Displaying publications 1 - 20 of 1044 in total

  1. Hayati F, Wong MJJ, Jailani RF, Ng CY
    ANZ J Surg, 2021 10;91(10):2226.
    PMID: 34665495 DOI: 10.1111/ans.17088
    Matched MeSH terms: Tomography, X-Ray Computed
  2. Ariffin AC, Ngadiron H
    World J Surg, 2018 04;42(4):1212.
    PMID: 28879497 DOI: 10.1007/s00268-017-4222-1
    Matched MeSH terms: Tomography, X-Ray Computed*
  3. Pszczolkowski S, Manzano-Patrón JP, Law ZK, Krishnan K, Ali A, Bath PM, et al.
    Eur Radiol, 2021 Oct;31(10):7945-7959.
    PMID: 33860831 DOI: 10.1007/s00330-021-07826-9
    OBJECTIVES: To test radiomics-based features extracted from noncontrast CT of patients with spontaneous intracerebral haemorrhage for prediction of haematoma expansion and poor functional outcome and compare them with radiological signs and clinical factors.

    MATERIALS AND METHODS: Seven hundred fifty-four radiomics-based features were extracted from 1732 scans derived from the TICH-2 multicentre clinical trial. Features were harmonised and a correlation-based feature selection was applied. Different elastic-net parameterisations were tested to assess the predictive performance of the selected radiomics-based features using grid optimisation. For comparison, the same procedure was run using radiological signs and clinical factors separately. Models trained with radiomics-based features combined with radiological signs or clinical factors were tested. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) score.

    RESULTS: The optimal radiomics-based model showed an AUC of 0.693 for haematoma expansion and an AUC of 0.783 for poor functional outcome. Models with radiological signs alone yielded substantial reductions in sensitivity. Combining radiomics-based features and radiological signs did not provide any improvement over radiomics-based features alone. Models with clinical factors had similar performance compared to using radiomics-based features, albeit with low sensitivity for haematoma expansion. Performance of radiomics-based features was boosted by incorporating clinical factors, with time from onset to scan and age being the most important contributors for haematoma expansion and poor functional outcome prediction, respectively.

    CONCLUSION: Radiomics-based features perform better than radiological signs and similarly to clinical factors on the prediction of haematoma expansion and poor functional outcome. Moreover, combining radiomics-based features with clinical factors improves their performance.

    KEY POINTS: • Linear models based on CT radiomics-based features perform better than radiological signs on the prediction of haematoma expansion and poor functional outcome in the context of intracerebral haemorrhage. • Linear models based on CT radiomics-based features perform similarly to clinical factors known to be good predictors. However, combining these clinical factors with radiomics-based features increases their predictive performance.

