scholarly journals Cranial Structure of Varanus komodoensis as Revealed by Computed-Tomographic Imaging

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1078
Author(s):  
Sara Pérez ◽  
Mario Encinoso ◽  
Juan Alberto Corbera ◽  
Manuel Morales ◽  
Alberto Arencibia ◽  
...  
Keyword(s):  
Maximum Intensity Projection ◽  
Helical Ct ◽  
Ct Images ◽  
Ct Imaging ◽  
Ct Scanner ◽  
Imaging Studies ◽  
Computed Tomographic ◽  
Projection Images ◽  
Ct Density ◽  
Normal Head

This study aimed to describe the anatomic features of the normal head of the Komodo dragon (Varanus komodoensis) identified by computed tomography. CT images were obtained in two dragons using a helical CT scanner. All sections were displayed with a bone and soft tissue windows setting. Head reconstructed, and maximum intensity projection images were obtained to enhance bony structures. After CT imaging, the images were compared with other studies and reptile anatomy textbooks to facilitate the interpretation of the CT images. Anatomic details of the head of the Komodo dragon were identified according to the CT density characteristics of the different organic tissues. This information is intended to be a useful initial anatomic reference in interpreting clinical CT imaging studies of the head and associated structures in live Komodo dragons.

scholarly journals Anatomical Assessment of the Thorax in the Neonatal Foal Using Computed Tomography Angiography, Sectional Anatomy, and Gross Dissections

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1045
Author(s):  
Alberto Arencibia ◽  
Juan Alberto Corbera ◽  
Gregorio Ramírez ◽  
María Luisa Díaz-Bertrana ◽  
Lidia Pitti ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Helical Ct ◽  
Ct Imaging ◽  
Ct Scanner ◽  
Cardiac Volume ◽  
Sectional Anatomy ◽  
Contrast Enhanced ◽  
Ct Density

The purpose of this study was to correlate the anatomic features of the normal thorax of neonatal foals identified by CTA, with anatomical sections and gross dissections. Contrast-enhanced transverse CTA images were obtained in three neonatal foals using a helical CT scanner. All sections were imaged with a bone, mediastinal, and lung windows setting. Moreover, cardiac volume-rendered reconstructed images were obtained. After CT imaging, the cadaver foals were sectioned and dissected to facilitate the interpretation of the intrathoracic cardiovascular structures to the corresponding CTA images. Anatomic details of the thorax of neonatal foals were identified according to the characteristics of CT density of the different organic tissues and compared with the corresponding anatomical sections and gross dissections. The information obtained provided a valid anatomic pattern of the thorax of foals, and useful information for CTA studies of this region.

scholarly journals Cervical Spine Surgery with Intraoperative CT Imaging System using A Self-Moving Helical CT Scanner Gantry enabling Intraoperative 3D Imaging

2001 ◽  
Vol 15 (1) ◽  
pp. 35-40
Author(s):  
Toshiaki Kodera ◽  
Toshihiko Kubota ◽  
Masanori Kabuto ◽  
Yuji Handa ◽  
Hisamasa Ishii ◽  
...  
Keyword(s):  
Cervical Spine ◽  
Spine Surgery ◽  
3D Imaging ◽  
Imaging System ◽  
Helical Ct ◽  
Ct Imaging ◽  
Ct Scanner ◽  
Intraoperative Ct ◽  
Intraoperative 3D Imaging ◽  
Cervical Spine Surgery

scholarly journals Feline injection site sarcoma: computed-tomographic density and assessment of tumor dimensions by different methods

2017 ◽  
Vol 37 (10) ◽  
pp. 1113-1118
Author(s):  
Karen Maciel Zardo ◽  
Lucas Petri Damiani ◽  
Julia Maria Matera ◽  
Ana Carolina B.C. Fonseca-Pinto
Keyword(s):  
Physical Examination ◽  
Injection Site ◽  
Malignant Neoplasm ◽  
Hounsfield Unit ◽  
Ct Images ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Definition Of ◽  
Ct Density ◽  
Contrast Ct

ABSTRACT: Feline injection site sarcoma is a malignant neoplasm with digitiform projections into muscular planes that are ill recognized during physical examination and may compromise tumor margin demarcation. This study compared tumoral size of 32 cats measured by different methods, and evaluated the CT density of 10 tumoral tissues (Hounsfield unit) based on histograms. Tumor axes were measured by physical examination and CT images. Larger craniocaudal axis measurements were obtained following multiplanar reconstruction of pre- and post-contrast CT images (p=0.049 and p=0.041 respectively); dorsoventral axis measurements taken from post-contrast CT images were also larger (p=0.010). Tumor volume estimates increased following contrast-enhancement. Histograms tended to produce two peaks: one in the fat and another in the soft tissue attenuation range. Multiplanar reconstructed post-contrast CT images provided clearer definition of tumor margins and more judicious determination of tumor size. A tendency of common FISS attenuation profile could be described.

MO-D-330A-05: Cone-Helical CT Imaging Using the 256-Row (Cone Beam) CT Scanner

2006 ◽  
Vol 33 (6Part14) ◽  
pp. 2159-2159
Author(s):  
S Mori ◽  
M Endo ◽  
T Obata ◽  
S Tanada
Keyword(s):  
Cone Beam Ct ◽  
Helical Ct ◽  
Ct Imaging ◽  
Cone Beam ◽  
Ct Scanner

scholarly journals Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ninlawan Thammasiri ◽  
Chutimon Thanaboonnipat ◽  
Nan Choisunirachon ◽  
Damri Darawiroj
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Ct Images ◽  
Slice Thickness ◽  
Maximum Width ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

Method for transforming CT images for attenuation correction in PET/CT imaging

2006 ◽  
Vol 33 (4) ◽  
pp. 976-983 ◽  
Author(s):  
Jonathan P. J. Carney ◽  
David W. Townsend ◽  
Vitaliy Rappoport ◽  
Bernard Bendriem
Keyword(s):  
Attenuation Correction ◽  
Ct Images ◽  
Ct Imaging ◽  
Pet Ct
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