Neurosurgeons often neglect the sphenoid sinus due to its deep location and difficulties in accessing during surgical interventions. Disease of the sphenoid sinus is difficult to diagnose since its presenting symptoms are difficult to recognize. Moreover, compared with other paranasal sinuses, the sphenoid sinus is considered the most variable air sinus in terms of its degree of pneumatization, number and position of inter-sinus septa, and its relationship with the surrounding anatomical structures. Anatomical variations of the sphenoid sinus are significant from a neurosurgical point of view. Understanding of these variations and its relationships with surrounding structures such as the internal carotid artery, optic nerve, and pituitary gland are clinically relevant to minimize injuries associated with surgical procedures that involve sphenoid sinus. We implemented principles of imaging using computed tomography to elucidate any anatomical variations of the sphenoid sinus in the Ethiopian population. We conducted a prospective study in 200 patients with ages 18–79, who underwent scans of the sphenoid sinus at the Tikur Anbessa Referral Teaching Hospital in 2017–2018. Our findings revealed an incidence of anatomographical variations in terms of pneumatization that varied between 2–50%. These variants include 2% conchal, 25.5% presellar, 50% sellar, and 22.5% postsellar pneumatization. We also demonstrated anatomographic variants in terms of septation, 77.5% single complete septa, 11.5% single incomplete, 10% double septa, and 1% absence of septa. In summary, the sellar pneumatization was found to be the most clinically relevant anatomographic variant among Ethiopians participating in the study, of which 90% were tomographically single septated. These variants must be taken into consideration during trans-sphenoidal surgery and knowledge of the variations has clinical implication in minimizing injuries during invasive surgical procedures involving the sphenoid sinus.

BACKGROUND: Computed Tomography plays a priceless role for diagnostic and therapeutic purpose; however, applying an optimized Computed Tomography Technique to produce qualified image while delivering minimum radiation dose to patients is the common challenge. The main objective of this study was to establish local diagnostic reference levels for adult patients who visited abdominopelvic Computed Tomography examination. METHODS: A total of 158 patients who had taken abdominopelvic Computed Tomography examination from three selectedAmhara region hospitals were investigated. Both prospective and retrospective techniques of data collection were used while collecting the data in the entire sample. Two GE - Optima Computed Tomography 540 (16 slices) and one Phillips – Brilliance (64slices), were employed during data collections. Data for patient demographics scan protocols, Computed Tomography dose descriptors and machine specifications were collected and analyzed by using SPSS software version 26. RESULTS: The third quartile estimated computed tomography dose index volume and dose length product, which is the local Diagnostic Reference Levels, were 12 mGy and 1904 cm.mGy respectively. The investigated local Diagnostic Reference Levels of Computed Tomography Dose index volume (mGy) was comparable to other international Diagnostic Reference Levels. However, the third quartile value of dose length product (cm.mGy) was higher than other reported international Diagnostic Reference Levels. CONCLUSION: The values of local Diagnostic Reference Levels presented in this work can be used as a baseline upon which future dose measurements can be compared in Amhara region.

Objectives The aim of this research was to develop an appropriate algorithm-routine that can automatically detect and classify liver cancers from the digital images of CT sample based on the texture and morphological features. Method The digital image processing (DIP) provides techniques first for liver image segmentation using semi-automatic technique from the enhanced abdominal CT images and classification using Artificial Neural Network (ANN) through texture features, then the liver tumor automatically extracted from the liver image using Otsu method which is a histogram based thresholding and the classification using ANN through morphological features. Result The samples were collected from a number of thirty six diagnosed patients in the procedure of abdominal CT images, at axial view in Black Lion Hospital from DICOM store in Addis Ababa. These images were selected to be processed and analyzed to get best generated result of the predefined classification model based on the extracted features. First nine texture features differentiate 12 normal and 24 abnormal liver images out of the given sample’s containing 36 images then tumor images extracted from the 24 abnormal images, the ANN classifier using six morphological features classified the 24 abnormal liver images as benign and malignant. It’s result presented by confusion matrix and showed that the maximum accuracy rate for tumor classification was 95.8%. The performance can be increased more by increasing the number of samples. Conclusions The outcome of this research work help radiologists to follow the condition of diseases at early stages and classify tumor as benign and malignant. It also helps in decreasing the rate of presence of disease by detecting cancer in its earlier stages.