semi-quantitative and quantitative methods. As a prelude to the quantitative imaging studies, it was established that irreversible tracer uptake characterised tumour (breast cancer) [11C]choline kinetics. Similar irreversible uptake characterised PCa. An important finding was that tumour [11C]choline uptake (in 29 PCa patients) correlated with choline kinase (CHK) expression but not proliferation, as assessed by Ki67 labelling index. Immunohistochemistry of the above patients’ prostate cores with CHKα antibody demonstrated a spectrum of CHKα expression, ranging from expression in prostatic-intraepithelial-neoplasia to low to high expression in malignant cores. These findings were further corroborated in a larger cohort of 75 malignant cores derived from non-imaging studies. Having established [11C]choline as a proliferation independent marker of growth, its role in assessing treatment response was investigated. [11C]choline PET was sensitive to metabolic changes within prostate tumours following androgen deprivation and radical radiotherapy. While promising data were obtained with [11C]choline PET, the radiotracer is subject to metabolic degradation complicating data analysis. To this end, a novel metabolically stable analogue of choline ([18F]fluoromethyl-[1,2-2H4]-choline ([18F]D4FCH)) was transitioned into volunteers and patients to study its pharmacokinetics and preliminary diagnostic potential. This tracer embodies deuterium isotope substitution as a means to discourage systemic metabolism. The radiotracer had favourable dosimetry (effective-dose: 0.025mSv/MBq) and safety. Preliminary results in non-small cell lung cancer showed that the tracer is taken up in tumours. Further studies are warranted to characterise this new tracer in different tumour types. As a prelude to imaging cancer cell death in tumours, a caspase-3 specific radiotracer, [18F](S)-1-((1-(2-fluoroethyl)-1H-[1,2,3]-triazol-4-yl)methyl)-5-(2(2,4- difluorophenoxymethyl)-pyrrolidine-1-sulfonyl) isatin ([18F]ICMT-11) was also transitioned into volunteers. The radiotracer had favourable dosimetry (effective-dose: 0.025mSv/MBq) and safety. In summary, FLT-PET/CT combined with KSF and [11C]choline PET/CT were shown to be promising methods for imaging early treatment response in patients. Further work will be required to evaluate the clinical relevance of these data in terms of overall patient outcome. Furthermore, a new choline-based radiotracer and a caspase-3 specific radiotracer have been transitioned into humans.Open Acces

Methods for assisting the automation of Dynamic Susceptibility Contrast Magnetic Resonance Imaging Analysis

Purpose Dynamic susceptibility-contrast magnetic resonance imaging (DSC-MRI) is widely used for cerebral perfusion measurement, but dependence on operator input leads to a time-consuming, subjective, and poorly-reproducible analysis. Although automation can overcome these limitations, investigations are required to further simplify and accelerate the analysis. This research focuses on automating arterial voxel (AV) and brain tissue segmentation, and model-dependent deconvolution steps of DSC-MRI analysis. Methods Several features were extracted from DSC-MRI data; their AV- and tissue voxel- discriminatory powers were evaluated by the area-under-the-receiver-operating-characteristic-curve (AUCROC). Thresholds for discarding non-arterial voxels were identified using ROC cut-offs. The applicability of DSC-MRI time-series data for brain segmentation was explored. Two segmentation approaches that clustered the dimensionality-reduced raw data were compared with two raw−data-based approaches, and an approach using principal component analysis (PCA) for dimension-reduction. Computation time and Dice coefficients (DCs) were compared. For model-dependent deconvolution, four parametric transit time distribution (TTD) models were compared in terms of goodness- and stability-of-fit, consistency of perfusion estimates, and computation time. Results Four criteria were effective in distinguishing AVs, forming the basis of a framework that can determine optimal thresholds for effective criteria to discard tissue voxels with high sensitivity and specificity. Compared to raw−data-based approaches, one of the proposed segmentation approaches identified GM with higher (>0.7, p<0.005), and WM with similar DC. The approach outperformed the PCA-based approach for all tissue regions (p<0.005), and clustered similar regions faster than other approaches (p<0.005). For model-dependent deconvolution, all TTD models gave similar perfusion estimates and goodness-of-fit. The gamma distribution was most suitable for perfusion analysis, showing significantly higher fit stability and lower computation time. Conclusion The proposed methods were able to simplify and accelerate automatic DSC-MRI analysis while maintaining performance. They will particularly help clinicians in rapid diagnosis and characterisation of tumour or stroke lesions, and subsequent treatment planning and monitoring