Influence of an object's z-axis location and location on the axial plane on the voxel value representation and uniformity in cone beam computed tomography

Nishino, Kanako; Shiba, Hiroaki; Kakimoto, Naoya; Tsujimoto, Tokuzou; Chindasombatjaroen, Jira; Murakami, Shumei; Furukawa, Souhei

doi:10.1016/j.oooo.2014.08.010

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Hohlweg-Majert et al 43 demonstrated average shifts of 24 GV (maximum, 67 GV) between axial slices in CBCT. Nishino et al 52 varied the position of a cylindrical phantom along the z-axis, showing shifts up to 19-69 GV, depending on the material. Figure 4 shows stable GVs along the z-axis for different FOV heights for one CBCT model for a water phantom, with maximum differences of 5-9 GV and standard deviation values of 0.6-1.4 GV.…”

Section: Variability Between Slicesmentioning

confidence: 99%

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

Pauwels

Jacobs²,

Singer³

et al. 2015

Dentomaxillofacial Radiology

336

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: Variability Between Slicesmentioning

confidence: 99%

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

Pauwels

Jacobs²,

Singer³

et al. 2015

Dentomaxillofacial Radiology

336

228

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“…The GV of voxels in CBCT images can vary due to imaging environmental factors and the inherent limitations of CBCT, such as the geometry of the beam and low dose of radiation [38][39][40][41][42][43] . In particular, the geometry of the cone beam can cause GV variability between central and peripheral positions and between axial slices within the field of view (FOV), along with asymmetrical endo/exo-mass effects 5,[44][45][46] . As a result, CBCT images are inadequate for quantitatively measuring bone density because it is difficult to calculate accurate Hounsfield units (HU) with GVs obtained by CBCT and to directly compare the bone density of different sites.Various researchers have tried to solve the problem of inaccurate HU measurement in CBCT images [47][48][49][50][51][52][53][54][55] .…”

mentioning

confidence: 99%

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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“…61 All remaining papers found it has significant effect. 36,47,54,55,58 Studies done by Nackaerts et al 2011 36 , Eskandarloo et al 2012 55 , Oliveira et al 2014 54 , Nishino et al 2014 47 , Pauwels et al 2016 58 changed the object position in the FOV of the same machine. All of them, for at least part of their experiments, had all variables fixed except for the tested one.…”

mentioning

confidence: 99%

Validity of Cone Beam Computed Tomography Voxel Density Values: A Review.

Tabarany¹,

Aboulfotouh²,

Ashmawy³

2018

IJAR

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Cone-beam computed tomography, grey value, Hounsfield unite, density value, voxel density value. Introduction: voxel density value in CBCT is used regularly in many clinical aspects such as virtual implant planning. Many researchers studied factors affecting it and their significance. Most of the results obtained are contradicting and /or inconclusive. Objectives: to compile papers inquiring effect factors affecting voxel grey values and their significance. Methodology: internet search was done on two databases using MeSH terms. Resulting studies went through two level screening according to predetermined inclusion and exclusion criteria. Results: A total of 27 studies were included. Total number of factors, which were tested in the included papers, were eighteen. Conclusion:Significantly affecting factors are (FOV, mA, Objects position inside the FOV, Objects exo-mass, kVp, time between exposure, number of basis, adjacent air to the ROI).Insignificantly affecting factors are (exposure parameters, software, exposure dose, presence of teeth, presence of metallic post in or out the FOV). Inconclusive factors are (Machines model, voxel size, objects mass, receptor type, exposure time). Grey levels in CBCT which is known by Hounsfield Unit of CBCT is not reliable nor repeatable.

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Influence of an object's z-axis location and location on the axial plane on the voxel value representation and uniformity in cone beam computed tomography

Cited by 3 publications

References 18 publications

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

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

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

Validity of Cone Beam Computed Tomography Voxel Density Values: A Review.

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