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Journal articles on the topic 'Computer-Aided Tomography'

Author: Grafiati

Published: 25 May 2024

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1

Juchems, Markus S., Andrea S. Ernst, Hans-Juergen Brambs, and Andrik J. Aschoff. "Computer-aided detection in computer tomography colonography: a review." Expert Opinion on Medical Diagnostics 2, no. 5 (April 29, 2008): 487–95. http://dx.doi.org/10.1517/17530059.2.5.487.

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van Ginneken, B. "Computer-Aided Diagnosis in Thoracic Computed Tomography." Imaging Decisions MRI 12, no. 3 (September 2008): 11–22. http://dx.doi.org/10.1111/j.1617-0830.2009.00129.x.

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3

Woodworth, B. A., A. G. Chiu, N. A. Cohen, D. W. Kennedy, B. W. O'Malley, and J. N. Palmer. "Real-time computed tomography image update for endoscopic skull base surgery." Journal of Laryngology & Otology 122, no. 4 (August 16, 2007): 361–65. http://dx.doi.org/10.1017/s0022215107000485.

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AbstractIntroduction:The development of computer-aided systems for endoscopic sinus surgery has enabled surgical navigation through diseased or surgically altered sinus anatomy with increased confidence. However, conventional computer-aided systems do not provide intra-operative updated computed tomography imaging. We describe the technical aspects of the xCAT™, a new intra-operative mobile volume computed tomography scanner.Technical report:A patient with a malignant melanoma unwittingly removed at another hospital underwent surgery for removal of the lateral nasal wall and directed biopsies, in an attempt to identify the site of tumour origin. The procedure was performed with the GE InstaTrak 3500 PlusTM computer-aided system, updated with intra-operative computed tomography images. Intra-operative, updated images were integrated successfully into the InstaTrak system, and these images were consistent with the observed endoscopic anatomy.Conclusion:The xCAT intra-operative mobile volume computed tomography scanner is a technological advancement that can assist the endoscopic sinus surgeon when performing complex rhinological and skull base procedures.

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Saba, Luca, Giancarlo Caddeo, and Giorgio Mallarini. "Computer-Aided Detection of Pulmonary Nodules in Computed Tomography." Journal of Computer Assisted Tomography 31, no. 4 (July 2007): 611–19. http://dx.doi.org/10.1097/rct.0b013e31802e29bf.

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Ziyad, Shabana Rasheed, Venkatachalam Radha, and Thavavel Vayyapuri. "Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 16–26. http://dx.doi.org/10.2174/1573405615666190206153321.

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Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

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Ribeiro, Luisa, Rui Bernardes, and José Cunha-Vaz. "Computer-aided Analysis of Fundus Photographs." European Ophthalmic Review 05, no. 02 (2011): 104. http://dx.doi.org/10.17925/eor.2011.05.02.104.

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Colour fundus photography is the most frequently used imaging modality because it is non-invasive, well accepted by patients and above all, because it allows documentation and automated analysis of the ophthalmoscopic examination. Colour fundus photography is considered crucial for diabetic retinopathy management to identify disease and its progression in clinical practice. This article focuses on automated computer-aided analysis of fundus digital photographs with special emphasis on microaneurysm dynamics. Together with optical coherence tomography measurements of extracellular space and retinal thickness, both based on non-invasive procedures, this technique allows close follow-up of the main changes in the diabetic retina. Determination of the activity of the retinal disease and individual risk profiles using these non-invasive procedures contribute to personalised management of diabetic retinopathy by identifying eyes at risk from vision-threatening complications, such as macular oedema and proliferative retinopathy.

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Ahmed, Mamdouhh, Mariam Kamel Salah, and Nesrine Khairy. "Computer-Aided Design/Computer-Aided Manufacturing Cutting Guides for Odontectomy of Deeply Impacted Mandibular Third Molars." Open Access Macedonian Journal of Medical Sciences 6, no. 12 (December 19, 2018): 2395–401. http://dx.doi.org/10.3889/oamjms.2018.371.

