Maxillofacial Virtual Surgery from 3-D CT Images

Virtual reality based surgical training have a great potential as an alternative to traditional training methods. In neurosurgery, state-of-the-art training devices are limited and the surgical experience accumulates only after so many surgical procedures. Incorrect surgical movements can be destructive; leaving patients paralyzed, comatose or dead. Traditional techniques for training in surgery use animals, phantoms, cadavers and real patients. Most of the training is based either on these or on observation behind windows. The aim of this research is the development of a novel virtual reality training system for neurosurgical interventions based on a real surgical microscope for a better visual and tactile realism. The simulation works by an accurate tissue modeling, a force feedback device and a representation of the virtual scene on the screen or directly on the oculars of the operating microscope. An intra-operative presentation of the preoperative three-dimensional data will be prepared in our laboratory and by using this existing platform virtual organs will be reconstructed from real patients’ images. VISPLAT is a platform for virtual surgery simulation. It is designed as a patient-specific system that provides a database where patient information and CT images are stored. It acts as a framework for modeling 3D objects from CT images, visualization of the surgical operations, haptic interaction and mechanistic material-removal models for surgical operations. It tries to solve the challenging problems in surgical simulation, such as real-time interaction with complex 3D datasets, photorealistic visualization, and haptic (force-feedback) modeling. Surgical training on this system for educational and preoperative planning purposes will increase the surgical success and provide a better quality of life for the patients. Surgical residents trained to perform surgery using virtual reality simulators will be more proficient and have fewer errors in the first operations than those who received no virtual reality simulated education. VISPLAT will help to accelerate the learning curve. In future VISPLAT will offer more sophisticated task training programs for minimally invasive surgery; this system will record errors and supply a way of measuring operative efficiency and performance, working both as an educational tool and a surgical planning platform quality.

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Three-Dimensional Virtual Surgery Based on CT Images

2009 3rd International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2009.5163688 ◽

2009 ◽

Cited By ~ 4

Author(s):

Limei Song ◽

Jing Luo ◽

Yuhua Wen

Keyword(s):

Three Dimensional ◽

Ct Images ◽

Virtual Surgery

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Dilatation of Hemashield prostheses – a new method for measuring the true prosthetic diameter based on ECG-gated multislice CT images

The Thoracic and Cardiovascular Surgeon ◽

10.1055/s-0032-1332727 ◽

2013 ◽

Vol 61 (S 01) ◽

Author(s):

M Hamiko ◽

M Endlich ◽

C Krämer ◽

C Probst ◽

A Welz ◽

...

Keyword(s):

Multislice Ct ◽

Ct Images ◽

New Method

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Fully automatic quotient image generation using V/Q SPECT/CT images

10.1055/s-0039-1683718 ◽

2019 ◽

Author(s):

K Herdinai ◽

S Urbán ◽

Z Besenyi ◽

L Pávics ◽

N Zsótér ◽

...

Keyword(s):

Ct Images ◽

Image Generation ◽

Fully Automatic

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Fully automatic lung lobe segmentation using V/Q SPECT/CT images

10.1055/s-0040-1708371 ◽

2020 ◽

Author(s):

A Király ◽

S Urbán ◽

Z Besenyi ◽

L Pávics ◽

N Zsótér ◽

...

Keyword(s):

Ct Images ◽

Lung Lobe ◽

Fully Automatic ◽

Lobe Segmentation

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Pseudocolor Transformation for the Visualization of Stapes in CT Images

Skull Base ◽

10.1055/s-2006-958540 ◽

2007 ◽

Vol 16 (S 2) ◽

Author(s):

Moon Suh Park ◽

Jae Yong Byun ◽

Seung Gun Yeo ◽

Chang Il Cha

Keyword(s):

Ct Images

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Clinical Manifestations and Radiological Characteristics on Rosai-Dorfman Disease: A Retrospective Observational Study

Medical & Clinical Research ◽

10.33140/mcr.05.05.005 ◽

2020 ◽

Vol 5 (5) ◽

Keyword(s):

Blood Vessels ◽

Abdominal Cavity ◽

Clinical Manifestations ◽

Ct Images ◽

High Signal ◽

Radiological Features ◽

Imaging Characteristics ◽

Rosai Dorfman Disease ◽

Heterogeneous Enhancement ◽

The Masses

Background and Objective: Rosai-Dorfman disease (RDD) are usually misdiagnosed because of rarity and nonspecific clinical and radiological features. The aim of our study is to explore the clinical and imaging characteristics of RDD to improve diagnostic accuracy. Methods: Clinical and imaging data in 10 patients with RDD were retrospectively analyzed. 7 patients were underwent CT scanning and 3 patients were underwent MR examination. Results: 8 (8/10) patients presented with painless enlarged lymph nodes (LNs) or mass. 3 cases were involved with LNs, 5 cases were involved with extra-nodal tissues, and the remaining 2 cases were involved with LNs and extra-nodal tissue simultaneously. In enhanced CT images, enlarged LNs displayed mild or moderate enhancement, and 2 cases showed heterogeneous ring-enhancement. MR features of 3 patients with extra-nodal RDD, 2 cases showed a mass located in the subcutaneous and anterior abdominal wall respectively, and 1 case showed an intracranial mass. Besides, all lesions showed high signal foci on DWI images, and were characterized by marked heterogeneous enhancement with blurred edge. The dural/fascia tail sign and dilated blood vessels could be seen around all the lesions on enhanced MRI. Radiological features of 2 cases with LN and extranodal tissue involved, one case presented with the swelling and thickening of pharyngeal lymphoid ring and nasopharynx, meanwhile with enlarged LNs in bilateral submandibular area, neck and abdominal cavity, and also companied with osteolytic lesion in right proximal humerus. All these LNs displayed mild and moderate enhancement on CT images. Another case showed enlarged LNs in bilateral neck accompanied with soft tissue mass in the sinuses. Conclusions: RDD occurred commonly in young and middle-aged men and presented with painless enlarged LNs or mass.RDD had a huge diversity of imaging findings, which varied with different location. The radiological features, such as small patches of high signal foci in the masses on DWI images, heterogeneous enhancement and blood vessels around the masses, are helpful in diagnosis of extranodal RDD.

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