FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications.

The correlation of epicardial adipose tissue on postmortem CT with coronary artery stenosis as determined by autopsy

The goal of this study was to assess whether epicardial and paracardial adipose tissue volumes, as determined by computed tomography (CT), correlate with coronary artery stenosis as determined by autopsy. The postmortem CT data and autopsy findings of 116 adult human decedents were retrospectively compared. Subjects were classified into three groups according to their degree of coronary artery stenosis: ≥50, <50%, and no stenosis. Epicardial and paracardial adipose tissue volumes were calculated based on manual segmentation after threshold based masking. In addition, epicardial adipose tissue thickness was measured using a caliper. All three parameters (thickness of epicardial fat and volumes of both epicardial and paracardial fat) were compared among the three groups and correlated with the degree of coronary artery stenosis. The group with no coronary artery stenosis showed the lowest mean values of epicardial adipose tissue volume, while the coronary artery stenosis ≥50 % group showed the highest volume. All measured variables (thickness of epicardial fat and volumes of both epicardial and paracardial fat) correlated significantly with the grade of coronary artery stenosis, even after controlling for BMI, however, epicardial adipose tissue volume exhibited the strongest correlation. This study reveals that there is an association between the degree of coronary artery stenosis and the amount of epicardial fat tissue: The larger the volume of epicardial fat, the higher the degree of coronary artery stenosis.

Coronary Artery Tracking in 3D Cardiac CT Images Using Local Morphological Reconstruction Operators

Automatic segmentation and tracking of the coronary artery tree from Cardiac Multislice-CT images is an important goal to improve the diagnosis and treatment of coronary artery disease. This paper presents a semi-automatic algorithm (one input point per vessel) based on morphological grayscale local reconstructions in 3D images devoted to the extraction of the coronary artery tree. The algorithm has been evaluated in the framework of the Coronary Artery Tracking Challenge 2008 [1], obtaining consistent results in overlapping measurements (a mean of 70% of the vessel well tracked). Poor results in accuracy measurements suggest that future work should refine the centerline extraction. The algorithm can be efficiently implemented and its general strategy can be easily extrapolated to a completely automated centerline extraction or to a user interactive vessel extraction

Accumulation de dose à partir de champs de déformation 4D appliqués aux traitements au CyberKnife et à l'IMRT

Le cancer pulmonaire est la principale cause de décès parmi tous les cancers au Canada. Le pronostic est généralement faible, de l'ordre de 15% de taux de survie après 5 ans. Les déplacements internes des structures anatomiques apportent une incertitude sur la précision des traitements en radio-oncologie, ce qui diminue leur efficacité. Dans cette optique, certaines techniques comme la radio-chirurgie et la radiothérapie par modulation de l'intensité (IMRT) visent à améliorer les résultats cliniques en ciblant davantage la tumeur. Ceci permet d'augmenter la dose reçue par les tissus cancéreux et de réduire celle administrée aux tissus sains avoisinants. Ce projet vise à mieux évaluer la dose réelle reçue pendant un traitement considérant une anatomie en mouvement. Pour ce faire, des plans de CyberKnife et d'IMRT sont recalculés en utilisant un algorithme Monte Carlo 4D de transport de particules qui permet d'effectuer de l'accumulation de dose dans une géométrie déformable. Un environnement de simulation a été développé afin de modéliser ces deux modalités pour comparer les distributions de doses standard et 4D. Les déformations dans le patient sont obtenues en utilisant un algorithme de recalage déformable d'image (DIR) entre les différentes phases respiratoire générées par le scan CT 4D. Ceci permet de conserver une correspondance de voxels à voxels entre la géométrie de référence et celles déformées. La DIR est calculée en utilisant la suite ANTs («Advanced Normalization Tools») et est basée sur des difféomorphismes. Une version modifiée de DOSXYZnrc de la suite EGSnrc, defDOSXYZnrc, est utilisée pour le transport de particule en 4D. Les résultats sont comparés à une planification standard afin de valider le modèle actuel qui constitue une approximation par rapport à une vraie accumulation de dose en 4D.

