Deep grey matter volumetry as a function of age using a semi-automatic qMRI algorithm

Quantitative Magnetic Resonance has become more and more accepted for clinical trial in many fields. This technique not only can generate qMRI maps (such as T1/T2/PD) but also can be used for further postprocessing including segmentation of brain and characterization of different brain tissue. Another main application of qMRI is to measure the volume of the brain tissue such as the deep Grey Matter (dGM). The deep grey matter serves as the brain's "relay station" which receives and sends inputs between the cortical brain regions. An abnormal volume of the dGM is associated with certain diseases such as Fetal Alcohol Spectrum Disorders (FASD). The goal of this study is to investigate the effect of age on the volume change of the dGM using qMRI. Thirteen patients (mean age= 26.7 years old and age range from 0.5 to 72.5 years old) underwent imaging at a 1.5T MR scanner. Axial images of the entire brain were acquired with the mixed Turbo Spin-echo (mixed -TSE) pulse sequence. The acquired mixed-TSE images were transferred in DICOM format image for further analysis using the MathCAD 2001i software (Mathsoft, Cambridge, MA). Quantitative T1 and T2-weighted MR images were generated. The image data sets were further segmented using the dual-space clustering segmentation. Then volume of the dGM matter was calculated using a pixel counting algorithm and the spectrum of the T1/T2/PD distribution were also generated. Afterwards, the dGM volume of each patient was calculated and plotted on scatter plot. The mean volume of the dGM, standard deviation, and range were also calculated. The result shows that volume of the dGM is 47.5 ±5.3ml (N=13) which is consistent with former studies. The polynomial tendency line generated based on scatter plot shows that the volume of the dGM gradually increases with age at early age and reaches the maximum volume around the age of 20, and then it starts to decrease gradually in adulthood and drops much faster in elderly age. This result may help scientists to understand more about the aging of the brain and it can also be used to compare with the results from former studies using different techniques

Vessel tractography using an intensity based tensor model with branch detection

In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Super-Resolution for Computed Tomography Based on Discrete Tomography

In computed tomography (CT), partial volume effects impede accurate segmentation of structures that are small with respect to the pixel size. In this paper, it is shown that for objects consisting of a small number of homogen

Accuracy of ITK-SNAP software for 3D analysis of a non-regular topography structure

To evaluate the accuracy of ITK-SNAP software for measuring volumes of a non-regular shape structure, using cone beam computed tomography (CBCT) scans, besides for developing a mathematical model to correct the software measurement error in case it existed. A phantom made by moulding a rubber duck’s head was filled with total (38,000 mm3) and partial volumes of water (7000 mm3, 14,000 mm3, 21,000 mm3, 28,000 mm3 and 35,000 mm3), which constituted the gold standards. The sound phantom and the phantom filled with different volumes of water were scanned in a Picasso Trio CBCT unit set at 80 kVp, 3.7 mA, 0.2 mm3 voxel and 12 × 8.5 cm field of view. Semi-automatic segmentation was performed with ITK-SNAP 3.0 software by two trained oral radiologists. Linear regression analyzed the relation between ITK-SNAP calculated volumes and the gold standard. Intraclass correlation coefficient was applied to analyze the reproducibility of the method. Significance level was set at 5%. Linear regression analysis showed a significant relationship between ITK-SNAP volumes and the gold standard (F = 22,537.3, p < 0.0001), with an R2 of 0.9993. The average error found was 4.7 (± 4.3) %. To minimize this error, a mathematical model was developed and provided a reduction of it. ICC revealed excellent intra-examiner agreements for both examiners 1 (ICC = 0.9991, p < 0.0001) and 2 (ICC = 0.9989, p < 0.0001). Likewise, inter-examiner agreement was excellent (ICC = 0.9991, p < 0.0001). The software showed to be accurate for evaluating non-regular shape structures. The mathematical model developed reduced an already small error on the software’s measurements36183189COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES00

Evaluation of local and global atrophy measurement techniques with simulated Alzheimer's disease data

The main goal of this work was to evaluate several well-known methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of atrophy in brain structures using Magnetic Resonance images. For that purpose, we have generated realistic simulated Alzheimer's disease images in which volume changes are modelled with a Finite Element thermoelastic model, which mimic the patterns of change obtained from a cohort of 19 real controls and 27 probable Alzheimer's disease patients. SIENA and BSI results correlate very well with gold standard data (BSI mean absolute error <0.29%; SIENA <0.44%). Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD registration technique provided more satisfactory results than the fluid one. Mean absolute error differences between volume changes given by the FFD-based technique and the gold standard were: sulcal CSF <2.49%; lateral ventricles 2.25%; brain <0.36%; hippocampi <0.42%