    Matched MeSH terms: Tomography, X-Ray Computed*
  4. Chan R, Kumar G, Abdullah B, Ng Kh, Vijayananthan A, Mohd Nor H, et al.
    Biomed Imaging Interv J, 2011 Apr;7(2):e12.
    PMID: 22287986 MyJurnal DOI: 10.2349/biij.7.2.e12
    To optimize the delay time before the initiation of arterial phase scan in the detection of focal liver lesions in contrast enhanced 5 phase liver CT using the bolus tracking technique.
    Matched MeSH terms: Tomography, X-Ray Computed
  5. Jasmine Pemeena Priyadarsini M, Kotecha K, Rajini GK, Hariharan K, Utkarsh Raj K, Bhargav Ram K, et al.
    J Healthc Eng, 2023;2023:3563696.
    PMID: 36776955 DOI: 10.1155/2023/3563696
    The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient's treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  6. Abdulkareem KH, Mostafa SA, Al-Qudsy ZN, Mohammed MA, Al-Waisy AS, Kadry S, et al.
    J Healthc Eng, 2022;2022:5329014.
    PMID: 35368962 DOI: 10.1155/2022/5329014
    Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  7. Joshi SC, Pant I, Hamzah F, Kumar G, Shukla AN
    Indian J Cancer, 2008 12 30;45(4):137-41.
    PMID: 19112200
    Positron emission tomography (PET) has emerged as an important diagnostic tool in the management of lung cancers. Although PET is sensitive in detection of lung cancer, but FDG (2-deoxy-2- 18 fluro-D-glucose) is not tumor specific and may accumulate in a variety of nonmalignant conditions occasionally giving false positive result. Addition of CT to PET improves specificity foremost, but also sensitivity in tumor imaging. Thus, PET/CT fusion images are a more accurate test than either of its individual components and are probably also better than side-by-side viewing of images from both modalities. PET/CT fusion images are useful in differentiating between malignant and benign disease, fibrosis and recurrence, staging and in changing patient management to more appropriate therapy. With analysis and discussion it appears that PET/ CT fusion images have the potential to dramatically improve our ability to manage the patients with lung cancer and is contributing to our understanding of cancer cell biology and in development of new therapies.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*; Tomography, X-Ray Computed/trends
  8. Radhiana H, Azian AA, Razali MR, Kamariah CM
    Med J Malaysia, 2010 Dec;65(4):319-25.
    PMID: 21901958
    Computed tomography (CT) is widely used in assessing clinically stable patients with blunt abdominal trauma. In these patients, liver is one of the commonest organs being injured and CT can accurately identify and assess the extent of the injury. The CT features of blunt liver trauma include laceration, subcapsular or parenchymal haematomas, active haemorrhage and vascular injuries. Widespread use of CT has notably influenced the management of blunt liver injury from routine surgical to nonsurgical management. We present pictorial illustrations of various liver injuries depicted on CT in patients with blunt trauma.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  9. Majid AA, Rathakrishnan V, Alhady SF
    J R Soc Med, 1991 Nov;84(11):686-7.
    PMID: 1744882
    Matched MeSH terms: Tomography, X-Ray Computed*
  10. Rais NNM, Bradley DA, Hashim A, Osman ND, Noor NM
    Appl Radiat Isot, 2019 Nov;153:108810.
    PMID: 31351374 DOI: 10.1016/j.apradiso.2019.108810
    For a range of doses familiarly incurred in computed tomography (CT), study is made of the performance of Germanium (Ge)-doped fibre dosimeters formed into cylindrical and flat shapes. Indigenously fabricated 2.3 mol% and 6 mol% Ge-dopant concentration preforms have been used to produce flat- and cylindrical-fibres (FF and CF) of various size and diameters; an additional 4 mol% Ge-doped commercial fibre with a core diameter of 50 μm has also been used. The key characteristics examined include the linearity index f(d), dose sensitivity and minimum detectable dose (MDD), the performance of the fibres being compared against that of lithium-fluoride based TLD-100 thermoluminescence (TL) dosimeters. For doses in the range 2-40 milligray (mGy), delivered at constant potential of 120 kilovoltage (kV), both the fabricated and commercial fibres demonstrate supralinear behaviours at doses  4 mGy. In terms of dose sensitivity, all of the fibres show superior TL sensitivity when compared against TLD-100, the 2.3 mol% and 6 mol% Ge-doped FF demonstrating the greatest TL sensitivity at 84 and 87 times that of TLD-100. The TL yields for the novel Ge-doped silica glass render them appealing for use within the present medical imaging dose range, offering linearity at high sensitivity down to less than 2 mGy.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  11. Ooi EH, Lee KW, Yap S, Khattab MA, Liao IY, Ooi ET, et al.
    Comput Biol Med, 2019 03;106:12-23.
    PMID: 30665137 DOI: 10.1016/j.compbiomed.2019.01.003
    Effects of different boundary conditions prescribed across the boundaries of radiofrequency ablation (RFA) models of liver cancer are investigated for the case where the tumour is at the liver boundary. Ground and Robin-type conditions (electrical field) and body temperature and thermal insulation (thermal field) conditions are examined. 3D models of the human liver based on publicly-available CT images of the liver are developed. An artificial tumour is placed inside the liver at the boundary. Simulations are carried out using the finite element method. The numerical results indicated that different electrical and thermal boundary conditions led to different predictions of the electrical potential, temperature and thermal coagulation distributions. Ground and body temperature conditions presented an unnatural physical conditions around the ablation site, which results in more intense Joule heating and excessive heat loss from the tissue. This led to thermal damage volumes that are smaller than the cases when the Robin type or the thermal insulation conditions are prescribed. The present study suggests that RFA simulations in the future must take into consideration the choice of the type of electrical and thermal boundary conditions to be prescribed in the case where the tumour is located near to the liver boundary.
    Matched MeSH terms: Tomography, X-Ray Computed*
  12. Razali MASM, Ahmad MZ, Shuaib IL, Osman ND
    Radiat Prot Dosimetry, 2020 Jun 13;188(2):213-221.
    PMID: 31885043 DOI: 10.1093/rpd/ncz278
    The aim of this study was to propose local diagnostic reference levels (LDRLs) for the most common computed tomography (CT) examinations (including contrast and non-contrast scan phase) performed at Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia (USM), Malaysia. A retrospective CT dose survey of 1488 subjects from January 2015 until December 2018 was performed at AMDI USM, Malaysia. The proposed DRLs were established at 50th and 75th percentile of dose distribution for all dose metrics (CT dose index [CTDI]; CTDIvol, CTDIw and dose-length product). The proposed LDRLs were compared with national DRLs and other established DRLs. The 10 most common CT examinations at AMDI were thorax-abdomen-pelvis (TAP) CT (46%), followed by pelvis CT (17%), abdomen-pelvis CT (10%), brain/head CT (9%) and other CT protocols. The local DRLs were established using the third quartile values of dose distribution and were categorized based on CT region protocols. Most of the proposed DRLs were exceeded the national DRLs (63%) and other international DRLs (67%). From the dose auditing, almost half of the recent dose data (for year 2018) exceeded the proposed local DRLs and the unusual dose were observed in TAP, brain/head and pelvis CT examinations. The unusual higher dose could be due to higher mAs settings, higher number of scan phase for contrast study and higher pitch factor. The local DRLs should be established for dose optimization and reduction of the occurrence of excessive radiation exposure to the patients. The establishment of the Ads and LDRLs should also consider all the factors that affect the variation in DRLs such as CT technology, scanning protocols and population characteristics. The local dose distribution should always be revised for improvement of the current local practice.
    Matched MeSH terms: Tomography, X-Ray Computed*
  13. Lee SA, Chiu CK, Chan CYW, Yaakup NA, Wong JHD, Kadir KAA, et al.
    Spine J, 2020 07;20(7):1114-1124.
    PMID: 32272253 DOI: 10.1016/j.spinee.2020.03.015
    BACKGROUND CONTEXT: Biopsy is important to obtain microbiological and histopathological diagnosis in spine infections and tumors. To date, there have been no prospective randomized trials comparing fluoroscopic guided and computed tomography (CT) transpedicular biopsy techniques. The goal of this study was to evaluate the accuracy, safety, and diagnostic outcome of these two diagnostic techniques.