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AIM: To evaluate a new technique for surgical removal of deeply impacted mandibular third molars (DIMTM), using computer-guided cutting guide to maintain inferior alveolar nerve (IAN) integrity and the covering buccal bone. PATIENTS AND METHODS: Eighteen cases indicated for removal of DIMTM. Cone-beam Computed Tomography (CBCTs) used to determine the tooth’s relation to the IAN. Computer-guided software used for fabrication of surgical cutting guide stent to expose the impacted tooth and repositioning of bone after odontectomy without fixation. Clinical assessment included a neurosensory deficit of IAN, pain using a visual analogue scale (VAS), facial swelling, and maximal mouth opening (MMO). CBCTs were taken immediately and six months postoperatively to evaluate position and healing of bone. RESULTS: None of the patients showed a permanent neurological deficit of IAN while all patients showed normal parameters of pain, facial swelling and MMO. CONCLUSION: this technique has shown the accurate determination of the bony window cuts with subsequent preservation of IAN and external oblique ridge.

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Wyber, Ron J., and Brian G. Ferguson. "Interferometry and computer‐aided tomography as an acoustic analysis tool." Journal of the Acoustical Society of America 115, no. 5 (May 2004): 2547. http://dx.doi.org/10.1121/1.4783706.

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Liu, Jiamin, Sanket Pattanaik, Jianhua Yao, Evrim Turkbey, Weidong Zhang, Xiao Zhang, and Ronald M. Summers. "Computer aided detection of epidural masses on computed tomography scans." Computerized Medical Imaging and Graphics 38, no. 7 (October 2014): 606–12. http://dx.doi.org/10.1016/j.compmedimag.2014.04.007.

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Marom, Shlomo A., and Martin J. Linden. "Computer aided stress analysis of long bones utilizing computed tomography." Journal of Biomechanics 23, no. 5 (January 1990): 399–404. http://dx.doi.org/10.1016/0021-9290(90)90294-d.

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Nikolaev, A. E., V. Yu Chernina, I. A. Blokhin, A. N. Shapiev, A. P. Gonchar, V. A. Gombolevskiy, A. V. Petraikin, A. Yu Silin, G. D. Petrova, and S. P. Morozov. "The future of computer-aided diagnostics in chest computed tomography." Khirurgiya. Zhurnal im. N.I. Pirogova, no. 12 (2019): 91. http://dx.doi.org/10.17116/hirurgia201912191.

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12

Chang, Chin-Chen, Hong-Hao Chen, Yeun-Chung Chang, Ming-Yang Yang, Chung-Ming Lo, Wei-Chun Ko, Yee-Fan Lee, Kao-Lang Liu, and Ruey-Feng Chang. "Computer-aided diagnosis of liver tumors on computed tomography images." Computer Methods and Programs in Biomedicine 145 (July 2017): 45–51. http://dx.doi.org/10.1016/j.cmpb.2017.04.008.

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13

Kravchenko, V. F., V. I. Ponomaryov, R. Reyes-Reyes, and E. M. Rendon-Gonzalez. "Computer-Aided Diagnosis System for Lung Nodule Classification Using Computer Tomography Scan Images." Физические основы приборостроения 8, no. 2 (June 15, 2019): 50–61. http://dx.doi.org/10.25210/jfop-1902-050061.

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Chan, Tao. "Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain." Computerized Medical Imaging and Graphics 31, no. 4-5 (June 2007): 285–98. http://dx.doi.org/10.1016/j.compmedimag.2007.02.010.

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15

Kirpatovskaya, L. Ye, T. A. Akhadov, L. K. Dzeranova, Ye I. Marova, A. K. Kravtsov, R. Ya Sneghiryova, Ye Ye Makarovskaya, and I. A. Ilovaiskaya. "Potentialities of computer-aided tomography and magnetic imaging in the diagnosis of hypophyseal prolactinomas." Problems of Endocrinology 41, no. 1 (February 15, 1995): 14–18. http://dx.doi.org/10.14341/probl11335.

