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Fulltext Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects

Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, et al.

J Infect Public Health, 2020 Oct;13(10):1381-1396.
PMID: 32646771 DOI: 10.1016/j.jiph.2020.06.028

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Matched MeSH terms: Tomography, X-Ray Computed/classification*
Fulltext Value of Repeat CT Brain in Mild Traumatic Brain Injury Patients with High Risk of Intracerebral Hemorrhage Progression

Fadzil F, Mei AKC, Mohd Khairy A, Kumar R, Mohd Azli AN

Int J Environ Res Public Health, 2022 Nov 02;19(21).
PMID: 36361190 DOI: 10.3390/ijerph192114311

Patients with mild traumatic brain injury (MTBI) with intracerebral hemorrhage (ICH), particularly those at higher risk of having ICH progression, are typically prescribed a second head Computer Tomography (CT) scan to monitor the disease development. This study aimed to evaluate the role of a repeat head CT in MTBI patients at a higher risk of ICH progression by comparing the intervention rate between patients with and without ICH progression.
METHODS: 192 patients with MTBI and ICH were treated between November 2019 to December 2020 at a single level II trauma center. The Glasgow Coma Scale (GCS) was used to classify MTBI, and initial head CT was performed according to the Canadian CT head rule. Patients with a higher risk of ICH progression, including the elderly (≥65 years old), patients on antiplatelets or anticoagulants, or patients with an initial head CT that revealed EDH, contusional bleeding, or SDH > 5 mm, and multiple ICH underwent a repeat head CT within 12 to 24 h later. Data regarding types of intervention, length of stay in the hospital, and outcome were collected. The risk of further neurological deterioration and readmission rates were compared between these two groups. All patients were followed up in the clinic after one month or contacted via phone if they did not return.
RESULTS: 189 patients underwent scheduled repeated head CT, 18% had radiological intracranial bleed progression, and 82% had no changes. There were no statistically significant differences in terms of intervention rate, risk of neurological deterioration in the future, or readmission between them.
CONCLUSION: Repeat head CT in mild TBI patients with no neurological deterioration is not recommended, even in patients with a higher risk of ICH progression.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Clinical characteristics and computed tomographical features of pulmonary thromboembolic disease associated with COVID-19 infection: A tertiary hospital analysis

Tan TL, Illa NE, Ting SY, Hwong PL, Azmel A, Shunmugarajoo A, et al.

Med J Malaysia, 2023 Mar;78(2):155-162.
PMID: 36988524

INTRODUCTION: The co-existence of coronavirus disease 2019 (COVID-19) and pulmonary thromboembolic (PTE) disease poses a great clinical challenge. To date, few researches have addressed this important clinical issue among the South-East Asian populations. The objectives of this study were as follow: (1) to describe the clinical characteristics and computed tomographical (CT) features of patients with PTE disease associated with COVID-19 infection and (2) to compare these parameters with those COVID-19 patients without PTE disease.
MATERIALS AND METHODS: This cross-sectional study with retrospective record review was conducted in Hospital Tengku Ampuan Rahimah, Selangor, Malaysia. We included all hospitalised patients with confirmed COVID-19 infection who had undergone CT pulmonary angiogram (CTPA) examinations for suspected PTE disease between April 2021 and May 2021. Clinical data and laboratory data were extracted by trained data collectors, whilst CT images retrieved were analysed by a senior radiologist. Data analysis was performed using Statistical Package for the Social Sciences (SPSS) version 20.
RESULTS: We studied 184 COVID-19 patients who were suspected to have PTE disease. CTPA examinations revealed a total of 150 patients (81.5%) suffered from concomitant PTE disease. Among the PTE cohort, the commonest comorbidities were diabetes mellitus (n=78, 52.0%), hypertension (n=66, 44.0%) and dyslipidaemia (n=25, 16.7%). They were generally more ill than the non-PTE cohort as they reported a significantly higher COVID-19 disease category during CTPA examination with p=0.042. Expectedly, their length of both intensive care unit stays (median number of days 8 vs. 3; p=0.021) and hospital stays (median number of days 14.5 vs. 12; p=0.006) were significantly longer. Intriguingly, almost all the subjects had received either therapeutic anticoagulation or thromboprophylactic therapy prior to CTPA examination (n=173, 94.0%). Besides, laboratory data analysis identified a significantly higher peak C-reactive protein (median 124.1 vs. 82.1; p=0.027) and ferritin levels (median 1469 vs. 1229; p=0.024) among them. Evaluation of CT features showed that COVID-19 pneumonia pattern (p<0.001) and pulmonary angiopathy (p<0.001) were significantly more profound among the PTE cohort. To note, the most proximal pulmonary thrombosis was located in the segmental (n=3, 2.0%) and subsegmental pulmonary arteries (n=147, 98.0%). Also, the thrombosis predominantly occurred in bilateral lungs with multilobar involvement (n=95, 63.3%).
CONCLUSION: Overall, PTE disease remains prevalent among COVID-19 patients despite timely administration of thromboprophylactic therapy. The presence of hyperinflammatory activities, unique thrombotic locations as well as concurrent pulmonary parenchyma and vasculature aberrations in our PTE cohort implicate immunothrombosis as the principal mechanism of this novel phenomenon. We strongly recommend future researchers to elucidate this important clinical disease among our post- COVID vaccination populations.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Fulltext Lung Cancer Screening in Asia: An Expert Consensus Report