    PURPOSE: To evaluate the accuracy, safety, and diagnostic outcome of fluoroscopic guided and CT transpedicular biopsy techniques.

    STUDY DESIGN: Prospective randomized trial.

    PATIENT SAMPLE: Sixty consecutive patients with clinical symptoms and radiological features suggestive of spinal infection or malignancy were recruited and randomized into fluoroscopic or CT guided spinal biopsy groups. Both groups were similar in terms of patient demographics, distribution of spinal infections and malignancy cases, and the level of biopsies.

    OUTCOME MEASURES: The primary outcome measure was diagnostic accuracy of both methods, determined based on true positive, true negative, false positive, and false negative biopsy findings. Secondary outcome measures included radiation exposure to patients and doctors, complications, and postbiopsy pain score.

    METHODS: A transpedicular approach was performed with an 8G core biopsy needle. Specimens were sent for histopathological and microbiological examinations. Diagnosis was made based on biopsy results, clinical criteria and monitoring of disease progression during a 6-month follow up duration. Clinical criteria included presence of risk factors, level of inflammatory markers and magnetic resonance imaging findings. Radiation exposure to patients and doctors was measured with dosimeters.

    RESULTS: There was no significant difference between the diagnostic accuracy of fluoroscopic and CT guided spinal biopsy (p=0.67) or between the diagnostic accuracy of spinal infection and spinal tumor in both groups (p=0.402 for fluoroscopy group and p=0.223 for CT group). Radiation exposure to patients was approximately 26 times higher in the CT group. Radiation exposure to doctors in the CT group was approximately 2 times higher compared to the fluoroscopic group if a lead shield was not used. Lead shields significantly reduced radiation exposure to doctors anywhere from 2 to 8 times. No complications were observed for either group and the differences in postbiopsy pain scores were not significant.

