Assessment of bone segmentation quality of cone-beam CT versus multislice spiral CT: a pilot study

Loubele, Miet; Maes, Frederik; Schutyser, Filip; Marchal, Guy; Jacobs, Reinhilde; Suetens, Paul

doi:10.1016/j.tripleo.2005.10.039

Cited by 153 publications

(143 citation statements)

References 15 publications

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“…However, it can be clear that the voxel size itself provides only a crude prediction of the spatial accuracy. ( 2 , 16 ) The voxel size, determined by the manufacturer, should ideally provide a balance between spatial resolution and noise. It is seen that for some devices, predefined exposure protocols using different mAs values are reconstructed at different voxel sizes.…”

Section: Discussionmentioning

confidence: 99%

“…As another example, a high contrast between aluminum and PMMA can partly predict a good bone segmentation quality in vivo , but the latter also depends on factors such as geometric accuracy and spatial resolution of the system, artifacts originating from the cortical bone, and other high‐attenuation objects such as implants and metallic restorations. ( 16 ) The further development of the QC phantom and the formation of a QA protocol will therefore be conducted in parallel with research on diagnostic image quality, which could allow determination of specific ranges or thresholds for certain image quality parameters. Our results suggest that different ranges may have to be applied for large and small FOV devices, since they perform differently in terms of noise and uniformity.…”

Section: Discussionmentioning

confidence: 99%

“…To develop such a tool, all available knowledge regarding image quality assessment on other 3D or pseudo‐3D imaging modalities (e.g., spiral CT, tomosynthesis, kV‐CBCT used in radiotherapy) ( 4 – 7 ) needs to be combined with the existing knowledge of CBCT and previous studies on CBCT image quality. ( 2 , 8 – 16 ) Even though a large number of CBCT image quality studies have been published over the last few years, most have focused on diagnostic image quality. However, a number of studies have already assessed technical image quality for one or more CBCT devices, using an existing commercial quality control (QC) phantom, ( 8 , 9 ) a phantom provided by a CBCT manufacturer, ( 2 , 10 ) a water phantom, ( 8 , 11 ) a customized test object, ( 12 – 15 ) or clinical data.…”

Section: Introductionmentioning

confidence: 99%

“…However, a number of studies have already assessed technical image quality for one or more CBCT devices, using an existing commercial quality control (QC) phantom, ( 8 , 9 ) a phantom provided by a CBCT manufacturer, ( 2 , 10 ) a water phantom, ( 8 , 11 ) a customized test object, ( 12 – 15 ) or clinical data. ( 16 , 17 ) Although these studies have provided useful insights regarding certain image quality aspects, they also show the need for a standardized QC phantom that is suited for use on all CBCT devices, and which provides results that are relevant to dental imaging and that can be compared between systems. Commercial QC phantoms have been described for conventional CT, but these are not applicable for dental CBCT due to the difference in performance for certain image quality aspects.…”

Section: Introductionmentioning

confidence: 99%

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Development and applicability of a quality control phantom for dental cone‐beam CT

Pauwels

Stamatakis

Manousaridis

et al. 2011

J Applied Clin Med Phys

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125

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Cone‐beam CT (CBCT) has shown to be a useful imaging modality for various dentomaxillofacial applications. However, optimization and quality control of dental CBCT devices is hampered due to the lack of an appropriate tool for image quality assessment. To investigate the application of different image quality parameters for CBCT, a prototype polymethyl methacrylate (PMMA) cylindrical phantom with inserts for image quality analysis was developed. Applicability and reproducibility of the phantom were assessed using seven CBCT devices with different scanning protocols. Image quality parameters evaluated were: CT number correlation, contrast resolution, image homogeneity and uniformity, point spread function, and metal artifacts. Deviations of repeated measurements were between 0.0% and 3.3%. Correlation coefficients of CBCT voxel values with CT numbers ranged between 0.68 and 1.00. Contrast‐to‐noise ratio (CNR) values were much lower for hydroxyapatite (0

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development and applicability of a quality control phantom for dental cone‐beam CT

Pauwels

Stamatakis

Manousaridis

et al. 2011

J Applied Clin Med Phys

Self Cite

125

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show abstract

“…Studies on cadavers with soft tissue [15][16][17][18] are more elaborate, and are practically limited to a feasible number of measurements, especially if these are to be processed manually. A common example is the acquisition of distances between marked anatomical reference points, which are few by nature.…”

Section: Introductionmentioning

confidence: 99%

Accuracy assessment of CT-based outer surface femur meshes

Gelaude

Sloten

Lauwers

2008

Computer Aided Surgery

100

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Objectives: Computer-aided bone surgery planning and implant design applications require accurate and compact representations of the patient's bone. The accuracy of bone segmentation from medical images has been studied extensively, with each study using a specific ground truth and a specific type and number of accuracy measurements. However, for convenience and practical reasons these three specifications have always been limited. The goal of this study is to thoroughly assess the absolute 3D accuracy of CT-based bone outer surface meshes, using femora as the examples. Materials and Methods: Using dense and very accurate optical surface scans of 15 dried femora as an absolute ground truth, this paper reports on the absolute 3D geometric accuracy of triangulated bone outer surface meshes, which were segmented from the CT scans of the corresponding formalin-fixed intact cadaver specimens using the author's previously presented contour-based segmentation algorithm on the one hand, and the commercially available Mimics Õ software (Materialise N.V., Leuven, Belgium) on the other. The study incorporates the effect of soft tissue presence on hard tissue segmentation and simultaneously reveals the accuracy shift introduced as a result of boiling the cadaver bones by processing extra CT scans of the dried bones. Results: The presented study demonstrates that, when using the optimal parameter settings for the respective segmentation procedures, sub-voxel mesh accuracies can be attained. Compact surface representations of femora can be generated with mean absolute accuracies of up to one fifth of the voxel size and Root Mean Square (RMS) error of half the voxel size. Conclusions: The 3D accuracy of the contour-based segmentation previously presented by the author makes it most suitable for generating outer bone surface meshes for use in the aforementioned applications. The optimal parameter settings for this segmentation procedure have been identified. For the Mimics Õ bone surface meshes, a single, but excellent, pre-defined set of parameters was identified.

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Functional and Technical Characteristics of Different Cone Beam Computed Tomography Units

Kharbanda

Wadhawan

Balachandran

et al. 2014

Cone Beam Computed Tomography in Orthodontics: Indications, Insights, and Innovations

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Assessment of bone segmentation quality of cone-beam CT versus multislice spiral CT: a pilot study

Cited by 153 publications

References 15 publications

Development and applicability of a quality control phantom for dental cone‐beam CT

Development and applicability of a quality control phantom for dental cone‐beam CT

Accuracy assessment of CT-based outer surface femur meshes

Functional and Technical Characteristics of Different Cone Beam Computed Tomography Units

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