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Fifty-seven women with the hyperoprolactinemic syndrome coursing from 6 months to 13 years were examined. Total blood serum immunoreactive prolactin was measured, and craniograms made in all the patients, computer-aided tomography of the head was carried out in 49, and magnetic imaging in 38 patients. A total of 29 micro-, 16 macroadenomas, 1 chaniopharyngiomas, and 2 cases of “empty sella turcica” were revealed. Efficacy of prolactinoma detection by computer-aided tomography and magnetic imaging was 63.2 and 78.9%, respectively. Hence, these methods may effectively diagnose hypophyseal prolactinomas, but magnetic imaging is preferable for the detection of microprolactinomas and in cases with suspected volumic processes of the brain.

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Rahman, Imran A., and Selena Y. Smith. "Virtual paleontology: computer-aided analysis of fossil form and function." Journal of Paleontology 88, no. 4 (July 2014): 633–35. http://dx.doi.org/10.1666/13-001i.

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‘Virtual paleontology’ entails the use of computational methods to assist in the three-dimensional (3-D) visualization and analysis of fossils, and has emerged as a powerful approach for research on the history of life. Three-dimensional imaging techniques allow poorly understood or previously unknown anatomies of fossil plants, invertebrates, and vertebrates, as well as microfossils and trace fossils, to be described in much greater detail than formerly possible, and are applicable to a wide range of preservation types and specimen sizes (Table 1). These methods include non-destructive high-resolution scanning technologies such as conventional X-ray micro-tomography and synchrotron-based X-ray tomography. In addition, form and function can be rigorously investigated through quantitative analysis of computer models, for example finite-element analysis.

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C, Vinod, and Menaka D. "Computer Aided Detection of Nodule from Computed Tomography Images of Lung." International Research Journal on Advanced Science Hub 3, Special Issue ICARD 3S (March 23, 2021): 96–100. http://dx.doi.org/10.47392/irjash.2021.073.

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Yin, Nan, Cong Shen, Fuwen Dong, Jun Wang, Youmin Guo, and Lu Bai. "Computer-aided identification of interstitial lung disease based on computed tomography." Journal of X-Ray Science and Technology 27, no. 4 (September 4, 2019): 591–603. http://dx.doi.org/10.3233/xst-180460.

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19

Citardi, Martin J., Ryan P. Gallivan, Pete S. Batra, Calvin R. Maurer, Torsten Rohlfing, Hwan-Jung Roh, and Donald C. Lanza. "Quantitative Computer-Aided Computed Tomography Analysis of Sphenoid Sinus Anatomical Relationships." American Journal of Rhinology 18, no. 3 (May 2004): 173–78. http://dx.doi.org/10.1177/194589240401800308.

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Abduh, Zaid, Manal Abdel Wahed, and Yasser M. Kadah. "Robust Computer-Aided Detection of Pulmonary Nodules from Chest Computed Tomography." Journal of Medical Imaging and Health Informatics 6, no. 3 (June 1, 2016): 693–99. http://dx.doi.org/10.1166/jmihi.2016.1731.

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21

Kim, D. Y., and J. W. Park. "Computer-aided detection of kidney tumor on abdominal computed tomography scans." Acta Radiologica 45, no. 7 (November 2004): 791–95. http://dx.doi.org/10.1080/02841850410001312.

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22

Sethi, Gaurav, and Barjinder S. Saini. "Computer aided diagnosis system for abdomen diseases in computed tomography images." Biocybernetics and Biomedical Engineering 36, no. 1 (2016): 42–55. http://dx.doi.org/10.1016/j.bbe.2015.10.008.

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23

Schoepf, U. Joseph, Alex C. Schneider, Marco Das, Susan A. Wood, Jugesh I. Cheema, and Philip Costello. "Pulmonary Embolism: Computer-aided Detection at Multidetector Row Spiral Computed Tomography." Journal of Thoracic Imaging 22, no. 4 (November 2007): 319–23. http://dx.doi.org/10.1097/rti.0b013e31815842a9.

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F., Adam, Hammer D., Pape D., and Kohn D. "Femoral anatomy, computed tomography and computer-aided design of prosthetic implants." Archives of Orthopaedic and Trauma Surgery 122, no. 5 (June 1, 2002): 262–68. http://dx.doi.org/10.1007/s00402-001-0361-8.