    CONCLUSIONS: The accuracy, procedure time, complication rate and pain score for both groups were similar. However, radiation exposure to patients and doctors were significantly higher in the CT group without lead protection. With lead protection, radiation to doctors reduced significantly.

    Matched MeSH terms: Tomography, X-Ray Computed*
  14. Tan D, Mohamad NA, Wong YH, Yeong CH, Cheah PL, Sulaiman N, et al.
    Int J Hyperthermia, 2019;36(1):554-561.
    PMID: 31132888 DOI: 10.1080/02656736.2019.1610800
    Purpose: This study aimed to evaluate the effects of various computed tomography (CT) acquisition parameters and metal artifacts on CT number measurement for CT thermometry during CT-guided thermal ablation. Methods: The effects of tube voltage (100-140 kVp), tube current (20-250 mAs), pitch (0.6-1.5) and gantry rotation time (0.5, 1.0 s) as well as metal artifacts from a radiofrequency ablation (RFA) needle on CT number were evaluated using liver tissue equivalent polyacrylamide (PAA) phantom. The correlation between CT number and temperature from 37 to 80 °C was studied on PAA phantom using optimum CT acquisition parameters. Results: No statistical significant difference (p > 0.05) was found on CT numbers under the variation of different acquisition parameters for the same temperature setting. On the other hand, the RFA needle has induced metal artifacts on the CT images of up to 8 mm. The CT numbers decreased linearly when the phantom temperature increased from 37 to 80 °C. A linear regression analysis on the CT numbers and temperature suggested that the CT thermal sensitivity was -0.521 ± 0.061 HU/°C (R2 = 0.998). Conclusion: CT thermometry is feasible for temperature assessment during RFA with the current CT technology, which produced a high CT number reproducibility and stable measurement at different CT acquisition parameters. Despite being affected by metal artifacts, the CT-based thermometry could be further developed as a tissue temperature monitoring tool during CT-guided thermal ablation.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  15. Teh V, Sim KS, Wong EK
    Scanning, 2016 Nov;38(6):842-856.
    PMID: 27302216 DOI: 10.1002/sca.21334
    According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  16. Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B
    Comput Biol Med, 2022 Jun;145:105464.
    PMID: 35390746 DOI: 10.1016/j.compbiomed.2022.105464
    BACKGROUND: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning.

    METHOD: Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.

    RESULTS: The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.

    CONCLUSION: While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  17. Tuang GJ, Zahedi FD, Husain S, Hamizan AKW, Kew TY, Thanabalan J
    Int J Med Sci, 2023;20(2):211-218.
    PMID: 36794158 DOI: 10.7150/ijms.68095
    Introduction: The fundament of forensic science lies in identifying a body. The morphological complexity of the paranasal sinus (PNS), which varies greatly amongst individual, possess a discriminatory value that potentially contributes to the radiological identification. The sphenoid bone represents the keystone of the skull and forms part of the cranial vault. It is intimately associated with vital neurovascular structures. The sphenoid sinus, located within the body of the sphenoid bone, has variable morphology. The sphenoid septum's inconsistent position and the degree, as well as the direction disparities of sinus pneumatization, have indeed accorded it a unique structure in providing invaluable information in forensic personnel identification. Additionally, the sphenoid sinus is situated deep within the sphenoid bone. Therefore, it is well protected from traumatic degradation from external causes and can be potentially utilized in forensic studies. The authors aim to study the possibility of variation among the race, and gender in the Southeast Asian (SEA) population, using volumetric measurements of the sphenoid sinus. Materials and methods: This is a retrospective cross-sectional analysis of computerized tomographic (CT) imaging of the PNS of 304 patients (167 males, 137 females) in a single centre. The volume of the sphenoid sinus was reconstructed and measured using commercial real-time segmentation software. Result: The total volume of sphenoid sinus of male gender had shown to be larger, 12.22 (4.93 - 21.09) cm3 compared to the counterpart of 10.19 (3.75 - 18.72) cm3 (p = .0090). The Chinese possessed a larger total sphenoid sinus volume, 12.96 (4.62 - 22.21) cm3) than the Malays, 10.68 (4.13 - 19.25) cm3 (p = .0057). No correlation was identified between the age and volume of the sinus (cc= -.026, p = .6559). Conclusion: The sphenoid sinus volume in males was found to be larger than those of females. It was also shown that race influences sinus volume. Volumetric analysis of the sphenoid sinus can potentially be utilized in gender and race determination. The current study provided normative data on the sphenoid sinus volume in the SEA region, which can be helpful for future studies.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  18. Zahedi FD, Yaacob NM, Wang Y, Abdullah B
    Clin Otolaryngol, 2023 Mar;48(2):271-285.
    PMID: 35997634 DOI: 10.1111/coa.13975
    OBJECTIVES: To determine the anatomical variations of the lateral nasal wall and anterior skull base amongst populations in different geographical regions.

