Purpose: This study aimed to evaluate the effect of field-of-view (FOV) size on the gray values derived from cone-beam computed tomography (CBCT) compared with the Hounsfield unit values from multidetector computed tomography (MDCT) scans as the gold standard. Materials and Methods: A radiographic phantom was designed with 4 acrylic cylinders. One cylinder was filled with distilled water, and the other 3 were filled with 3 types of bone substitute: namely, Nanobone, Cenobone, and Cerabone. The phantom was scanned with 2 CBCT systems using 2 different FOV sizes, and 1 MDCT system was used as the gold standard. The mean gray values(MGVs) of each cylinder were calculated in each imaging protocol. Results: In both CBCT systems, significant differences were noted in the MGVs of all materials between the 2 FOV sizes(P<.05) except for Cerabone in the Cranex3D system. Significant differences were found in the MGVs of each material compared with the others in both FOV sizes for each CBCT system. No significant difference was seen between the Cranex3D CBCT system and the MDCT system in the MGVs of bone substitutes on images obtained with a small FOV. Conclusion: The size of the FOV significantly changed the MGVs of all bone substitutes, except for Cerabone in the Cranex3D system. Both CBCT systems had the ability to distinguish the 3 types of bone substitutes based on a comparison of their MGVs. The Cranex3D CBCT system used with a small FOV had a significant correlation with MDCT results.
Wonju Hong;Hong Il Ha;Jung Woo Lee;Sang Min Lee;Min-Jeong Kim
Korean Journal of Radiology
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v.20
no.4
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pp.599-608
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2019
Objective: To evaluate the effectiveness of computed tomography (CT) Hounsfield unit histogram analysis (HUHA) in postoperative pancreatic fistula (PF) prediction. Materials and Methods: Fifty-four patients (33 males and 21 females; mean age, 65.6 years; age range, 37-89 years) who had undergone preoperative CT and pancreaticoduodenectomy were included in this retrospective study. Two radiologists measured mean CT Hounsfield unit (CTHU) values by drawing regions of interest (ROIs) at the level of the pancreaticojejunostomy site on preoperative pre-contrast images. The HUHA values were arbitrarily divided into three categories, comprising HUHA-A ≤ 0 HU, 0 HU < HUHA-B < 30 HU, and HUHA-C ≥ 30 HU. Each HUHA value within the ROI was calculated as a percentage of the entire area using commercial 3-dimensional analysis software. Pancreas texture was evaluated as soft or hard by manual palpation. Results: Fifteen patients (27.8%) had clinically relevant PFs. The PF group had significantly higher HUHA-A (p < 0.01) and significantly lower mean CTHU (p < 0.01) values than those of the non-PF group. The HUHA-A value had a moderately strong correlation with PF occurrence (r = 0.60, p < 0.01), whereas the mean CTHU had a weak negative correlation with PF occurrence (r = -0.27, p < 0.01). The HUHA-A and mean CTHU areas under the curve (AUCs) for predicting PF occurrence were 0.86 and 0.65, respectively, with significant difference (p < 0.01). The HUHA-A and mean CTHU AUCs for predicting pancreatic softness were 0.86 and 0.64, respectively, with significant difference (p < 0.01). Conclusion: The HUHA-A values on preoperative pre-contrast CT images demonstrate a strong correlation with PF occurrence.
Purpose: The aim of present study is to evaluate a possibility of clinical application for the effect of low intensity ultrasound stimulation (LIUS) in mechanical characteristics of bone on osteoporotic fractures prevention. Materials and Methods: Eight virgin ICR mice (14 weeks old, approximate weight 25g) were ovariectomized (OVX) to induce osteoporosis. The right hind limbs were then stimulated with LIDS (US Group), whereas left hind limbs were not stimulated (CON Group). Both hind limbs of all mice were scanned by in-vivo micro-CT to acquire two-dimensional (2D) images at 0 week before stimulation and 3 weeks and 6 weeks after stimulation. Three-dimensional (3D) finite element (FE) models generated by scanned 2D images were used to determine quantitatively the effect of LIUS on strength related to bone structure. Additionally, distributions of Hounsfield units and elastic moduli, which are related to the bone quality, for the bones in the US and CON groups were determined to analyze quantitatively a degree of improvement of bone qualities achieved by LIUS. Results: The result of FE analysis showed that the structural strength in US Group was significantly increased over time (p<0.05), while that in CON Group was statistically constant over time (p>0.05). High values of Hounsfield units obtained from voxels on micro-CT images and high values of elastic moduli converted from the Hounsfield units were dominantly appeared in US Group compared with those in CON Group. Conclusion: These finding indicated that LIUS would improve the mechanical characteristics of osteoporotic bone via the effects of bone structure (bone strength) and quality (Hounsfield unit and elastic modulus). Therefore, the LIUS may decrease effectively the risk of osteoporotic fracture in clinics.
