• Title/Summary/Keyword: Diagnostic imaging

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Oroantral communication, its causes, complications, treatments and radiographic features: A pictorial review

  • Shahrour, Rama;Shah, Priya;Withana, Thimanthi;Jung, Jennifer;Syed, Ali Z
    • Imaging Science in Dentistry
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    • v.51 no.3
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    • pp.307-311
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    • 2021
  • Purpose: An oroantral communication (OAC) is an abnormal space between the maxillary sinus and oral cavity. The causes, complications, treatment, and radiographic features of OAC in 2-dimensional and 3-dimensional imaging modalities are discussed. Materials and Methods: This pictorial review presents a broad spectrum of imaging findings of OAC. Representative radiographs depicting OAC were chosen from our database. PubMed was used to conduct a comprehensive literature search of OAC. Results: Characteristic features of OAC include discontinuity of the maxillary sinus floor, thickening of the maxillary sinus mucosa, or a combination of both. Two-dimensional imaging modalities are the method of choice for identifying discontinuities in the maxillary sinus floor. However, 3-dimensional imaging modalities are also essential for determining the status of soft tissue in the maxillary sinus. Conclusion: The integration of 2-dimensional and 3-dimensional imaging modalities is crucial for the correct diagnosis and comprehensive treatment of OAC. However, the diagnosis of OAC must be confirmed clinically to prevent unnecessary mental and financial burdens to patients.

Feasibility of Three-Dimensional Balanced Steady-State Free Precession Cine Magnetic Resonance Imaging Combined with an Image Denoising Technique to Evaluate Cardiac Function in Children with Repaired Tetralogy of Fallot

  • YaFeng Peng;XinYu Su;LiWei Hu;Qian Wang;RongZhen Ouyang;AiMin Sun;Chen Guo;XiaoFen Yao;Yong Zhang;LiJia Wang;YuMin Zhong
    • Korean Journal of Radiology
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    • v.22 no.9
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    • pp.1525-1536
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    • 2021
  • Objective: To investigate the feasibility of cine three-dimensional (3D) balanced steady-state free precession (b-SSFP) imaging combined with a non-local means (NLM) algorithm for image denoising in evaluating cardiac function in children with repaired tetralogy of Fallot (rTOF). Materials and Methods: Thirty-five patients with rTOF (mean age, 12 years; range, 7-18 years) were enrolled to undergo cardiac cine image acquisition, including two-dimensional (2D) b-SSFP, 3D b-SSFP, and 3D b-SSFP combined with NLM. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) of the two ventricles were measured and indexed by body surface index. Acquisition time and image quality were recorded and compared among the three imaging sequences. Results: 3D b-SSFP with denoising vs. 2D b-SSFP had high correlation coefficients for EDV, ESV, SV, and EF of the left (0.959-0.991; p < 0.001) as well as right (0.755-0.965; p < 0.001) ventricular metrics. The image acquisition time ± standard deviation (SD) was 25.1 ± 2.4 seconds for 3D b-SSFP compared with 277.6 ± 0.7 seconds for 2D b-SSFP, indicating a significantly shorter time with the 3D than the 2D sequence (p < 0.001). Image quality score was better with 3D b-SSFP combined with denoising than with 3D b-SSFP (mean ± SD, 3.8 ± 0.6 vs. 3.5 ± 0.6; p = 0.005). Signal-to-noise ratios for blood and myocardium as well as contrast between blood and myocardium were higher for 3D b-SSFP combined with denoising than for 3D b-SSFP (p < 0.05 for all but septal myocardium). Conclusion: The 3D b-SSFP sequence can significantly reduce acquisition time compared to the 2D b-SSFP sequence for cine imaging in the evaluation of ventricular function in children with rTOF, and its quality can be further improved by combining it with an NLM denoising method.

