CT cinematic rendering for pelvic primary tumor photorealistic visualization

Yang, Jun; Li, Kun; Deng, Huiyuan; Feng, Jun; Fei, Yong; Yiren, Jin; Liao, Chengde; Li, Qinqing

doi:10.21037/qims.2018.09.21

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…15,16 Many pictorial reviews and numerous case reports have provided examples of CR images showing a more photorealistic representation of tumours, bones, and blood vessels, and thus demonstrating the potential of CR reconstructions in aiding doctor-patient communication, treatment planning, and medical education. 10,[17][18][19][20][21] However, a study that has objectively evaluated the clinical value of the CR technique applied to maxillofacial fractures has not yet been published. 22 In the present study, the value of CR reconstructions of maxillofacial fractures in clinical application was evaluated, and comparisons were made with VR reconstructions.…”

Section: Discussionmentioning

confidence: 99%

Clinical application of cinematic rendering in maxillofacial fractures

Hu¹,

Zhang²,

Liu³

et al. 2022

International Journal of Oral and Maxillofacial Surgery

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Section: Discussionmentioning

confidence: 99%

Clinical application of cinematic rendering in maxillofacial fractures

Hu¹,

Zhang²,

Liu³

et al. 2022

International Journal of Oral and Maxillofacial Surgery

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“…2020;18:1-7 in a study with pelvic tumors. (9) In a different study involving 18 surgeons and comparing the value of cinematic rendering and conventional CT for the understanding of tomographic surgical anatomy in 40 patients, cinematic rendering allowed faster and more accurate understanding of surgical anatomy compared to conventional CT, regardless of surgeon expertise. (10)…”

Section: Surgical Planningmentioning

confidence: 99%

Cinematic rendering for three-dimensional reconstructions of the chest wall: a new reality

Costa

Gellada

2020

Einstein (São Paulo)

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Computed tomography with multiple detectors and the advancement of processors improved rendered images and three-dimensional reconstructions in clinical practice. Traditional axial slices form non-intuitive images because they are seen in only one plane. The three-dimensional reconstructions can show structures details and diseases with complex anatomy in different perspectives. Cinematic rendering is a newly three-dimensional reconstruction technique, already approved for clinical use, which can produce realistic images from traditional computed tomography data. The algorithm used is based on light trajectory methods and the global lighting model, which simulate thousands of images from all possible directions. Thus, the technique shapes the physical propagation of light and generates a realistic three-dimensional image with depth, shadows and more anatomic details. It is a multidimensional rendering acquired through complex lighting effects. The aim of this article was to show the advance of three-dimensional technology with the cinematic rendering in images exams of the thoracic wall.

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“…Multi-detector CT can be a useful adjunct to radiographs as it allows precise delineation of complex anatomical locations including articulations [ 3 ] and irregular bones, such as the sacrum or vertebral bodies [ 14 ]. These regions are often difficult to visualize in 2D (2-Dimensional) planes and CT can provide additional 3D (3-Dimensional) reconstructions [ 15 ], which are useful for surgical planning.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Ong

Zhu

Tan

et al. 2023

Cancers

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An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

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CT cinematic rendering for pelvic primary tumor photorealistic visualization

Cited by 11 publications

References 26 publications

Clinical application of cinematic rendering in maxillofacial fractures

Clinical application of cinematic rendering in maxillofacial fractures

Cinematic rendering for three-dimensional reconstructions of the chest wall: a new reality

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

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