Image Definition Research Articles

Osteosarcoma.

Osteosarcoma is the most common primary malignant tumour of the bone. Osteosarcoma incidence is bimodal, peaking at 18 and 60 years of age, and is slightly more common in males. The key pathophysiological mechanism involves several possible genetic drivers of disease linked to bone formation, causing malignant progression and metastasis. While there have been significant improvements in the outcome of patients with localized disease, with event-free survival outcomes exceeding 60%, in patients with metastatic disease, event-free survival outcomes remain poor at less than 30%. The suspicion of osteosarcoma based on radiographs still requires pathological evaluation of a bone biopsy specimen for definitive diagnosis and CT imaging of the chest should be performed to identify lung nodules. So far, population-based screening and surveillance strategies have not been implemented due to the rarity of osteosarcoma and the lack of reliable markers. Current screening focuses only on groups at high risk such as patients with genetic cancer predisposition syndromes. Management of osteosarcoma requires a multidisciplinary team of paediatric and medical oncologists, orthopaedic and general surgeons, pathologists, radiologists and specialist nurses. Survivors of osteosarcoma require specialized medical follow-up, as curative treatment consisting of chemotherapy and surgery has long-term adverse effects, which also affect the quality of life of patients. The development of osteosarcoma model systems and related research as well as the evaluation of new treatment approaches are ongoing to improve disease outcomes, especially for patients with metastases.

Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.

Prevalence of Suspected non‐Alzheimer’s disease pathophysiology across the Alzheimer’s disease clinical spectrum: a meta‐analysis

AbstractBackgroundSuspected non‐Alzheimer’s disease pathophysiology (SNAP) is a heterogeneous biomarker‐based concept that describes individuals with neurodegeneration without amyloid pathology. Our aim was to perform a meta‐analysis of all available SNAP studies to better estimate the prevalence of SNAP based on different definitions across the Alzheimer’s disease (AD) clinical spectrum.MethodA search in PubMed was performed to identify all papers on SNAP. From these papers, we extracted the prevalence of persons with SNAP, biomarker negative persons and amyloid positive persons. We performed a meta‐analysis on overall SNAP prevalence by cognitive status and compared different SNAP definitions according to ATN biomarker classifications. In persons without dementia, we examined SNAP prevalence based on imaging versus CSF definitions and investigated differences in sex and APOE‐e4 carriership. We used R packages meta and metafor.ResultWe included 47 papers with SNAP across the AD clinical spectrum. Table 1 presents SNAP prevalence by cognitive status, compared to the prevalence of other biomarker groups. Overall prevalence of SNAP was 19.6% (95% CI 16.3‐23.2) in persons with normal cognition (NC), 15.6% (12.3‐19.3) in persons with mild cognitive impairment (MCI), and 10.1% (5.5‐15.8) in persons with dementia. In persons with NC, SNAP based on tau only (A‐T+N‐) was most common (36%) and SNAP based on both tau and neurodegeneration (A‐T+N+) least common (21%). In persons with MCI or dementia, SNAP based on neurodegeneration only (A‐T‐N+) was most common (MCI 39%, dementia 34%) and SNAP based on tau only least common (MCI 20%, dementia 7%). In persons without dementia, a similar SNAP prevalence was observed between imaging and CSF biomarker definitions (NC 19 vs. 20%, MCI 18 vs. 12%). Yet, persons with NC‐SNAP using imaging‐based definitions were more often male and less often APOEe4 carrier as compared to NC‐SNAP using CSF‐based definitions (imaging: male 54% and APOEe4 16%; CSF: male 41% and APOEe4 34%).ConclusionSNAP is common across the AD clinical spectrum. In persons without dementia, CSF and imaging definitions of SNAP resulted in a similar prevalence but in differences in sex and APOE‐e4 carriership in NC‐SNAP. Findings are valuable for AD trial design and clinical diagnostics.