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25

Cui, Hang, Liang Hu, and Ling Chi. "Advances in Computer-Aided Medical Image Processing." Applied Sciences 13, no. 12 (June 13, 2023): 7079. http://dx.doi.org/10.3390/app13127079.

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The primary objective of this study is to provide an extensive review of deep learning techniques for medical image recognition, highlighting their potential for improving diagnostic accuracy and efficiency. We systematically organize the paper by first discussing the characteristics and challenges of medical imaging techniques, with a particular focus on magnetic resonance imaging (MRI) and computed tomography (CT). Subsequently, we delve into direct image processing methods, such as image enhancement and multimodal medical image fusion, followed by an examination of intelligent image recognition approaches tailored to specific anatomical structures. These approaches employ various deep learning models and techniques, including convolutional neural networks (CNNs), transfer learning, attention mechanisms, and cascading strategies, to overcome challenges related to unclear edges, overlapping regions, and structural distortions. Furthermore, we emphasize the significance of neural network design in medical imaging, concentrating on the extraction of multilevel features using U-shaped structures, dense connections, 3D convolution, and multimodal feature fusion. Finally, we identify and address the key challenges in medical image recognition, such as data quality, model interpretability, generalizability, and computational resource requirements. By proposing future directions in data accessibility, active learning, explainable AI, model robustness, and computational efficiency, this study paves the way for the successful integration of AI in clinical practice and enhanced patient care.

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Abbate, Vincenzo, Giulia Togo, Umberto Committeri, Fernando Zarone, Gilberto Sammartino, Alessandra Valletta, Andrea Elefante, Luigi Califano, and Giovanni Dell’Aversana Orabona. "Full Digital Workflow for Mandibular Ameloblastoma Management: Showcase for Technical Description." Journal of Clinical Medicine 12, no. 17 (August 25, 2023): 5526. http://dx.doi.org/10.3390/jcm12175526.

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This is a showcase for technical description of a full digital workflow aimed to reconstruct and prosthetically rehabilitate the mandible after surgical resection. The surgery was performed following a computer-aided design and computer-aided manufacturing (CAD-CAM) guided workflow, using 3D reconstruction of the mandible and the fibula. After 2 years, when the ossification of the flap was reached and verified by a computed tomography (CT) scan, surgery was performed using a two-step implant rehabilitation, with successful outcomes.

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Wang, L., and S. I. Rokhlin. "Application of computer-aided design approach for computer simulation of radiography and 3-D tomography." NDT & E International 25, no. 4-5 (August 1992): 238. http://dx.doi.org/10.1016/0963-8695(92)90309-5.

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Gardner, Lauren, Toby Gillgrass, and Mark Devlin. "The role of three-dimensional imaging in patients with cleft lip and palate." Faculty Dental Journal 4, no. 3 (July 2013): 118–23. http://dx.doi.org/10.1308/204268513x13703528618960.

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Three-dimensional (3D) imaging is revolutionising patient assessment, diagnosis, management and treatment planning. Restorative dentistry is using optical scanning such as the computer aided design/computer aided manufacture systems to help with tooth preparation design and construction of fixed prosthodontics. Other specialties in dentistry are frequently employing cone beam computed tomography (CBCT) to facilitate 3D imaging. This article outlines how CBCT and 3D sterophotogrammetry have been used in the management of cleft lip and palate with reference to the cleft team based at Glasgow Dental Hospital.

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Attallah, Omneya. "A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images." DIGITAL HEALTH 8 (January 2022): 205520762210925. http://dx.doi.org/10.1177/20552076221092543.