The Comparison of Appropriateness of Abdominal Computed Tomography (CT) and Abdominal Radiography Imaging Modality for Patients with Acute Nontraumatic Abdominal Pain (비외상성 급성 복부 통증 환자에게 시행한 복부 전산화단층촬 영과 복부 단순 촬영의 적정성 비교)

  • Song, Jung-Hup;Ryeom, Hun-Kyu
    • Quality Improvement in Health Care
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    • v.24 no.2
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    • pp.15-25
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    • 2018
  • Purpose: To compare the Appropriateness of abdominal CT to abdominal radiography as an imaging modality in terms of the diagnostic value, medical costs and decision making times for patients presented to the emergency department with nontraumatic abdominal pain. Methods: This study used the records of 530 cases presented to the emergency department(ED) with nontraumatic abdominal pain from February to March 2012. Imaging modalities were categorized into abdominal radiography and CT (radiography first or CT first) or radiography alone or CT alone. The diagnostic value, total medical costs and effect on decision making time of the each imaging modalities were compared. Especially, in retrospective review, to evaluate the predictability of the abdominal radiography, alit was assumed that all the 530 cases performed that exam as initial imaging. Results: Among 530 cases, 255 cases underwent abdominal radiography only, 28 cases underwent abdominal CT only and the remnant 247 cases underwent abdominal CT with plain abdominal radiography. The diagnostic value was higher in the cases with abdominal CT (268/275, 97.5%) than in the cases with plain abdominal radiography (19/255, 7.5%).The number of cases predicted by abdominal radiography only as initial imaging were 39/530 (7.4%). In cases where the patients performed the abdominal CT as the first imaging modality thereby omitting the abdominal radiography, the total diagnostic imaging fee was lower than in cases with plain abdominal radiography first followed by the abdominal CT (277,140 vs. 284,226(mean, Korean Won)). Although diagnostic value of the plain abdominal radiography as first imaging modality was lower than the abdominal CT, Decision making time, average duration of hospital stay was longer and the total medical costs was higher than abdominal CT. Conclusion: As an imaging modality in the ED for patients with acute nontraumatic abdominal pain, plain abdominal radiography is an avoidable procedure when viewed in terms of the diagnostic value and total medical costs and decision making times comparing with abdominal CT.

Cone-beam computed tomographic imaging of silent sinus syndrome: A case series and a literature review

  • Manila, Nisha G.;Arashlow, Mehrnaz Tahmasbi;Ehlers, Scott;Liang, Hui;Nair, Madhu K.
    • Imaging Science in Dentistry
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    • v.50 no.4
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    • pp.365-371
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    • 2020
  • While silent sinus syndrome (SSS) is familiar to otolaryngologists and ophthalmologists, it is a rare clinical entity in dentistry and is likely to be underdiagnosed due to dentists' lack of awareness of this condition. SSS presents a diagnostic challenge to dentists, as patients typically have no history of trauma or sinusitis. The characteristic feature of SSS is a gradual retreat of the maxillary sinus walls, resulting in enophthalmos and hypoglobus. Multidetector (multislice) computed tomography is the imaging modality of choice for SSS and other paranasal sinus diseases. Cone-beam computed tomography promises to be an alternative low-dose imaging modality. This report describes 3 cases of SSS in adults, who had no identified clinical symptoms except diminutive and opacified maxillary sinuses, as well as the inward bowing of the sinus walls as noted on cone-beam computed tomographic imaging.

A Study on the Analysis of Area for the Planning of Diagnostic Imaging Department (영상의학부 공간계획을 위한 면적분석에 관한 연구)

  • Youn, Woo-Young;Chai, Choul-Gyun
    • Journal of The Korea Institute of Healthcare Architecture
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    • v.12 no.1
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    • pp.61-69
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    • 2006
  • The Diagnostic Imagining Department essentially needs to be transformed by the plan of the room and the medical equipment which should be improved according to a rapid development in technology. And the room should be considered the scale and composition an the time of planning. Because this part is often influenced in a specific character of imaging equipment in the room. The researches on the scale and composition of Diagnostic Imaging Department were the main part in 1980's but after 1990's this kind of researches have not been enough. So this study has an intention of proposing basic data which is used in planning the Diagnostic Imaging Department by analyzing the actual condition of the area organization in general hospital.

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Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

  • Subhanik Purkayastha;Yanhe Xiao;Zhicheng Jiao;Rujapa Thepumnoeysuk;Kasey Halsey;Jing Wu;Thi My Linh Tran;Ben Hsieh;Ji Whae Choi;Dongcui Wang;Martin Vallieres;Robin Wang;Scott Collins;Xue Feng;Michael Feldman;Paul J. Zhang;Michael Atalay;Ronnie Sebro;Li Yang;Yong Fan;Wei-hua Liao;Harrison X. Bai
    • Korean Journal of Radiology
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    • v.22 no.7
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    • pp.1213-1224
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    • 2021
  • Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.