Luminosity rectified blind Richardson-Lucy deconvolution for single retinal image restoration

Background and objectiveDue to imperfect imaging conditions, retinal images can be degraded by uneven/insufficient illumination, blurriness caused by optical aberrations and unintentional motions. Degraded images reduce the effectiveness of diagnosis by an ophthalmologist. To restore the image quality, in this research we propose the luminosity rectified Richardson-Lucy (LRRL) blind deconvolution framework for single retinal image restoration. MethodsWe established an image formation model based on the double-pass fundus reflection feature and developed a differentiable non-convex cost function that jointly achieves illumination correction and blind deconvolution. To solve this non-convex optimization problem, we derived the closed-form expression of the gradients and used gradient descent with Nesterov-accelerated adaptive momentum estimation to accelerate the optimization, which is more efficient than the traditional half quadratic splitting method. ResultsThe LRRL was tested on 1719 images from three public databases. Four image quality matrixes including image definition, image sharpness, image entropy, and image multiscale contrast were used for objective assessments. The LRRL was compared against the state-of-the-art retinal image blind deconvolution methods. ConclusionsOur LRRL corrects the problematic illumination and improves the clarity of the retinal image simultaneously, showing its superiority in terms of restoration quality and implementation efficiency. The MATLAB code is available on Github.

Infrared Thermography to Evaluate Thermal Comfort under Controlled Ambient Conditions

Infrared thermography (IRT) is often used to assess body temperature and can be useful as a diagnostic tool to detect human diseases. Despite this clear application in medicine, some studies can be found in the literature pointing to the use of IRT to measure body temperature as a parameter to evaluate thermal comfort inside buildings. However, there are still some issues that are understudied that this paper tried to address. For this purpose, an intensive experimental campaign was carried out, in which different combinations of temperature and relative humidity were implemented. Thermal images were taken of the face of a young adult, while the air temperature and relative humidity were assessed, as well as the body temperature, using traditional means. The results confirmed that different IR cameras and different ambient conditions (air temperature) impact the image resolution and definition. A linear correlation between the IRT results and the PMV was found for six subregions of the face. This correlation was higher in the forehead, cheekbones, and chin, and less interesting when measuring the temperature of the nose. However, if the overall temperature of the face is assessed, a good agreement between the PMV and the IRT results can still be found, indicating that the average facial temperature can be used as an indicator for the determination of thermal comfort. A prediction model for PMV based on IRT was proposed, with a root mean square error close to 0.70, when applied in a face temperature range between 28.9 °C and 34.4 °C.

Deep learning-based internal gross target volume definition in 4D CT images of lung cancer patients.

Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intra-fractional target motion. However, it is usually time-consuming and easily subjected to intra-observer and inter-observer variability. So far, few studies have been explored to directly predict iGTV by deep learning technique, because the iGTV contains not only the gross target volume (GTV) but also the motion information of the GTV. This work was an exploratory study to present a deep learning-based framework to segment iGTV rapidly and accurately in 4D CT images for lung cancers. Five models, including 3D UNet, mmUNet with point-wise add merging approach (mmUNet-add), mmUNet with concatenate fusion strategy (mmUNet-cat), gruUNet with point-wise add fusion approach (gruUNet-add), and gruUNet with concatenate method (gruUNet-cat), were adopted for iGTV segmentation. All the models originated from the 3D UNet network, with multi-channel multi-path and convolutional gated recurrent unit (GRU) added in the mmUNet and gruUNet networks, respectively. Seventy patients with lung cancers were collected and 55 cases were randomly selected as the training set, and 15 cases as the testing set. In addition, the segmentation results of the five models were compared with the ground truths qualitatively and quantitatively. In terms of Dice Similarity Coefficient (DSC), the proposed four networks (mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat) increased the DSC score of 3D UNet from 0.6945 to 0.7342, 0.7253, 0.7405, and 0.7365, respectively. However, the differences were not statistically significant (p > 0.05). After a simple post-processing to remove the small isolated connected regions, the mean 95th percentile Hausdorff distances (HD_95s) of the 3D UNet, mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat networks were 19.70, 15.75, 15.84, 15.61, and 15.83 mm, respectively, corresponding to 25.35, 25.96, 25.11, 28.23, and 24.47 mm before the post-processing. With regard to runtime, significant elapsed time growths (about 70s and 230s) were observed both in the mmUNet and gruUNet architectures due to the increasing parameters. But the mmUNet structure showed less growth. Our study demonstrated the ability of the deep learning technique to predict iGTVs directly. With the introduction of multi-channel multi-path and convolutional GRU, the segmentation accuracy was improved under certain conditions with a reduced segmentation efficiency and a further research topic when the 3D UNet network would lead to poor performance is elicited. Less efficiency degradation was observed in the mmUNet structure. Besides, the element-wise add fusing strategy was favorable to increase DSC, whereas HD_95 benefited from the concentrate merging approach. Nevertheless, the segmentation accuracy by deep learning still remains to be improved.