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The accurate and rapid detection of the novel coronavirus infection, coronavirus is very important to prevent the fast spread of such disease. Thus, reducing negative effects that influenced many industrial sectors, especially healthcare. Artificial intelligence techniques in particular deep learning could help in the fast and precise diagnosis of coronavirus from computed tomography images. Most artificial intelligence-based studies used the original computed tomography images to build their models; however, the integration of texture-based radiomics images and deep learning techniques could improve the diagnostic accuracy of the novel coronavirus diseases. This study proposes a computer-assisted diagnostic framework based on multiple deep learning and texture-based radiomics approaches. It first trains three Residual Networks (ResNets) deep learning techniques with two texture-based radiomics images including discrete wavelet transform and gray-level covariance matrix instead of the original computed tomography images. Then, it fuses the texture-based radiomics deep features sets extracted from each using discrete cosine transform. Thereafter, it further combines the fused texture-based radiomics deep features obtained from the three convolutional neural networks. Finally, three support vector machine classifiers are utilized for the classification procedure. The proposed method is validated experimentally on the benchmark severe respiratory syndrome coronavirus 2 computed tomography image dataset. The accuracies attained indicate that using texture-based radiomics (gray-level covariance matrix, discrete wavelet transform) images for training the ResNet-18 (83.22%, 74.9%), ResNet-50 (80.94%, 78.39%), and ResNet-101 (80.54%, 77.99%) is better than using the original computed tomography images (70.34%, 76.51%, and 73.42%) for ResNet-18, ResNet-50, and ResNet-101, respectively. Furthermore, the sensitivity, specificity, accuracy, precision, and F1-score achieved using the proposed computer-assisted diagnostic after the two fusion steps are 99.47%, 99.72%, 99.60%, 99.72%, and 99.60% which proves that combining texture-based radiomics deep features obtained from the three ResNets has boosted its performance. Thus, fusing multiple texture-based radiomics deep features mined from several convolutional neural networks is better than using only one type of radiomics approach and a single convolutional neural network. The performance of the proposed computer-assisted diagnostic framework allows it to be used by radiologists in attaining fast and accurate diagnosis.

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Sherin, A., and R. Rajeswari. "Computer-Aided Diagnosis System for Alzheimer’s Disease Using Positron Emission Tomography Images." Interdisciplinary Sciences: Computational Life Sciences 13, no. 3 (April 3, 2021): 433–42. http://dx.doi.org/10.1007/s12539-020-00409-0.

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Al-Saeed, Yasmeen, Wael A. Gab-Allah, Hassan Soliman, Maysoon F. Abulkhair, Wafaa M. Shalash, and Mohammed Elmogy. "Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans." Computers, Materials & Continua 71, no. 3 (2022): 4871–94. http://dx.doi.org/10.32604/cmc.2022.023638.

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32

Paulraj, Tharcis, and Kezi Selva Vijila Chellliah. "Computer-aided Diagnosis of Lung Cancer in Computed Tomography Scans: A Review." Current Medical Imaging Reviews 14, no. 3 (May 8, 2018): 374–88. http://dx.doi.org/10.2174/1573405613666170111155017.

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El-Regaily, Salsabil A., Mohammed A. Salem, Mohammed H. Abdel Aziz, and Mohammed I. Roushdy. "Survey of Computer Aided Detection Systems for Lung Cancer in Computed Tomography." Current Medical Imaging Reviews 14, no. 1 (December 28, 2017): 3–18. http://dx.doi.org/10.2174/1573405613666170602123329.

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Iwano, Shingo, Mariko Kitano, Keiji Matsuo, Kenichi Kawakami, Wataru Koike, Mariko Kishimoto, Tsutomu Inoue, Yuanzhong Li, and Shinji Naganawa. "Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography." Interactive CardioVascular and Thoracic Surgery 17, no. 1 (March 22, 2013): 59–65. http://dx.doi.org/10.1093/icvts/ivt122.

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Pack, Chulwoo, Seong-Ho Son, and Sung Shin. "Computer aided diagnosis with boosted learning for anomaly detection in microwave tomography." ACM SIGAPP Applied Computing Review 17, no. 3 (November 14, 2017): 39–47. http://dx.doi.org/10.1145/3161534.3161538.

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Lilje, Osu, Erna Lilje, Agostina V. Marano, and Frank H. Gleason. "Three dimensional quantification of biological samples using micro-computer aided tomography (microCT)." Journal of Microbiological Methods 92, no. 1 (January 2013): 33–41. http://dx.doi.org/10.1016/j.mimet.2012.10.006.

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Liao, Chun-Chih, Furen Xiao, Jau-Min Wong, and I. Jen Chiang. "Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography." Computerized Medical Imaging and Graphics 34, no. 7 (October 2010): 563–71. http://dx.doi.org/10.1016/j.compmedimag.2010.03.003.