    DESIGN: Systematic review and meta-analysis.

    METHODS: Using PRISMA guidelines, SCOPUS and PUBMED databases were searched from inception until 1 March 2022. The regions and populations identified were from Europe, Asia, Middle East, Australia-New Zealand-Oceania, South America, North America and Africa. Random-effects model was used to estimate the pooled prevalence with 95% confidence intervals (CIs). Heterogeneity was assessed using the I2 statistic and Cochran's Q test.

    MAIN OUTCOME MEASURES: Anatomical variations of the lateral nasal wall and anterior skull base confirmed by computed tomography scan.

    RESULTS: Fifty-six articles were included with a total of 11 805 persons. The most common anatomical variation of the ostiomeatal complex was pneumatization of the agger nasi (84.1%), olfactory fossa was Keros type 2 (53.8%) and ethmoids was asymmetry of the roof (42.8%). Sphenoethmoidal and suprabullar cells have a higher prevalence in North Americans (53.7%, 95% CI: 46.00-61.33) while asymmetry of ethmoid roof more common in Middle Easterns (85.5%, 95% CI: .00-100). Bent uncinate process has greater prevalence in Asians while supraorbital ethmoid cells and Keros type 3 more common in non-Asians. The overall studies have substantial heterogeneity and publication bias.

    CONCLUSION: Certain anatomic variants are more common in a specific population. The 'approach of analysis' plays a role in the prevalence estimates and consensus should be made in future studies regarding the most appropriate 'approach of analysis' either by persons or by sides.

    Matched MeSH terms: Tomography, X-Ray Computed*
  19. Raman K, Govindaraju R, James K, Abu Bakar MZ, Patil N, Shah MN
    J Laryngol Otol, 2023 Feb;137(2):169-173.
    PMID: 34924062 DOI: 10.1017/S0022215121004175
    OBJECTIVE: Knowledge of anatomical variations of the frontal recess and frontal sinus and recognition of endoscopic landmarks are vital for safe and effective endoscopic sinus surgery. This study revisited an anatomical landmark in the frontal recess that could serve as a guide to the frontal sinus.

    METHOD: Prevalence of the anterior ethmoid genu, its morphology and its relationship with the frontal sinus drainage pathway was assessed. Computed tomography scans with multiplanar reconstruction were used to study non-diseased sinonasal complexes.

    RESULTS: The anterior ethmoidal genu was present in all 102 anatomical sides studied, independent of age, gender and race. Its position was within the frontal sinus drainage pathway, and the drainage pathway was medial to it in 98 of 102 cases. The anterior ethmoidal genu sometimes extended laterally and formed a recess bounded by the lamina papyracea laterally, by the uncinate process anteriorly and by the bulla ethmoidalis posteriorly. Distance of the anterior ethmoidal genu to frontal ostia can be determined by the height of the posterior wall of the agger nasi cell rather than its volume or other dimensions.

    CONCLUSION: This study confirmed that the anterior ethmoidal genu is a constant anatomical structure positioned within frontal sinus drainage pathway. The description of anterior ethmoidal genu found in this study explained the anatomical connection between the agger nasi cell, uncinate process and bulla ethmoidalis and its structural organisation.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
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