Secondary Progressive Multiple Sclerosis: A Review of Clinical Characteristics, Definition, Prognostic Tools, and Disease-Modifying Therapies.

Many challenges exist in the precise diagnosis and clinical management of secondary progressive multiple sclerosis (SPMS) because of the lack of definitive clinical, imaging, immunologic, or pathologic criteria that demarcate the transition from relapsing-remitting MS to SPMS. This review provides an overview of the diagnostic criteria/definition and the heterogeneity associated with different SPMS patient populations; it also emphasizes the importance of available prospective/retrospective tools to identify patients with SPMS earlier in the disease course so that approved disease-modifying therapies and nonpharmacological strategies will translate into better outcomes. Delivery of such interventions necessitates an evolving patient-clinician dialog within the context of a multidisciplinary team.

Definition and attributes of the emotional memory images underlying psychophysiological dis-ease.

Previously, we proposed a "Split-second Unlearning" model to explain how emotional memories could be preventing clients from adapting to the stressors of daily living, thus forming a barrier to learning, health and well-being. We suggested that these emotional memories were mental images stored inside the mind as 'emotional memory images' (EMIs). To elaborate on the nature of these emotional memory images within the context of split-second learning and unlearning and the broader field of psychoanalysis, to initiate a conversation among scholars concerning the path that future healthcare research, practice, and policy should take. A narrative review of the attributes of EMIs utilizing relevant and contentious research and/or scholarly publications on the topic, facilitated by observations and approaches used in clinical practice. Results: We propose a refined definition of EMIs as Trauma induced, non-conscious, contiguously formed multimodal mental imagery, which triggers an amnesic, anachronistic, stress response within a split-second. The systematic appraisal of each attribute of an EMI supports the idea that the EMI is distinct from similar entities described in literature, enabling further sophistication of our Split-second Unlearning model of psychophysiological dis-ease. Exploration of the concept of EMIs provides further insight on mechanisms associated with psychophysiological dis-ease and opportunities for therapeutic approaches.

An AI Based Approach for Medicinal Plant Identification Using Deep CNN Based on Global Average Pooling

Medicinal plants have always been studied and considered due to their high importance for preserving human health. However, identifying medicinal plants is very time-consuming, tedious and requires an experienced specialist. Hence, a vision-based system can support researchers and ordinary people in recognising herb plants quickly and accurately. Thus, this study proposes an intelligent vision-based system to identify herb plants by developing an automatic Convolutional Neural Network (CNN). The proposed Deep Learning (DL) model consists of a CNN block for feature extraction and a classifier block for classifying the extracted features. The classifier block includes a Global Average Pooling (GAP) layer, a dense layer, a dropout layer, and a softmax layer. The solution has been tested on 3 levels of definitions (64 × 64, 128 × 128 and 256 × 256 pixel) of images for leaf recognition of five different medicinal plants. As a result, the vision-based system achieved more than 99.3% accuracy for all the image definitions. Hence, the proposed method effectively identifies medicinal plants in real-time and is capable of replacing traditional methods.

Memory-Efficient Deformable Convolution Based Joint Denoising and Demosaicing for UHD Images

This paper introduces deformable convolution in deep learning based joint denoising and demosaicing (JDD), which yields more adaptable representation and larger receptive fields in features extraction for a superior restoration performance. However, the deformable convolution generally leads to considerable computational load and irregular memory access bottleneck, limiting its extensive deployment on edge devices. To address this issue, we develop grouping strategy and assign independent offsets to each kernel group to reduce the computation latency while keeping the accuracy. Motivated by the exploration for aggregate distribution characteristics of deformable offsets, we present the offset sharing methodology to simplify the memory access complexity of deformable convolution. As for hardware acceleration, we specially design a novel deformable matrix multiplication workflow incorporated with a deformable memory mapping unit to boost the computational throughput. The verification experiments on FPGA demonstrate that the proposed deformable convolution based JDD can restore 4K Ultra High Definition (UHD) images at 70FPS and yields significant promotion in visual effect and objective quality assessment.