Accumulation de dose à partir de champs de déformation 4D appliqués aux traitements au CyberKnife et à l'IMRT

Le cancer pulmonaire est la principale cause de décès parmi tous les cancers au Canada. Le pronostic est généralement faible, de l'ordre de 15% de taux de survie après 5 ans. Les déplacements internes des structures anatomiques apportent une incertitude sur la précision des traitements en radio-oncologie, ce qui diminue leur efficacité. Dans cette optique, certaines techniques comme la radio-chirurgie et la radiothérapie par modulation de l'intensité (IMRT) visent à améliorer les résultats cliniques en ciblant davantage la tumeur. Ceci permet d'augmenter la dose reçue par les tissus cancéreux et de réduire celle administrée aux tissus sains avoisinants. Ce projet vise à mieux évaluer la dose réelle reçue pendant un traitement considérant une anatomie en mouvement. Pour ce faire, des plans de CyberKnife et d'IMRT sont recalculés en utilisant un algorithme Monte Carlo 4D de transport de particules qui permet d'effectuer de l'accumulation de dose dans une géométrie déformable. Un environnement de simulation a été développé afin de modéliser ces deux modalités pour comparer les distributions de doses standard et 4D. Les déformations dans le patient sont obtenues en utilisant un algorithme de recalage déformable d'image (DIR) entre les différentes phases respiratoire générées par le scan CT 4D. Ceci permet de conserver une correspondance de voxels à voxels entre la géométrie de référence et celles déformées. La DIR est calculée en utilisant la suite ANTs («Advanced Normalization Tools») et est basée sur des difféomorphismes. Une version modifiée de DOSXYZnrc de la suite EGSnrc, defDOSXYZnrc, est utilisée pour le transport de particule en 4D. Les résultats sont comparés à une planification standard afin de valider le modèle actuel qui constitue une approximation par rapport à une vraie accumulation de dose en 4D.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

X-ray computed tomography to quantify tree rooting spatial distributions

Poor root development due to constraining soil conditions could be an important factor influencing health of urban trees. Therefore, there is a need for efficient techniques to analyze the spatial distribution of tree roots. An analytical procedure for describing tree rooting patterns from X-ray computed tomography (CT) data is described and illustrated. Large irregularly shaped specimens of undisturbed sandy soil were sampled from Various positions around the base of trees using field impregnation with epoxy resin, to stabilize the cohesionless soil. Cores approximately 200 mm in diameter by 500 mm in height were extracted from these specimens. These large core samples were scanned with a medical X-ray CT device, and contiguous images of soil slices (2 mm thick) were thus produced. X-ray CT images are regarded as regularly-spaced sections through the soil although they are not actual 2D sections but matrices of voxels similar to 0.5 mm x 0.5 mm x 2 mm. The images were used to generate the equivalent of horizontal root contact maps from which three-dimensional objects, assumed to be roots, were reconstructed. The resulting connected objects were used to derive indices of the spatial organization of roots, namely: root length distribution, root length density, root growth angle distribution, root spatial distribution, and branching intensity. The successive steps of the method, from sampling to generation of indices of tree root organization, are illustrated through a case study examining rooting patterns of valuable urban trees. (C) 1999 Elsevier Science B.V. All rights reserved.

Bedside estimation of recruitable alveolar collapse and hyperdistension by electrical impedance tomography

To present a novel algorithm for estimating recruitable alveolar collapse and hyperdistension based on electrical impedance tomography (EIT) during a decremental positive end-expiratory pressure (PEEP) titration. Technical note with illustrative case reports. Respiratory intensive care unit. Patients with acute respiratory distress syndrome. Lung recruitment and PEEP titration maneuver. Simultaneous acquisition of EIT and X-ray computerized tomography (CT) data. We found good agreement (in terms of amount and spatial location) between the collapse estimated by EIT and CT for all levels of PEEP. The optimal PEEP values detected by EIT for patients 1 and 2 (keeping lung collapse < 10%) were 19 and 17 cmH(2)O, respectively. Although pointing to the same non-dependent lung regions, EIT estimates of hyperdistension represent the functional deterioration of lung units, instead of their anatomical changes, and could not be compared directly with static CT estimates for hyperinflation. We described an EIT-based method for estimating recruitable alveolar collapse at the bedside, pointing out its regional distribution. Additionally, we proposed a measure of lung hyperdistension based on regional lung mechanics.

Assessment of linear and angular measurements on three-dimensional cone-beam computed tomographic images

Objective. The purpose of this research was to provide further evidence to demonstrate the precision and accuracy of maxillofacial linear and angular measurements obtained by cone-beam computed tomography (CBCT) images. Study design. The study population consisted of 15 dry human skulls that were submitted to CBCT, and 3-dimensional (3D) images were generated. Linear and angular measurements based on conventional craniometric anatomical landmarks, and were identified in 3D-CBCT images by 2 radiologists twice each independently. Subsequently, physical measurements were made by a third examiner using a digital caliper and a digital goniometer. Results. The results demonstrated no statistically significant difference between inter-and intra-examiner analysis. Regarding accuracy test, no statistically significant differences were found of the comparison between the physical and CBCT-based linear and angular measurements for both examiners (P = .968 and .915, P = .844 and .700, respectively). Conclusions. 3D-CBCT images can be used to obtain dimensionally accurate linear and angular measurements from bony maxillofacial structures and landmarks. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2009; 108: 430-436)

