WE-E-330D-01: The Production of Ultrafast Bright K-Alpha X-Rays From Laser Produced Plasmas for Medical Imaging

2006 ◽  
Vol 33 (6Part20) ◽  
pp. 2251-2251 ◽  
Author(s):  
J Rassuchine ◽  
G Dyer ◽  
B Cho ◽  
Y Sentoku ◽  
T Cowan ◽  
...  
Keyword(s):  
Medical Imaging ◽  
X Rays

Electrical Impedance Tomography (EIT)

2014 ◽  
pp. 235-273 ◽  
Author(s):  
Tushar Kanti Bera ◽  
J. Nagaraju
Keyword(s):  
Medical Imaging ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Optical Tomography ◽  
Body Part ◽  
Imaging Modalities ◽  
X Rays ◽  
Science And Engineering ◽  
Impedance Tomography ◽  
Anatomy And Physiology

Looking into the human body is very essential not only for studying the anatomy and physiology, but also for diagnosing a disease or illness. Doctors always try to visualize an organ or body part in order to study its physiological and anatomical status for understanding and/or treating its illness. This necessity introduced the diagnostic tool called medical imaging. The era of medical imaging started in 1895, when Roentgen discovered the magical powerful invisible rays called X-rays. Gradually the medical imaging introduced X-Ray CT, Gamma Camera, PET, SPECT, MRI, USG. Recently medical imaging field is enriched with comparatively newer tomographic imaging modalities like Electrical Impedance Tomography (EIT), Diffuse Optical Tomography (DOT), Optical Coherence Tomography (OCT), and Photoacaustic Tomography (PAT). The EIT has been extensively researched in different fields of science and engineering due to its several advantages. This chapter will present a brief review on the available medical imaging modalities and focus on the need of an alternating method. EIT will be discussed with its physical and mathematical aspects, potentials, and challenges.

scholarly journals Analyse Du Rebut Des Cliches Radiographiques Dans Le Service De Radiologie Et Imagerie Médicale Du Chu Campus De Lomé Au Togo

2017 ◽  
Vol 13 (33) ◽  
pp. 244
Author(s):  
P. Gbande ◽  
L. Sonhaye ◽  
K. Adambounou ◽  
K. Lambon ◽  
B. N’timon ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Prospective Study ◽  
Lower Limb ◽  
University Hospital ◽  
X Rays ◽  
Financial Loss ◽  
X Ray ◽  
Chest X Ray ◽  
Almost All

Purpose: To analyze the waste factors of rejected X-rays films. Methodology: Descriptive and analytical prospective study from 1 January to 30 June 2017 carried out in the department of radiology and medical imaging of the Campus University Hospital of Lomé in Togo. Results: 4912 patients had received 5630 radiographic incidences, including 3288 (58.4%) on the analogy and 2342 (41.5%) on the digital. The reject rate was 12.5%. The vast majority of the X-rays films, 682 (96.9%) were rejected by the radiographers themselves just after development. The resumption frequency ranged from one repeat (550 X-rays films, or 78%) to 4 repeats (8 X-rays films, or 1%). Almost all of the rejected films, 702 (99.7%) came from the analogical room. Chest X-ray was the incidence with more rejection in 33.9% followed by pelvic and lower limb incidences in 21% of cases. More than 2/3 of the rejected films, 473 (67.2%), came from the students' act. The causes of the rejection were mainly centering (25.5%), underexposure (20.17%) and overexposure (12.93). The financial loss caused by the scrap of X-rays films amounted to about 418800F CFA or 638.5 €. Conclusion: Strengthening communication between radiographers and radiologists is necessary to avoid unnecessary repeats of patient’s radiographs.

scholarly journals RadImageNet: A Large-scale Radiologic Dataset for Enhancing Deep Learning Transfer Learning Research

2021 ◽  
Author(s):  
Yang Yang ◽  
Xueyan Mei ◽  
Philip Robson ◽  
Brett Marinelli ◽  
Mingqian Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Transfer Learning ◽  
Large Scale ◽  
Medical Images ◽  
Bone Age ◽  
Natural World ◽  
X Rays ◽  
Starting Point ◽  
Size Diversity

Abstract Most current medical imaging Artificial Intelligence (AI) relies upon transfer learning using convolutional neural networks (CNNs) created using ImageNet, a large database of natural world images, including cats, dogs, and vehicles. Size, diversity, and similarity of the source data determine the success of the transfer learning on the target data. ImageNet is large and diverse, but there is a significant dissimilarity between its natural world images and medical images, leading Cheplygina to pose the question, “Why do we still use images of cats to help Artificial Intelligence interpret CAT scans?”. We present an equally large and diversified database, RadImageNet, consisting of 5 million annotated medical images consisting of CT, MRI, and ultrasound of musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, and pulmonary pathologies over 450,000 patients. The database is unprecedented in scale and breadth in the medical imaging field, constituting a more appropriate basis for medical imaging transfer learning applications. We found that RadImageNet transfer learning outperformed ImageNet in multiple independent applications, including improvements for bone age prediction from hand and wrist x-rays by 1.75 months (p<0.0001), pneumonia detection in ICU chest x-rays by 0.85% (p<0.0001), ACL tear detection on MRI by 10.72% (p<0.0001), SARS-CoV-2 detection on chest CT by 0.25% (p<0.0001) and hemorrhage detection on head CT by 0.13% (p<0.0001). The results indicate that our pre-trained models that are open-sourced on public domains will be a better starting point for transfer learning in radiologic imaging AI applications, including applications involving medical imaging modalities or anatomies not included in the RadImageNet database.

Electrical Impedance Tomography (EIT)

2017 ◽  
pp. 71-114 ◽  
Author(s):  
Tushar Kanti Bera ◽  
J. Nagaraju
Keyword(s):  
Medical Imaging ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Optical Tomography ◽  
Body Part ◽  
Imaging Modalities ◽  
X Rays ◽  
Science And Engineering ◽  
Impedance Tomography ◽  
Anatomy And Physiology

Looking into the human body is very essential not only for studying the anatomy and physiology, but also for diagnosing a disease or illness. Doctors always try to visualize an organ or body part in order to study its physiological and anatomical status for understanding and/or treating its illness. This necessity introduced the diagnostic tool called medical imaging. The era of medical imaging started in 1895, when Roentgen discovered the magical powerful invisible rays called X-rays. Gradually the medical imaging introduced X-Ray CT, Gamma Camera, PET, SPECT, MRI, USG. Recently medical imaging field is enriched with comparatively newer tomographic imaging modalities like Electrical Impedance Tomography (EIT), Diffuse Optical Tomography (DOT), Optical Coherence Tomography (OCT), and Photoacaustic Tomography (PAT). The EIT has been extensively researched in different fields of science and engineering due to its several advantages. This chapter will present a brief review on the available medical imaging modalities and focus on the need of an alternating method. EIT will be discussed with its physical and mathematical aspects, potentials, and challenges.

Correction to "Status of Direct Conversion Detectors for Medical Imaging With X-Rays" [Aug 09 1800-1809]

2010 ◽  
Vol 57 (5) ◽  
pp. 2995-2995 ◽  
Author(s):  
Michael Overdick ◽  
Christian Bäumer ◽  
Klaus Jürgen Engel ◽  
Johannes Fink ◽  
Christoph Hermann ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Direct Conversion ◽  
X Rays

scholarly journals Medical Images Segmentation Based on Unsupervised Algorithms: A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 71-80
Author(s):  
Revella E. A. Armya Armya ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Automatic Process ◽  
X Rays ◽  
Resonance Imaging ◽  
Unsupervised Algorithms

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

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