A preliminary study on the hollow anode of a 450 kV rod-pinch diode

This paper proposes a hollow rod as the anode of a rod-pinch diode (RPD) at about 450 kV voltage. Two kinds of hollow rod anodes that may enhance the central radiation dose are obtained through PIC and Monte Carlo simulations. The experimental results show that the hollow anode made by Electrical Discharge Machining (EDM) can effectively enhance the forward central dose of the 450 kV rod-pinch diode, which helps to improve the radiation imaging definition.

Design of remote sensing-based geographic information system

To deal with problems of traditional geographic information collection, such as low real-time, poor authenticity of the data, and unclear description of detailed areas, a design scheme of remote sensing-based geographic information system is proposed. The system mainly consists of information collection, imaging processing, data storage management, scene control and data transmission module. By use of remote sensing technology, the reflected and radiated electromagnetic waves of the target area are collected from a long distance to form an image, and the hue–intensity–saturation (HIS) transformation method is used to enhance the image definition. Weighted fusion algorithm is adopted to process the details of the image. The spatial database stores and manages the text and image data respectively, and establishes the attribute self-correlation mechanism to render the ground objects in the picture with SketchUp software. Finally, using RS422 protocol to transmit information can achieve the effect of multi-purpose, and enhance the anti-interference of the system. The experimental results show that the practical experience of the proposed system is excellent, the geographic information image presented is clear, and the edge details are clearly visible, which can provide users with effective geographic information data.

Brand Image dalam Kajian Filsafat Ilmu

The study of theory and practice in marketing has explored and provided an operational definition of brand image which is one of the most extensive and intensive constructs. The purpose of this study is to describe the brand image in the study of the philosophy of science. This study will explain the brand image which will be explained from the aspects of ontology, epistemology, and axiology. The methodology in this study is to conduct a literature review from empirical sources or previous research. This study produces a conceptual model of age and gender variables that moderate the influence of brand image and brand personality on brand loyalty which affects brand equity.

Ultrasound-guided lumbar plexus block versus transversus abdominis plane block for analgesia in children with hip dislocation: A double-blind, randomized trial.

Lumbar plexus block is often used as analgesia for adult hip surgery, but it is rarely used in pediatric patients. This study aimed to compare the efficacy and feasibility of ultrasound-guided lumbar plexus block versus transversus abdominis plane block for postoperative analgesia in children with hip dislocation. Eighty children undergoing unilateral hip dislocation surgeries at our hospital from October 2019 to February 2021 were randomized to the lumbar plexus block group (group L) and transversus abdominis plane block group (group T). Compared with group L, the regional block time in group T was lower (8.0 ± 2.5 vs 11.5 ± 2.3 min, P < 0.05), and the ultrasound image definition was better (P < 0.05). There were no significant differences in mean blood pressure and heart rate within 24 h (all P > 0.05). Children's Hospital of Eastern Ontario Pain Scale scores were lower in group L than in group T at 18-24 h only (all P < 0.05). The satisfying analgesia rate in group L was higher than in group T (87.5 vs 65%, P < 0.05). No regional block-related complications were found in both groups. Ultrasound-guided lumbar plexus block showed a longer postoperative analgesic effect in children with hip dislocation compared with transversus abdominis plane block.

Usefulness of preoperative extracranial imaging in radiologically suspicious glioblastoma in the West of Scotland and proposal of an imaging diagnostic pathway.