Thoracic wall reconstruction using ultrasound images to model/bend the thoracic prosthesis for correction of pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior thoracic wall. The surgical correction of such deformity, using Nuss procedure, consists in the placement of a personalized convex prosthesis into sub-sternal position to correct the deformity. The aim of this work is the CT-scan substitution by ultrasound imaging for the pre-operative diagnosis and pre-modeling of the prosthesis, in order to avoid patient radiation exposure. To accomplish this, ultrasound images are acquired along an axial plane, followed by a rigid registration method to obtain the spatial transformation between subsequent images. These images are overlapped to reconstruct an axial plane equivalent to a CT-slice. A phantom was used to conduct preliminary experiments and the achieved results were compared with the corresponding CT-data, showing that the proposed methodology can be capable to create a valid approximation of the anterior thoracic wall, which can be used to model/bend the prosthesis

Assessment of 3D scanners for modeling Pectus Carinatum corrective bar

Pectus Carinatum (PC) is a chest deformity consisting on the anterior protrusion of the sternum and adjacent costal cartilages. Non-operative corrections, such as the orthotic compression brace, require previous information of the patient chest surface, to improve the overall brace fit. This paper focuses on the validation of the Kinect scanner for the modelling of an orthotic compression brace for the correction of Pectus Carinatum. To this extent, a phantom chest wall surface was acquired using two scanner systems – Kinect and Polhemus FastSCAN – and compared through CT. The results show a RMS error of 3.25mm between the CT data and the surface mesh from the Kinect sensor and 1.5mm from the FastSCAN sensor

Variations of the soft tissue thicknesses external to the ribs in Pectus Excavatum patients

Background: Surgical repair of pectus excavatum (PE) has become more popular due to improvements in the minimally invasive Nuss procedure. The pre-surgical assessment of PE patients requires Computerized Tomography (CT), as the malformation characteristics vary from patient to patient. Objective: This work aims to characterize soft tissue thickness (STT) external to the ribs among PE patients. It also presents a comparative analysis between the anterior chest wall surface before and after surgical correction. Methods: Through surrounding tissue segmentation in CT data, STT values were calculated at different lines along the thoracic wall, with a reference point in the intersection of coronal and median planes. The comparative analysis between the two 3D anterior chest surfaces sets a surgical correction influence area (SCIA) and a volume of interest (VOI) based on image processing algorithms, 3D surface algorithms, and registration methods. Results: There are always variations between left and right side STTs (2.54±2.05 mm and 2.95±2.97 mm for female and male patients, respectively). STTs are dependent on age, sex, and body mass index of each patient. On female patients, breast tissue induces additional errors in bar manual

Variations of the soft tissue thicknesses external to the ribs in pectus excavatum patients

Background: Surgical repair of pectus excavatum (PE) has become more popular due to improvements in the minimally invasive Nuss procedure. The pre-surgical assessment of PE patients requires Computerized Tomography (CT), as the malformation characteristics vary from patient to patient. Objective: This work aims to characterize soft tissue thickness (STT) external to the ribs among PE patients. It also presents a comparative analysis between the anterior chest wall surface before and after surgical correction. Methods: Through surrounding tissue segmentation in CT data, STT values were calculated at different lines along the thoracic wall, with a reference point in the intersection of coronal and median planes. The comparative analysis between the two 3D anterior chest surfaces sets a surgical correction influence area (SCIA) and a volume of interest (VOI) based on image processing algorithms, 3D surface algorithms, and registration methods. Results: There are always variations between left and right side STTs (2.54±2.05 mm and 2.95±2.97 mm for female and male patients, respectively). STTs are dependent on age, sex, and body mass index of each patient. On female patients, breast tissue induces additional errors in bar manual

Assessment of 3D scanners for modeling pectus carinatum corrective bar

Pectus Carinatum (PC) is a chest deformity consisting on the anterior protrusion of the sternum and adjacent costal cartilages. Non-operative corrections, such as the orthotic compression brace, require previous information of the patient chest surface, to improve the overall brace fit. This paper focuses on the validation of the Kinect scanner for the modelling of an orthotic compression brace for the correction of Pectus Carinatum. To this extent, a phantom chest wall surface was acquired using two scanner systems – Kinect and Polhemus FastSCAN – and compared through CT. The results show a RMS error of 3.25mm between the CT data and the surface mesh from the Kinect sensor and 1.5mm from the FastSCAN sensor.