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Foo, Y. H., J. H. D. Wong, R. R. Azman, Y. L. Leong, and L. K. Tan. "Identification of acute intracranial bleed on computed tomography using computer aided detection." Journal of Physics: Conference Series 1497 (March 2020): 012019. http://dx.doi.org/10.1088/1742-6596/1497/1/012019.

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Syryamkin, V. I., E. N. Bogomolov, and M. S. Kutsov. "Computer-Aided Design of X-Ray Microtomographic Scanners." Advanced Materials Research 1033-1034 (October 2014): 1327–30. http://dx.doi.org/10.4028/www.scientific.net/amr.1033-1034.1327.

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The article is to study the development of computer-aided design of X-ray microtomography - the device for investigating the structure and construction three-dimensional images of organic and inorganic objects on the basis of shadow projections. This article provides basic information regarding CAD of X-ray microtomography and a scheme consisting of three levels. The article also shows basic relations of X-ray computed tomography, the generalized scheme of a X-ray microtomographic scanner. The methods of X-ray imaging of the spatial microstructure and morphometry of materials are described. The main characteristics of an X-ray microtomographic scanner, the X-ray source, X-ray optical elements and mechanical components of the positioning system are shown. The block scheme and software functional scheme for intelligent neural network system of analysis of the internal microstructure of objects are presented. The method of choice of design parameters of CADof X-raymicrotomography is aimed at improving the quality of design and reduce of costs of it. It is supposed to reduce the design time and eliminate the growing number of engineers involved in development and construction of X-ray microtomographic scanners.

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Woo, Jonghye, Byung-Woo Hong, Sunil Kumar, Indranill Basu Ray, and C. C. Jay Kuo. "Multimodal Data Integration for Computer-Aided Ablation of Atrial Fibrillation." Journal of Biomedicine and Biotechnology 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/681303.

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Image-guided percutaneous interventions have successfully replaced invasive surgical methods in some cardiologic practice, where the use of 3D-reconstructed cardiac images, generated by magnetic resonance imaging (MRI) and computed tomography (CT), plays an important role. To conduct computer-aided catheter ablation of atrial fibrillation accurately, multimodal information integration with electroanatomic mapping (EAM) data and MRI/CT images is considered in this work. Specifically, we propose a variational formulation for surface reconstruction and incorporate the prior shape knowledge, which results in a level set method. The proposed method enables simultaneous reconstruction and registration under nonrigid deformation. Promising experimental results show the potential of the proposed approach.

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Lara-Mendes, Sônia T. de O., Camila de Freitas M. Barbosa, Caroline C. Santa-Rosa, and Vinícius C. Machado. "Guided Endodontic Access in Maxillary Molars Using Cone-beam Computed Tomography and Computer-aided Design/Computer-aided Manufacturing System: A Case Report." Journal of Endodontics 44, no. 5 (May 2018): 875–79. http://dx.doi.org/10.1016/j.joen.2018.02.009.

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David, Ovidiu Tiberiu, Camelia Szuhanek, Robert Angelo Tuce, Andra Patricia David, and Marius Leretter. "Polylactic Acid 3D Printed Drill Guide for Dental Implants Using CBCT." Revista de Chimie 68, no. 2 (March 15, 2017): 341–42. http://dx.doi.org/10.37358/rc.17.2.5449.

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The aim of the present study is to describe a method for setting up a polylactic acid (PLA) 3D printed surgical guide for the minimally invasive insertion of dental implants using the CBCT (cone beam computed tomography) acquisition without occlusal contact and CAD � CAM (computer-aided design/computer assisted manufacture) software for its design, production and testing.

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Mediavilla Guzmán, Alfonso, Elena Riad Deglow, Álvaro Zubizarreta-Macho, Rubén Agustín-Panadero, and Sofía Hernández Montero. "Accuracy of Computer-Aided Dynamic Navigation Compared to Computer-Aided Static Navigation for Dental Implant Placement: An In Vitro Study." Journal of Clinical Medicine 8, no. 12 (December 2, 2019): 2123. http://dx.doi.org/10.3390/jcm8122123.