A computed tomography chest, abdomen and pelvis scan (CT CAP) is probably unnecessary if a glioblastoma is detected on the initial CT brain (CTB) before more radiologically definitive magnetic resonance imaging (MRI). We audited its frequency to develop and improve our diagnostic management pathway. Twelve-month retrospective case series from 2018 of patients having an initial CTB suspicious for glioblastoma. We dichotomised patients into two groups: Group 1, tissue proven; and Group 2, non-tissue proven, owing to increased extracranial comorbidity in Group 2, which might influence a medical decision to request a CT CAP despite the radiological diagnosis of a glioblastoma being obvious on an initial CT. We quantified the frequencies of plain and contrast CTBs, CT CAPs and extracranial malignancy. In total, 131 patients had a CTB suspicious for glioblastoma; of these, 72% had a CT CAP and 17% had extracranial malignancy. In Group 1 (n = 84 [mean age 59 years]), 64% had a CT CAP. Plain CTB was undertaken in 24% of patients and contrast CTB in 76%. Extracranial malignancy was present in 8% and 12%. In Group 2 (n = 47 [mean age 73 years]), 85% had a CT CAP. Plain CTB was undertaken in 22% of patients and contrast CTB in 78%. Extracranial malignancy was present in 33% and 23%. Negative CT CAPs were found in ∼88% of CTBs in Group 1 and ∼75% of CTBs in Group 2. Patients having an initial contrast CTB suggestive of glioblastoma, prior to definitive MRI, who are going to be managed surgically, having no history of extracranial malignancy, do not necessarily need a CT CAP unless MRI is non-diagnostic.

Retrospective Analysis on Characteristics of Uterine Smooth Muscle Tumors of Uncertain Malignant Potential—13 Years' Experience

Background: Benign leiomyomas (LM) and malignant leiomyosarcomas (LMS) can be distinguished by increased cellularity with mitotic activity. Uterine smooth muscle tumors of uncertain malignant potential (STUMP) are a group of rare tumors in between, for which there is still no standardized classification, nor any definitive preoperative imaging or laboratory investigations regarding the possible inclusion of STUMP in the differential diagnosis. Methods: In this study, we retrospectively reviewed 6 cases of STUMP, and assessed their pathogenesis, risk factors, and prognostic features. Results: The mean age of STUMP patients was 40.6 years old. No recurrence has been reported in all six cases after more than 3.6 years of follow-up. The mean tumor size was 9.44 cm (range 6.14–12.21). 4 cases (66.7%) with &lt;5 mitoses, 1 case (16.7%) with 5–9 mitoses, and 1 case (16.7%) with &gt;10 mitoses per 10 high-power fields. Immunohistochemical staining for cyclin dependent kinase nhibitor 2A (p16), tumor protein p53 (p53), and Antigen KI-67 (Ki-67) was 100% positive (2/2, 6/6, and 6/6, respectively). The estrogen receptor (ER) expression rate was 50.0% (3/6), and the progesterone receptor (PR) was 33.3% (1/3). There was no correlation between the expression of these biomarkers and mitotic counts or recurrence. Conclusions: The current immunohistochemical biomarkers are ineffective in determining the probability of malignancy in STUMP patients with desire of further fertility. Detection of gene expression profiles or variants using next-generation molecular techniques may aid in disease prediction, diagnosis, treatment, and prognosis.

The Bronze Horseman of Justinian in Constantinople: The Cross-Cultural Biography of a Mediterranean Monument

The Bronze Horseman of Justinian in Constantinople: The Cross-Cultural Biography of a Mediterranean Monument

Reliability of real-world data for diagnosis of metastatic prostate cancer.

