Knowledge Based Measurement Of Enhancing Brain Tissue In Anisotropic Mr Imagery

Medical Image Analysis has emerged as an important field in the computer vision community. In this thesis, two important issues in medical imaging are addressed and a solution for each is derived and synergistically combined as one coherent system. Firstly, a novel approach is proposed for High Resolution Volume (HRV) construction by combining different frequency components at multiple levels, which are separated by using a multi-resolution pyramid structure. Current clinical imaging protocols make use of multiple orthogonal low resolution scans to measure the size of the tumor. The highly anisotropic data result in difficulty and even errors in tumor assessment. In previous approaches, simple interpolation has been used to construct HRVs from multiple low resolution volumes (LRVs), which fail when large inter-plane spacing is present. In our approach, Laplacian pyramids containing band-pass contents are first computed from registered LRVs. The Laplacian images are expanded in their low resolution axes separately and then fused at each level. A Gaussian pyramid is recovered from the fused Laplacian pyramid, where a volume at the bottom level of the Gaussian pyramid is the constructed HRV. The effectiveness of the proposed approach is validated by using simulated images. The method has also been applied to real clinical data and promising experimental results are demonstrated. Secondly, a new knowledge-based framework to automatically quantify the volume of enhancing tissue in brain MR images is proposed. Our approach provides an objective and consistent way to evaluate disease progression and assess the treatment plan. In our approach, enhanced regions are first located by comparing the difference between the aligned set of pre- and post-contrast T1 MR images. Since some normal tissues may also become enhanced by the administration of Gd-DTPA, using the intensity difference alone may not be able to distinguish normal tissue from the tumor. Thus, we propose a new knowledge-based method employing knowledge of anatomical structures from a probabilistic brain atlas and the prior distribution of brain tumor to identify the real enhancing tissue. Our approach has two main advantages. i) The results are invariant to the image contrast change due to the usage of the probabilistic knowledge-based framework. ii) Using the segmented regions instead of independent pixels facilitates an approach that is much less sensitive to small registration errors and image noise. The obtained results are compared to the ground truth for validation and it is shown that the proposed method can achieve accurate and consistent measurements

Magnetic Resonance Imaging of the Brain in Moving Subjects. Application of Fetal, Neonatal and Adult Brain Studies

Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). Motion makes MRI data inconsistent, causing artifacts in conventional anatomical imaging as well as invalidating diffusion tensor imaging (DTI) reconstruction. In this thesis some of the important issues regarding the acquisition and reconstruction of anatomical and DTI imaging of moving subjects are addressed; methods to achieve high resolution and high signalto- noise ratio (SNR) volume data are proposed. An approach has been developed that uses multiple overlapped dynamic single shot slice by slice imaging combined with retrospective alignment and data fusion to produce self consistent 3D volume images under subject motion. We term this method as snapshot MRI with volume reconstruction or SVR. The SVR method has been performed successfully for brain studies on subjects that cannot stay still, and in some cases were moving substantially during scanning. For example, awake neonates, deliberately moved adults and, especially, on fetuses, for which no conventional high resolution 3D method is currently available. Fine structure of the in-utero fetal brain is clearly revealed for the first time with substantially improved SNR. The SVR method has been extended to correct motion artifacts from conventional multi-slice sequences when the subject drifts in position during data acquisition. Besides anatomical imaging, the SVR method has also been further extended to DTI reconstruction when there is subject motion. This has been validated successfully from an adult who was deliberately moving and then applied to inutero fetal brain imaging, which no conventional high resolution 3D method is currently available. Excellent fetal brain 3D apparent diffusion coefficient (ADC) maps in high resolution have been achieved for the first time as well as promising fractional Anisotropy (FA) maps. Pilot clinical studies using SVR reconstructed data to study fetal brain development in-utero have been performed. Growth curves for the normally developing fetal brain have been devised by the quantification of cerebral and cerebellar volumes as well as some one dimensional measurements. A Verhulst model is proposed to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data

Current state of MRI of the fetal brain in utero.

In this article we provide an overview of fetal brain development, describe the range of more common fetal neuropathology, and discuss the relevance of in utero MR (iuMR). Although ultrasonography remains the mainstay of fetal brain imaging, iuMR imaging is both feasible and safe, but presents several challenges. We discuss those challenges, the techniques employed to overcome them, and new approaches that may extend the clinical applicability of fetal iuMR

3D model-based super-resolution motion-corrected cardiac T1 mapping

OBJECTIVE: To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR). APPROACH: Due to signal-to-noise ratio (SNR) limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6 to 8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6 to 8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5 to 2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects. MAIN RESULTS: Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%. SIGNIFICANCE: The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3×1.3×1.5 to 2 mm(3)). The approach led to improvements in the visualization of small structures and precise T1 values

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Analysis of the developing brain using image registration

Imperial Users onl

Magnetic Resonance Imaging to Enhance the Diagnosis of Fetal Brain Abnormalities in utero

Purpose This thesis aims to determine the diagnostic performance of in utero MR (iuMR) imaging to diagnose fetal brain abnormalities and describes the development, application and processing of a 3D volume MR acquisition. Methods A systematic review and meta-analysis of existing evidence was conducted. A prospective multicentre study of pregnant women, with a fetal brain abnormality on ultrasound (USS), was undertaken – The MERIDIAN study. In addition, an investigation of fetuses with no brain abnormality on USS was undertaken. Diagnostic accuracy and confidence, as well as positive and negative predictive values, were calculated. A 3D image acquisition technique was introduced, its ability to aid diagnosis measured and computational post-processing applied. Fetal brain volumes were extracted from the 3D data using image segmentation and these were assessed for reproducibility and validity. Resultant data allowed 3D models of fetal brains to be printed. Normally developing fetal brain volumes were plotted graphically thereby allowing comparison with abnormal fetuses. Results A total of 570 complete datasets were available from 903 eligible participants. Diagnostic accuracy was 68% for USS and 93% for iuMR. 95% of diagnoses made by iuMR were reported with high confidence compared to 82% on USS. Changes in pregnancy management occurred in 33% of cases. Positive and negative predictive values of iuMR were 93% and 99.5% respectively. 3D image quality was diagnostic in 89.6%, of which 91.4% gave an accurate diagnosis. Intra- and inter-observer agreement of brain volume measurements was high. Agreement between computer based and brain model volume measurements was also high. Conclusions iuMR imaging improves diagnostic accuracy and confidence for fetal brain abnormalities, influencing pregnancy management in a high proportion of cases. 3D imaging enables versatile visualisation of fetal brain anatomy and reliable extraction of volumes. This additional quantitative information could improve diagnosis in relevant cases