Lam DC, Liam CK, Andarini S, Park S, Tan DSW, Singh N, et al.

J Thorac Oncol, 2023 Oct;18(10):1303-1322.
PMID: 37390982 DOI: 10.1016/j.jtho.2023.06.014

INTRODUCTION: The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.
METHOD: A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.
RESULTS: Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.
CONCLUSIONS: Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Fulltext The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases

Wang W, Zhao X, Jia Y, Xu J

PLoS One, 2024;19(2):e0297578.
PMID: 38319912 DOI: 10.1371/journal.pone.0297578

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Matched MeSH terms: Tomography, X-Ray Computed/methods
The potential role of artificial intelligence-assisted chest X-ray imaging in detecting early-stage lung cancer in the community-a proposed algorithm for lung cancer screening in Malaysia

Sachithanandan A, Lockman H, Azman RR, Tho LM, Ban EZ, Ramon V

Med J Malaysia, 2024 Jan;79(1):9-14.
PMID: 38287751

INTRODUCTION: The poor prognosis of lung cancer has been largely attributed to the fact that most patients present with advanced stage disease. Although low dose computed tomography (LDCT) is presently considered the optimal imaging modality for lung cancer screening, its use has been hampered by cost and accessibility. One possible approach to facilitate lung cancer screening is to implement a risk-stratification step with chest radiography, given its ease of access and affordability. Furthermore, implementation of artificial-intelligence (AI) in chest radiography is expected to improve the detection of indeterminate pulmonary nodules, which may represent early lung cancer.
MATERIALS AND METHODS: This consensus statement was formulated by a panel of five experts of primary care and specialist doctors. A lung cancer screening algorithm was proposed for implementation locally.
RESULTS: In an earlier pilot project collaboration, AI-assisted chest radiography had been incorporated into lung cancer screening in the community. Preliminary experience in the pilot project suggests that the system is easy to use, affordable and scalable. Drawing from experience with the pilot project, a standardised lung cancer screening algorithm using AI in Malaysia was proposed. Requirements for such a screening programme, expected outcomes and limitations of AI-assisted chest radiography were also discussed.
CONCLUSION: The combined strategy of AI-assisted chest radiography and complementary LDCT imaging has great potential in detecting early-stage lung cancer in a timely manner, and irrespective of risk status. The proposed screening algorithm provides a guide for clinicians in Malaysia to participate in screening efforts.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Fulltext Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R

Sci Rep, 2021 Jul 08;11(1):14133.
PMID: 34238992 DOI: 10.1038/s41598-021-93658-y

COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

Matched MeSH terms: Tomography, X-Ray Computed/methods*
Fulltext GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R

Sci Rep, 2021 04 15;11(1):8304.
PMID: 33859222 DOI: 10.1038/s41598-021-87523-1

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .

Matched MeSH terms: Tomography, X-Ray Computed/methods*
Fulltext A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH

Tomography, 2023 Dec 05;9(6):2158-2189.
PMID: 38133073 DOI: 10.3390/tomography9060169

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Matched MeSH terms: Tomography, X-Ray Computed/methods
Fulltext Isolated sphenoid sinus lesion: A diagnostic dilemma