The objective of this study was to evaluate the differences and reproducibility of Hounsfield unit (HU) value and volume measurements on different computed tomography (CT) scanner types and different collimations by using a gelatin phantom. The phantom consisting of five synthetic simulated calculus spanning diameters from 3.0 mm to 12.0 mm with 100 HU was scanned using a two-channel multi-detector row CT (MDCT) scanner, a four-channel MDCT scanner, and two 64-channel MDCT scanners. For all different scanner types, the thinnest possible collimation and the second thinnest collimation was used. The HU values and volumes of the synthetic simulated calculus were independently measured three times with minimum intervals of 2 weeks and by three experienced veterinary radiologists. ANOVA and Scheff$\acute{e}$ test for the multiple comparison were performed for statistical comparison of the HU values and volumes of the synthetic simulated calculus according to different CT scanner types and different collimations. The reproducibility of the HU value and volume measurements was determined by calculating Cohen's k. The reproducibility of HU value and volume measurements was very good. HU value varied between different CT scanner types, among different beam collimations. However, there was not statistically significant difference. The percent error (PE) decreased as the collimation thickness decreased, but the decrease was statistically insignificant. In addition, no statistically significant difference in the PEs of the different CT scanner types was found. It can be concluded that the CT scanner type insignificantly affects HU value and the volumetric measurement, but that a thinner collimation tends to be more useful for accurate volumetric measurement.
Purpose: The aim of this study was to examine the correlation of the subjective and the objective evaluation of edentulous ridge bone quality, and to evaluate the change of the dental implant stability in each bone density group for early healing period after implant installation. Methods: Sixty-seven implants(Osstem implant$^{(R)}$, Seoul, Korea) were included in this study. We evaluated the bone density by 2 methods. The one was the subjective method which was determined by practitioner s tactile sense, the other was the objective bone type was based on Hounsfield units. The implant stability in each bone type group was assessed by resonance frequency analyzer(Osstell mentor$^{(R)}$). Data were analyzed for the change of the implant stability, and they were compared to verify the difference of groups at the time of installation, 2, 6, 10, 14 weeks postoperatively. Spearman's correlation was used to demonstrate the correlation between the subjective and the objective evaluation of the bone density, and analysis of variance(ANOVA) was used to analyze the differences of implant stability at each time point. Results: There was no close relation between the subjective and the objective evaluation of the bone density(r=0.57). In the subjective groups, there was statistically significant difference between the type 1 and 3 at 10 weeks and between the type 2 and 3 at 14 weeks. In the objective groups, there was no statistically significant difference between the D 1, 2, 3, 4, and 5 group with regard to RFA from baseline to 14 weeks(P>0.1). Conclusions: The implant stability increased over time during the study, and it was improved with bone density proportionally after 2weeks postoperatively. It is recommended that the decision of bone density is base on Hounsfield unit for implant loading time.
In this study, tissue equivalency (TE) of a newly developed epoxy-based phantom to 3-5 years child's tissue was investigated in paediatric energy range. Epoxy-based TE-phantoms were produced at different glandular/adipose (G/A) ratios of 17/83%, 31/69%, 36/64% and 10/90%. A procedure was developed in which specific amounts of boron, calcium, magnesium, sulphur compounds are mixed with epoxy resin, together with other minor substitutes. In paediatric energy range of 40-60 kVp half-value layer (HVL) values were measured and then Hounsfield Units (HU) were determined from Computed Tomography(CT) scans taken in the X-ray energy range of 80-120kVp. It is found that radiation absorption properties of these phantoms in terms of the measured HVL values related to linear attenuation coefficients (µ) are very well mimicking a 3 years child's soft tissue in case a ratio of 10/90%G/A. Additionally, the HU values of phantoms were determined from the CT scans. The HU = 47.8 ± 4.8 value was found for the epoxy-based phantom produced at a ratio of 10/90%G/A. The obtained HVL and HU values also support the suitability of the new epoxy based-phantom produced at a ratio of 10/90%G/A for a satisfactory mimicking a 3 years child's soft tissue by 5%. Thus they can have a potential use to perform the quality controls of medical X-ray systems and dose optimization studies.
Background: Persistent uroliths after a cystotomy in dogs are a common cause of surgical failure. Objectives: This study examined the following: the success rate of retrograde urohydropropulsion in male dogs using non-enhanced computed tomography (CT), whether the CT mean beam attenuation values in Hounsfield Units (mHU) measured in vivo could predict the urolithiasis composition and whether the selected reconstruction kernel may influence the measured mHU. Methods: All dogs and cats that presented with lower urinary tract uroliths and had a non-enhanced CT preceding surgery were included. In male dogs, CT was performed after retrograde urohydropropulsion to detect the remaining urethral calculi. The percentage and location of persistent calculi were recorded. The images were reconstructed using three kernels, from smooth to ultrasharp, and the calculi mHU were measured. Results: Sixty-five patients were included in the study. The success rate of retrograde urohydropropulsion in the 45 male dogs was 55.6% and 86.7% at the first and second attempts, respectively. The predominant components of the calculi were cystine (20), struvite (15), calcium oxalate (8), and urate (7). The convolution kernel influenced the mHU values (p < 0.05). The difference in mHU regarding the calculus composition was better assessed using the smoother kernel. A mHU greater than 1,000 HU was predictive of calcium oxalate calculi. Conclusions: Non-enhanced CT is useful for controlling the success of retrograde urohydropropulsion. The mHU could allow a prediction of the calculus composition, particularly for calcium oxalate, which may help determine the therapeutic strategy.
