• Title/Summary/Keyword: central panoramic curve

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INFLUENCE OF CENTRAL PANORAMIC CURVE DEVIATION ON THE MANDIBULAR IMAGE RECONSTRUCTION IN THE IMPLANT CT (임플랜트전산화단층촬영시 CENTRAL PANORAMIC CURVE의 변화가 하악골의 영상 재구성에 미치는 영향)

  • Park Rae-Jeong;Lee Sam-Sun;Choi Soon-Chul;Park Tae-Won;You Dong-Soo
    • Journal of Korean Academy of Oral and Maxillofacial Radiology
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    • v.28 no.1
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    • pp.47-58
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    • 1998
  • The purpose of this study was to investigate an influence of the change of central panoramic curves on the image reconstruction in the dental implant CT. The author designed three experimental groups according to the location of central panoramic curve. In group A, central panoramic curve was determined as the curve connecting the center of roots from the first premolar to the first molar. In group B, central panoramic curve was determined as the line connecting the lingual cortical plate at the level of the mesial aspect of the first premolar with the buccal cortical plate at the level of the mesial aspect of the first molar. In Group C, central panoramic curve was determined as the line connecting the buccal cortical plate at the level of the mesial aspect of the first premolar with the lingual cortical plate at the level of the mesial aspect of the first molar. Twenty four reformatted CT images was acquired from four mandibles embedded in the resin block and twenty four contact radiographs of dog specimens were acquired. Each Image was processed under Adobe Photoshop program analysed by MSPA(mandible/maxilla shape pattern analysis) variables such as MXVD, MXHD, UHD, MHD, and LHD. The obtained results were as follows ; 1. The mean of MXVD variable was 19.9, 20.2, and 20.0 in group A, B, and C, respectively, which were smaller than actual value 20.5. But, there was no significant difference among 3 groups (p>0.05). 2. The mean of MXHD, UHD, MHD, and LHD variables in group A, B, and C was 11.9, 12.2, and 12.3; 9.3, 9.5, and 9.6; 10.0, 10.3, and 10.3; 9.2, 9.3, and 9.4 respectively which were equal to or greater than the actual value 11.8, 9.3, 10.0, and 9.2. But, there was no significant difference among 3 groups (p>0.05). 3. The number of noneffective observations with difference over or under 1 mm with comparison to the actual value was 24(20%), 58(48.3%), and 52(43.3%), respectively, in group A, B, and C. 4. In group A, the number of observations over 1 mm and under 1 mm was 9 and 15, respectively, but in group Band C, the number of observations over 1 mm was more than under 1 mm.

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Effectiveness of digital subtraction radiography in detecting artificially created osteophytes and erosions in the temporomandibular joint

  • Kocasarac, Husniye Demirturk;Celenk, Peruze
    • Imaging Science in Dentistry
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    • v.47 no.2
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    • pp.99-107
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    • 2017
  • Purpose: Erosions and osteophytes are radiographic characteristics that are found in different stages of temporomandibular joint (TMJ) osteoarthritis. This study assessed the effectiveness of digital subtraction radiography (DSR) in diagnosing simulated osteophytes and erosions in the TMJ. Materials and Methods: Five intact, dry human skulls were used to assess the effectiveness of DSR in detecting osteophytes. Four cortical bone chips of varying thicknesses (0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm) were placed at the medial, central, and lateral aspects of the condyle anterior surface. Two defects of varying depth (1.0 mm and 1.5 mm) were created on the lateral, central, and medial poles of the condyles of 2 skulls to simulate erosions. Panoramic images of the condyles were acquired before and after artificially creating the changes. Digital subtraction was performed with Emago dental image archiving software. Five observers familiar with the interpretation of TMJ radiographs evaluated the images. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic accuracy of the imaging methods. Results: The area under the ROC curve (Az) value for the overall diagnostic accuracy of DSR in detecting osteophytic changes was 0.931. The Az value for the overall diagnostic accuracy of panoramic imaging was 0.695. The accuracy of DSR in detecting erosive changes was 0.854 and 0.696 for panoramic imaging. DSR was remarkably more accurate than panoramic imaging in detecting simulated osteophytic and erosive changes. Conclusion: The accuracy of panoramic imaging in detecting degenerative changes was significantly lower than the accuracy of DSR (P<.05). DSR improved the accuracy of detection using panoramic images.