High-quality image acquisition by double exposure overlap in cone beam computed tomography

Plachtovics, Mark; Bujtár, Péter; Nagy, Katalin; Mommaerts, Maurice Y.

doi:10.1016/j.oooo.2014.02.024

Cited by 6 publications

(10 citation statements)

References 23 publications

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“…Furthermore, phantom position (central or peripheral in the FOV) affected GVs. Plachtovics et al 61 found that a rotation of a centrally positioned, slightly asymmetrical phantom leads to GV shifts of 15 GV (maximum, 51 GV). In addition, they found mean GV differences between central and peripheral regions of 209 GV with a maximum of 240 GV.…”

Section: Uniformity a Uniform Issuementioning

confidence: 99%

“…Plachtovics et al 61 proposed a double-exposure overlap acquisition, consisting two consecutive (or simultaneous) full-rotation exposures with a relatively low number of projections. Compared with single-rotation protocols, the double-exposure protocol showed a notable improvement in GV uniformity throughout the axial plane (except at the very edge of the FOV) as well as a more accurate Hounsfield unit calibration.…”

mentioning

confidence: 99%

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CBCT-based bone quality assessment: are Hounsfield units applicable?

Pauwels

Jacobs²,

Singer³

et al. 2015

Dentomaxillofacial Radiology

320

228

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CBCT is a widely applied imaging modality in dentistry. It enables the visualization of highcontrast structures of the oral region (bone, teeth, air cavities) at a high resolution. CBCT is now commonly used for the assessment of bone quality, primarily for pre-operative implant planning. Traditionally, bone quality parameters and classifications were primarily based on bone density, which could be estimated through the use of Hounsfield units derived from multidetector CT (MDCT) data sets. However, there are crucial differences between MDCT and CBCT, which complicates the use of quantitative gray values (GVs) for the latter. From experimental as well as clinical research, it can be seen that great variability of GVs can exist on CBCT images owing to various reasons that are inherently associated with this technique (i.e. the limited field size, relatively high amount of scattered radiation and limitations of currently applied reconstruction algorithms). Although attempts have been made to correct for GV variability, it can be postulated that the quantitative use of GVs in CBCT should be generally avoided at this time. In addition, recent research and clinical findings have shifted the paradigm of bone quality from a density-based analysis to a structural evaluation of the bone. The ever-improving image quality of CBCT allows it to display trabecular bone patterns, indicating that it may be possible to apply structural analysis methods that are commonly used in micro-CT and histology.

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Section: Uniformity a Uniform Issuementioning

confidence: 99%

mentioning

confidence: 99%

CBCT-based bone quality assessment: are Hounsfield units applicable?

Pauwels

Jacobs²,

Singer³

et al. 2015

Dentomaxillofacial Radiology

320

228

View full text Add to dashboard Cite

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“…According to the literature, 5,17,21 CBCT technology is designed for relatively higher-density materials; therefore, lower-density materials such as fine trabecular bone, D4 16 (corresponding to acryl), or soft tissues that are mimicked by LDPE cannot be studied quantitatively by this method while using this equipment. 4,13 It is interesting to note that the four fitted quadratic curves of the four sets of measurements are below the ideal 45-degree line ( Figure 5), which corresponds to the perfect correlation (Table V). However, if we were extrapolating the data to materials of higher density than Teflon, such as metallic dental implants, the quadratic curves would cross over the ideal 45-degree line.…”

Section: Discussionmentioning

confidence: 87%

“…These average values of the four inserts were correlated with the multidetector computed tomography (MDCT) measured values (General Electric Medical Systems LightSpeed VFX Ultra, Tokyo, Japan, 120 kilovolt peak, 144.91 mAs) as described previously. 4 The MDCT value is the expected CT number measured for the same ISI quality assurance phantom (Table III). 4 Second, for checking the spatial accuracy of the CBCT images in the four applied scan conditions, the Reconstructed images of the 150-mm diameter standard ISI quality assurance phantom, which is made of air (1.29 kg/m 3 ¼ 0.00129 g/cm 3 ), LDPE (0.92 g/cm 3 ), acryl (1.18 g/cm 3 ) and Teflon (2.16 g/cm 3 ) embedded in a Plexiglas cylinder.…”

Section: Methodsmentioning

confidence: 99%

“…4 The MDCT value is the expected CT number measured for the same ISI quality assurance phantom (Table III). 4 Second, for checking the spatial accuracy of the CBCT images in the four applied scan conditions, the Reconstructed images of the 150-mm diameter standard ISI quality assurance phantom, which is made of air (1.29 kg/m 3 ¼ 0.00129 g/cm 3 ), LDPE (0.92 g/cm 3 ), acryl (1.18 g/cm 3 ) and Teflon (2.16 g/cm 3 ) embedded in a Plexiglas cylinder. In the height of these two axial slices, the bar pattern phantom is not visible.…”

Section: Methodsmentioning

confidence: 99%

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The effect of calibration and detector temperature on the reconstructed cone beam computed tomography image quality: a study for the workflow of the iCAT Classic equipment

Plachtovics

Goczan

Nagy

2015

Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology

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Validation of bone mineral density measurement using quantitative CBCT image based on deep learning

Park

Kang

Kim

et al. 2023

Sci Rep

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The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R2 (coefficient of determination), and SEE (standard error of estimation). Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm3, MAE of 67.94 mg/cm3, and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm3, 460.52 mg/cm3, and 54.29%, respectively. The linear regression between the QCBCT and QCT images showed a slope of 1.00 and a R2 of 0.85, whereas for the CAL_CBCT images, those values were 0.32 and 0.24, respectively. The overall SEE between the QCBCT images and QCT images was 81.06 mg/cm3, whereas the SEE for the CAL_CBCT images was 109.32 mg/cm3. The QCBCT images thus showed better accuracy, linearity, and uniformity than the CAL_CBCT images across the entire FOV. The BMD measurements from the quantitative CBCT images showed high accuracy, linearity, and uniformity regardless of the relative geometric positions of the bone in the potential implant site. When applied to actual patient CBCT images, the CBCT-based quantitative BMD measurement based on deep learning demonstrated high accuracy and reliability across the entire FOV.

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High-quality image acquisition by double exposure overlap in cone beam computed tomography

Cited by 6 publications

References 23 publications

CBCT-based bone quality assessment: are Hounsfield units applicable?

CBCT-based bone quality assessment: are Hounsfield units applicable?

The effect of calibration and detector temperature on the reconstructed cone beam computed tomography image quality: a study for the workflow of the iCAT Classic equipment

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning

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