397 Background: Real-world data (RWD) is playing an increasingly important role in cancer research. Surrogate endpoints such as metastasis-free survival play an important role in prostate cancer research, leading to interest in its computational extraction, typically with use of International Classification of Disease (ICD) metastatic codes. While prior studies have suggested that ICD codes are valid for identification of patients (pts) with metastatic prostate cancer (MPC), delays in coding may impact their accuracy. The objective of this informatics-based study is to quantify the time delay between diagnosis of MPC and entry of ICD MPC-related code and its interaction with changing institutional healthcare processes. Methods: A single institutional EHR data warehouse was queried to identify a random sample of 100 pts with MPC diagnosis based on ICD codes (ICD10 C79 or ICD9 198.5) from 2013-2021 who were also seen in the genitourinary medical oncology program (GUMOP). Of note, in 6/2018, the GUMOP adopted EHR-specific MPC visit diagnosis identifiers (Dx ID) to improve MPC coding during clinic independent of ICD codes typically used by RWD researchers. Thus, the study cohort was designed to include pts whose first follow up after being diagnosed with MPC was before (n = 50) or after (n = 50) Dx ID implementation. Date of first MPC ICD code entry at any point in the EHR was compared against true date of MPC, based on physician review of definitive imaging or pathology. Data analysis was performed with Wilcox Signed rank test, bivariate analyses, and multivariable linear regression. Covariates included modality of diagnosis confirmation and timing with Dx ID implementation. Results: One hundred pts with MPC ICD coded in the EHR were included, with 29 pts diagnosed by PSMA PET and 71 by conventional imaging. Median time from true MPC diagnosis to first subsequent clinic follow up was &lt; 1 month (IQR 0-2), while median time from true MPC diagnosis to entry of ICD MPC-related code was longer at 4mo (IQR 0-15). 5 pts had C79 applied for N1 disease and 10 pts for work-up of biochemical recurrence. On multivariable analysis of potential factors affecting time interval to MPC ICD entry, Dx ID implementation (b = -6.5 mo [95% CI -1.8 to -11.2], p = 0.007) and non-PSMA based diagnosis (b = -5.7 mo [95% CI -0.5 to -10.8], p = 0.03) were independently associated with shorter time to ICD coding. In subset analysis of the cohort after Dx ID implementation, use of both ICD and Dx ID to identify pts with MPC reduced the median time from true MPC diagnosis to EHR coding (1mo, IQR 0-6.3) compared to ICD alone (2mo, IQR 0-8) (p = 0.003). Conclusions: Timing of MPC ICD entry is highly variable and may carry biases derived from healthcare processes, including data entry and diagnostic testing. This may be improved with EHR workflow interventions. It is essential to have domain knowledge of clinical coding practices to improve information retrieval and recognize potential limitations and biases.

P‐3.7: An Improved Dehazing Algorithm for Fog Image at Night

In order to solve the problem of lack of detail information and serious damage to the texture of the light source area when the dark channel prior dehazing algorithm processes fog images at night, this paper proposes a dehazing algorithm for nighttime fog images using the dark‐state point light source model. By introducing the dark state point light source model, the dark channel credibility weight factor and the pseudo dehazing image, combined with the nighttime image imaging model, the improved transmittance distribution is obtained, and the fog image at night is dehazed. The experimental results show that the image processed by the algorithm in this paper has little loss of texture details, high image definition, and the contrast between the light and dark of the image is better stretched, which can effectively dehaze the fog image at night.

A prospective study of the Various Categories of Crown to Root Ratio of Premolars by Using Cone Beam CT Images

Background: Cone beam computed tomography (CBCT) has gained worldwide acceptance in the field of dentistry, because it provides definite images in the form of slices of single roots which can accurately measure the crown and root lengths of all the teeth including premolars. Crown and root lengths have its applications in surgical extrusion and crown lengthening in cases of tooth wear. Aim: To document the frequency of crown-root ratio (using CBCT images) in longitudinal section of the premolar teeth. Study design: Descriptive cross-sectional study. Place and duration of study: Department of Radiology, Khyber College of Dentistry and Hospital, Peshawar and Department of Radiology, Sardar Begum Dental College and Hospital Peshawar from 1st January 2020 to 30th June 2020. Methodology: One hundred and thirty five patients of both the genders were selected by obtaining there CBCT images. A written informed consent was taken from the patients or their attendants through concerned dentist who advised CBCT. Data of crown root ratio of premolar teeth were recorded in the form of categorical variables (crown and root length). Results: The mean age was 29.64±4.47 years. The highest frequency of patients was of 28(14.8%) years and 29(17%) years. There were 87(64.4%) males while 48(35.5%) females. Conclusion: There is significant difference of crown root ratio between the premolars of maxilla and mandible and the right and left quadrants of both arches. Key words: Cone beam computed tomography (CBCT), crown-to-root ratio (CRR), Premolar