Alazzawi S, Shahrizal T, Prepageran N, Pailoor J

Qatar Med J, 2014;2014(1):57-60.
PMID: 25320694 DOI: 10.5339/qmj.2014.10

Isolated sphenoid sinus lesions are an uncommon entity and present with non-specific symptoms. In this case report, the patient presented with a history of headaches for a duration of one month without sinonasal symptoms. A computed tomography scan showed a soft tissue mass occupying the sphenoid sinus. An endoscopic biopsy revealed fungal infection. Endoscopic wide sphenoidotomy with excision of the sphenoid sinus lesion was then performed however, the microbiological examination post-surgery did not show any fungal elements. Instead, Citrobacter species was implicated to be the cause of infection.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Computed Tomography (CT) of Blunt Abdominal Trauma: The Frequency of Incidental Findings, how it was Documented in Radiology Report and the Implication of These Findings to Acute Trauma Care

Radhiana H, Siti Kamariah CM, Mohd Nazli K, Azian AA

Med J Malaysia, 2014 Feb;69(1):46-8.
PMID: 24814633 MyJurnal

The wide use of computed tomography (CT) scanning for patients with blunt abdominal trauma can reveal incidental findings that vary in their importance. We evaluated these findings, how it was reported by radiologists and its implication on the trauma care. In 30 out of 154 patients, 32 incidental findings were discovered (19.5%). Out of these 32 findings, only 3 cases (9.4%) were considered significant and required immediate attention from the managing team. In all these 3 cases, the findings were described in the body of the report and highlighted in the conclusion section at the end of the radiology report. However, similar reporting style was used in only 58.4% of cases with moderate clinical concern and 23.5% of cases with little clinical concern. In 41.2% of cases with little concern, the incidental findings were not mentioned in the radiology report. In conclusion, incidental findings in CT scan performed for blunt abdominal trauma were common but many were clinically insignificant. There is little consistency in radiology reporting of these findings especially those with moderate and little clinical concern.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Papillary thyroid carcinoma presenting with intraluminal tracheal mass symptoms

Mohamad I, Haron A

Med J Malaysia, 2013 Apr;68(2):164-5.
PMID: 23629566 MyJurnal

Papillary thyroid carcinoma is a common thyroid malignancy reported world wide. It affects females more commonly in the 4th to 6th decades of life. The patients usually present with a painless anterior neck mass and occasionally with lymph node involvement. We report a case of an elderly male who presented with hoarseness and hemoptysis, which warranted bronchoscopy. Biopsy of the intraluminal tracheal mass revealed the diagnosis of papillary thyroid carcinoma. Computed tomography scan of the neck confirmed the presence of the primary lesion in the right thyroid lobe with invasion into the adjacent trachea and esophagus.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext A rare case of an appendiceal mass masquerading as a pelvic tumour and causing bilateral hydronephrosis

Abdul Rashid S, Ab Hamid S, Mohamad Saini S, Muridan R

Biomed Imaging Interv J, 2012 Apr;8(2):e11.
PMID: 22970067 MyJurnal DOI: 10.2349/biij.8.2.e11

Diagnosing acute appendicitis in children can be difficult due to atypical presenting symptoms. While there are reported cases of acute appendicitis or appendiceal masses causing unilateral hydronephrosis, bilateral hydronephrosis as a complication of appendiceal mass is very rare. We report a case of a child who presented with cardinal symptomatology associated with the urogenital tract. Ultrasound (US) investigation showed a pelvic mass causing bilateral hydronephrosis. An initial diagnosis of a pelvic teratoma was made based on the US and computed tomography (CT) scan findings. The final diagnosis of an appendiceal mass causing bilateral hydronephrosis was established intraoperatively.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Pelvic aneurysmal bone cyst

Sharifah M, Nurhazla H, Suraya A, Tan S

Biomed Imaging Interv J, 2011 Oct;7(4):e24.
PMID: 22279501 MyJurnal DOI: 10.2349/biij.7.4.24

This paper describes an extremely rare case of a huge aneurysmal bone cyst (ABC) in the pelvis, occurring in the patient's 5(th) decade of life. The patient presented with a history of painless huge pelvic mass for 10 years. Plain radiograph and computed tomography showed huge expansile lytic lesion arising from the right iliac bone. A biopsy was performed and histology confirmed diagnosis of aneurysmal bone cyst. Unfortunately, the patient succumbed to profuse bleeding from the tumour.