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Aim: To analyze the accuracy capability of two computer-aided navigation procedures for dental implant placement. Materials and Methods: A total of 40 dental implants were selected, which were randomly distributed into two study groups, namely, group A, consisting of those implants that were placed using a computer-aided static navigation system (n = 20) (guided implant (GI)) and group B, consisting of those implants that were placed using a computer-aided dynamic navigation system (n = 20) (navigation implant (NI)). The placement of the implants from group A was performed using surgical templates that were designed using 3D implant-planning software based on preoperative cone-beam computed tomography (CBCT) and a 3D extraoral surface scan, and the placement of group B implants was planned and performed using the dynamic navigation system. After placing the dental implants, a second CBCT was performed and the degree of accuracy of the planning and placement of the implants was analyzed using therapeutic planning software and Student’s t-test. Results: The paired t-test revealed no statistically significant differences between GI and NI at the coronal (p = 0.6535) and apical (p = 0.9081) levels; however, statistically significant differences were observed between the angular deviations of GI and NI (p = 0.0272). Conclusion: Both computer-aided static and dynamic navigation procedures allow accurate implant placement.

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Zubizarreta-Macho, Álvaro, Ana de Pedro Muñoz, Elena Riad Deglow, Rubén Agustín-Panadero, and Jesús Mena Álvarez. "Accuracy of Computer-Aided Dynamic Navigation Compared to Computer-Aided Static Procedure for Endodontic Access Cavities: An In Vitro Study." Journal of Clinical Medicine 9, no. 1 (January 2, 2020): 129. http://dx.doi.org/10.3390/jcm9010129.

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Purpose: To analyze the accuracy of two computer-aided navigation techniques to guide the performance of endodontic access cavities compared with the conventional access procedure. Materials and Methods: A total of 30 single-rooted anterior teeth were selected, which were randomly distributed into three study groups: Group A—guided performance of endodontic access cavities through computer-aided static navigation system (n = 10) (SN); Group B—guided performance of endodontic access cavities through computer-aided dynamic navigation system (n = 10) (DN); and Group C—manual (freehand) performance of endodontic access cavities (n = 10) (MN). The endodontic access cavities of the SN group were performed with a stereolithography template designed on 3D implant planning software, based on preoperative cone-beam computed tomography (CBCT) and a 3D extraoral surface scan, and endodontic access cavities of the DN group were planned and performed by the dynamic navigation system. After endodontic access cavities were performed, a second CBCT was done, and the degree of accuracy between the planned and performed endodontic access cavities was analyzed using therapeutic planning software and Student’s t-test. Results: Paired t-test revealed no statistically significant differences between SN and DN at the coronal (p = 0.6542), apical (p = 0.9144), or angular (p = 0.0724) level; however, statistically significant differences were observed between the two computer-aided navigation techniques and the MN group at the coronal (p < 0.0001), apical (p < 0.0001), and angular (p < 0.0001) level. Conclusion: Both computer-aided static and dynamic navigation procedures allowed accurate performance of endodontic access cavities.

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Rückert, Darius, Yuanhao Wang, Rui Li, Ramzi Idoughi, and Wolfgang Heidrich. "NeAT." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–13. http://dx.doi.org/10.1145/3528223.3530121.

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In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods. The adaptive explicit representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while the neural features act as a neural regularizer for the 3D reconstruction. The NeAT framework is designed specifically for the tomographic setting, which consists only of semi-transparent volumetric scenes instead of opaque objects. In this setting, NeAT outperforms the quality of existing optimization-based tomography solvers while being substantially faster. https://github.com/darglein/NeAT

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Dvoryashina, I. V., T. N. Ivanova, I. A. Rogozina, and A. A. Korobitsyn. "Computer-aided tomography and anthropometry in the diagnosis of visceral obesity in men." Problems of Endocrinology 47, no. 3 (June 15, 2001): 18–22. http://dx.doi.org/10.14341/probl11518.