Bone density in the recipient implant site seems to be an important factor for long term success of endosseous implants. Preoperative evaluation of bone density is very helpful to assist the clinician with the treatment planning of implant therapy. Accurate information on bone density will help the surgeon identify suitable implant sites, thereby improving the success rate of the procedure. Purpose; The aim of this study was to evaluate a correlation between bone density measured preoperatively with computerized tomography and histologically measured bone density by bone biopsy. Patients and methods; Twenty seven patients were selected. All the patients were in good health, with no systemic disorder and additional bone graft. Preoperatively the patients underwent CT scanning to evaluate Houmsfield Unit(HU). Each patients wore a surgical template for implant placement. During surgery 2mm in diameter and 6mm in length specimens were taken. Histomorphometric analysis was performed using digitalized image analysis software Axiovision 4.3. Also, the Resonance frequency analysis(RFA) and insertion torque values were recorded. Results; The highest histomorphometric values was found in the posterior mandible $32.3{\pm}3.8$, followed by $29.9{\pm}2.6$ for the posterior maxilla, $29.4{\pm}2.6$ for the anterior maxilla, $28.6{\pm}2.3$ for the anterior mandible(p=0.214). The hounsfield unit was $989.2{\pm}258.1$ in the posterior mandible, $845.0{\pm}241.5$ in the anterior maxilla, $744.5{\pm}92.6$ in the anterior mandible, $697.3{\pm}136.9$ in the posterior maxilla(p=0.045). This results may suggest that there are strong correlation between the histomorphometric values and hounsfield unit(r=0.760, p<0.05). The RF measurements were $81.9{\pm}2.4$ ISQ in the posterior mandible, $79.0{\pm}1.4$ ISQ in the anterior mandible, $78.3{\pm}4.6$ ISQ in the posterior maxilla, $76.5{\pm}5.0$ ISQ in the anterior maxilla(p=0.048). The insertion torque values was $43.2{\pm}4.2\;Ncm$ in the posterior mandible, $42.0{\pm}0.0\;Ncm$ in the anterior mandible, $41.3{\pm}4.1\;Ncm$ in the posterior maxilla, $40.8{\pm}3.8\;Ncm$ in the anterior maxilla(p=0.612). This results may suggest that there are statistical significance between the hounsfield unit and the insertion torque values(r=0.494, p<0.05), the histomorphometric values and the insertion torque values(r=0.689, p<0.05). But there was no correlation between histomorphometric values and ISQ. There was no statistical significance in age and gender effect on parameters. Conclusions; There was significant correlations between bone density and implant stability parameters. The bone density measurements using preoperative CT may help clinicians to predict primary stability before implant insertion, which is associated with implant survival rates.
The aim of this study wasto conduct a quantitative analysis of CT image quality according to an algorithm designed to reduce metal artifacts induced by metal components. Ten baseline images were obtained with the standard filtered back-projection algorithm using spectral detector-based CT and CT ACR 464 phantom, and ten images were also obtained on the identical phantom with the standard filtered back-projection algorithm after inducing metal artifacts. After applying the to raw data from images with metal artifacts, ten additional images for each were obtained by applying the virtual monoenergetic algorithm. Regions of interest were set for polyethylene, bone, acrylic, air, and water located in the CT ACR 464 phantom module 1 to conduct compare the Hounsfield units for each algorithm. The algorithms were individually analyzed using root mean square error, mean absolute error, signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index to assess the overall image quality. When the Hounsfield units of each algorithm were compared, a significant difference was found between the images with different algorithms (p < .05), and large changes were observed in images using the virtual monoenergetic algorithm in all regions of interest except acrylic. Image quality analysis indices revealed that images with the metal artifact reduction algorithm had the highest resolution, but the structural similarity index was highest for images with the metal artifact reduction algorithm followed by an additional virtual monoenergetic algorithm. In terms of CT images, the metal artifact reduction algorithm was shown to be more effective than the monoenergetic algorithm at reducing metal artifacts, but to obtain quality CT images, it will be important to ascertain the advantages and differences in image qualities of the algorithms, and to apply them effectively.
Nam, Kyoung Hyup;Seo, Il;Kim, Dong Hwan;Lee, Jae Il;Choi, Byung Kwan;Han, In Ho
Journal of Korean Neurosurgical Society
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v.62
no.4
/
pp.442-449
/
2019
Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.
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