Matched MeSH terms: Tomography, X-Ray Computed
A missed bilateral choanal atresia

Irfan M, Suzina SA

Ann Acad Med Singap, 2010 Jan;39(1):72.
PMID: 20126823
Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Electrodynamics sensor for the image reconstruction process in an electrical charge tomography system

Rahmat MF, Isa MD, Rahim RA, Hussin TA

Sensors (Basel), 2009;9(12):10291-308.
PMID: 22303174 DOI: 10.3390/s91210291

Electrical charge tomography (EChT) is a non-invasive imaging technique that is aimed to reconstruct the image of materials being conveyed based on data measured by an electrodynamics sensor installed around the pipe. Image reconstruction in electrical charge tomography is vital and has not been widely studied before. Three methods have been introduced before, namely the linear back projection method, the filtered back projection method and the least square method. These methods normally face ill-posed problems and their solutions are unstable and inaccurate. In order to ensure the stability and accuracy, a special solution should be applied to obtain a meaningful image reconstruction result. In this paper, a new image reconstruction method - Least squares with regularization (LSR) will be introduced to reconstruct the image of material in a gravity mode conveyor pipeline for electrical charge tomography. Numerical analysis results based on simulation data indicated that this algorithm efficiently overcomes the numerical instability. The results show that the accuracy of the reconstruction images obtained using the proposed algorithm was enhanced and similar to the image captured by a CCD Camera. As a result, an efficient method for electrical charge tomography image reconstruction has been introduced.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Degenerated uterine fibroid mimicking hydrometra: fallacy in CT

Tok Ch, Bux S, Mohamed S, Lim B

Biomed Imaging Interv J, 2006 Oct;2(4):e42.
PMID: 21614328 MyJurnal DOI: 10.2349/biij.2.4.e42

Fibroids are the commonest uterine neoplasms, occurring in 20% - 30% of women of reproductive age. In women who have pelvic masses of unknown cause, unusual manifestations of fibroids such as necrosis or degeneration may simulate a carcinoma or hydrometra resulting in problems with image interpretation. We report a case of an unsuspected large degenerated uterine fibroid in a lady mistakenly diagnosed as hydrometra on computed tomography scanning.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Alive with almost no Brain: Severe Post-Hemorrhagic Internal Hydrocephalus

Finsterer J, Rettensteiner J, Stellamor V, Stöphasius E

Med J Malaysia, 2013;68(1):86-7.
PMID: 23466778

Severe post-hemorrhaghic internal hydrocephalus with almost complete atrophy of the cerebral parenchyma, as in the following case, is rare.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Phytobezoar: an unusual cause of intestinal obstruction

Teng H, Nawawi O, Ng K, Yik Y

Biomed Imaging Interv J, 2005 Jul;1(1):e4.
PMID: 21625276 MyJurnal DOI: 10.2349/biij.1.1.e4

Small bowel phytobezoars are rare and almost always obstructive. There have been previously reported cases of phytobezoars in the literature, however there are few reports on radiological findings for small bowel bezoars. Barium studies characteristically show an intraluminal filling defect of variable size that is not fixed to the bowel wall with barium filling the interstices giving a mottled appearance. On CT scan, the presence of a round or ovoid intraluminal mass with a 'mottled gas' pattern is believed to be pathognomonic. Since features on CT scans are characteristics and physical findings are of little assistance in the diagnosis of bezoar, the diagnostic value of CT needs to be emphasised.

Matched MeSH terms: Tomography, X-Ray Computed
Fulltext Monte Carlo dose calculation in dental amalgam phantom

Aziz MZ, Yusoff AL, Osman ND, Abdullah R, Rabaie NA, Salikin MS

J Med Phys, 2015 Jul-Sep;40(3):150-5.
PMID: 26500401 DOI: 10.4103/0971-6203.165080

It has become a great challenge in the modern radiation treatment to ensure the accuracy of treatment delivery in electron beam therapy. Tissue inhomogeneity has become one of the factors for accurate dose calculation, and this requires complex algorithm calculation like Monte Carlo (MC). On the other hand, computed tomography (CT) images used in treatment planning system need to be trustful as they are the input in radiotherapy treatment. However, with the presence of metal amalgam in treatment volume, the CT images input showed prominent streak artefact, thus, contributed sources of error. Hence, metal amalgam phantom often creates streak artifacts, which cause an error in the dose calculation. Thus, a streak artifact reduction technique was applied to correct the images, and as a result, better images were observed in terms of structure delineation and density assigning. Furthermore, the amalgam density data were corrected to provide amalgam voxel with accurate density value. As for the errors of dose uncertainties due to metal amalgam, they were reduced from 46% to as low as 2% at d80 (depth of the 80% dose beyond Zmax) using the presented strategies. Considering the number of vital and radiosensitive organs in the head and the neck regions, this correction strategy is suggested in reducing calculation uncertainties through MC calculation.

Matched MeSH terms: Tomography, X-Ray Computed

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