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A total of 153 men with obesity of different degree and 51 men with normal body weight aged 40-60 years were examined in order to detect a relationship between anthropometric parameters and computer tomography data characterizing the accumulation of abdominal fat at different sites. Body weight, height, waist and hips circumferences, and cutaneo-fatty folds were measured. The area of total abdominal fatty tissue, vis- ceral and subcutaneous fat were evaluated by computer tomography; scanning of the abdominal cavity showed images of two sections (at the levels of the 2-3 and 4-5 lumbar verte brae). The results indicate that abdominal obesity in men is characterized by predominant accumulation of fatty tissue in visceral fat depots. The most informative anthropometric markers of the degree of visceral fat accumulation are sagittal diameter and waist circumference. Sagittal diameter 21.1 cm and waist circumference 95.9 cm and more indicate pronounced visceral obesity (93.2 and 88.9% examinees, respectively). These anthropometric parameters are recommended for the diagnosis of visceral obesity in clinical practice.

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Nishimura, Tadashi, Hiroshi Hosoi, Chihiro Morimoto, Tadao Okayasu, Ryota Shimokura, and Tadashi Kitahara. "Comparison of Cartilage Conduction Hearing Devices Designed by Ear Impression and Computed Tomography." Applied Sciences 13, no. 10 (May 17, 2023): 6152. http://dx.doi.org/10.3390/app13106152.

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The ear molds and shells of hearing devices are generally designed using ear impressions. Computed tomography (CT) can provide information concerning ear structure, which can be utilized in computer-aided design to create hearing devices. This study aimed to compare the differences in hearing devices designed using either ear impressions or CT images. The ear chip of a cartilage conduction (CC) hearing device was designed based on the ear impression and CT images of patients with aural atresia. Three patients with bilateral (n = 1) or unilateral (n = 2) aural atresia participated in this study. The aided thresholds and subjective evaluations were compared. There were no serious problems with the use of either device, and no remarkable differences were observed regarding their respective audiological and subjective benefits. Regarding the subjective patient preferences, the hearing aid with the device designed via CT imaging was better in three ears of two patients, whereas the wearing comfort of the transducer was better for the device designed via the ear impression. The subjective evaluation of appearance varied among the cases. Finally, a device that was designed using CT findings was purchased for one patient. CT images can be utilized in computer-aided design to create CC hearing devices.

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Yang, Hsin-Chia, Chen-Han Chang, Sheng-Wen Huang, Yi-Hong Chou, and Pai-Chi Li. "Correlations among Acoustic, Texture and Morphological Features for Breast Ultrasound CAD." Ultrasonic Imaging 30, no. 4 (October 2008): 228–36. http://dx.doi.org/10.1177/016173460803000404.

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Acoustic, textural and morphological features of the breast in ultrasound imaging were extracted for computer-aided diagnosis. In addition, correlations among different categories of features were analyzed. Clinical data from 14 patients (7 malignant and 7 benign samples) were acquired. A custom-made experimental apparatus was used for simultaneous data acquisition of B-mode ultrasound and limited-angle tomography images. Textural features were extracted from B-mode images, including five parameters derived from the gray-level concurrence matrix and five parameters derived from a nonseparable wavelet transform. Morphological features were also extracted from B-mode images, including the depth-to-width ratio and normalized radial gradient. Acoustic features were estimated using limited-angle tomography, including the sound velocity and attenuation coefficient. Generally, the correlation coefficients for features within the textural feature group were relatively high (0.48–0.79), whereas those between different feature categories were relatively low (0.17–0.40). This suggests that combining different sets of features would improve the computer-aided diagnosis of breast cancer.

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Pereira, Fernando Roberto, Joao Mario Clementin De Andrade, Dante Luiz Escuissato, and Lucas Ferrari De Oliveira. "Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System." IEEE Access 9 (2021): 123134–45. http://dx.doi.org/10.1109/access.2021.3109860.

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Do, Kyung Hyun, Myung Jin Chung, Jin Mo Goo, Kyung Won Lee, and Jung Gi Im. "Evaluation of Computer Aided Volumetry for Simulated Small Pulmonary Nodules on Computed Tomography." Journal of the Korean Radiological Society 50, no. 2 (2004): 101. http://dx.doi.org/10.3348/jkrs.2004.50.2.101.

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