scholarly journals SlicerITKUltrasound: A 3D Slicer extension for scan conversion of B-mode and next-generation ultrasound imaging modalities

2017 ◽  
Vol 2 (10) ◽  
pp. 153
Author(s):  
Matthew M McCormick ◽  
Mark L Palmeri ◽  
Jean-Christophe Fillion-Robin ◽  
Stephen Aylward
Keyword(s):  
Ultrasound Imaging ◽  
Imaging Modalities ◽  
Next Generation ◽  
3D Slicer ◽  
Scan Conversion

Biliary papillomatosis and new ultrasound imaging modalities

2012 ◽  
Vol 25 (01) ◽  
Author(s):  
XW Cui ◽  
A Ignee ◽  
B Braden ◽  
M Woenckhaus ◽  
CF Dietrich
Keyword(s):  
Ultrasound Imaging ◽  
Imaging Modalities ◽  
Biliary Papillomatosis

scholarly journals Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2629
Author(s):  
Kunkyu Lee ◽  
Min Kim ◽  
Changhyun Lim ◽  
Tai-Kyong Song
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Mobile Device ◽  
Network Architecture ◽  
Point Of Care ◽  
Ultrasound Image ◽  
Training Dataset ◽  
Point Of Care Ultrasound ◽  
Reverse Scan ◽  
Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

scholarly journals Diagnosis of Parathyroid Adenomas with New Ultrasound Imaging Modalities

2019 ◽  
Vol 6 (4) ◽  
Author(s):  
Ghobad Azizi ◽  
Michelle L. Mayo ◽  
James Keller ◽  
Jessica Farrell ◽  
Carl Malchoff
Keyword(s):  
Ultrasound Imaging ◽  
Imaging Modalities ◽  
Parathyroid Adenomas

scholarly journals Ultrasound and non-ultrasound imaging techniques in the assessment of diaphragmatic dysfunction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Franco A. Laghi ◽  
Marina Saad ◽  
Hameeda Shaikh
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Ultrasound Imaging ◽  
Imaging Techniques ◽  
Dynamic Imaging ◽  
Diaphragm Muscle ◽  
Imaging Modalities ◽  
Muscle Dysfunction ◽  
Resonance Imaging ◽  
Diaphragm Dysfunction

AbstractDiaphragm muscle dysfunction is increasingly recognized as an important element of several diseases including neuromuscular disease, chronic obstructive pulmonary disease and diaphragm dysfunction in critically ill patients. Functional evaluation of the diaphragm is challenging. Use of volitional maneuvers to test the diaphragm can be limited by patient effort. Non-volitional tests such as those using neuromuscular stimulation are technically complex, since the muscle itself is relatively inaccessible. As such, there is a growing interest in using imaging techniques to characterize diaphragm muscle dysfunction. Selecting the appropriate imaging technique for a given clinical scenario is a critical step in the evaluation of patients suspected of having diaphragm dysfunction. In this review, we aim to present a detailed analysis of evidence for the use of ultrasound and non-ultrasound imaging techniques in the assessment of diaphragm dysfunction. We highlight the utility of the qualitative information gathered by ultrasound imaging as a means to assess integrity, excursion, thickness, and thickening of the diaphragm. In contrast, quantitative ultrasound analysis of the diaphragm is marred by inherent limitations of this technique, and we provide a detailed examination of these limitations. We evaluate non-ultrasound imaging modalities that apply static techniques (chest radiograph, computerized tomography and magnetic resonance imaging), used to assess muscle position, shape and dimension. We also evaluate non-ultrasound imaging modalities that apply dynamic imaging (fluoroscopy and dynamic magnetic resonance imaging) to assess diaphragm motion. Finally, we critically review the application of each of these techniques in the clinical setting when diaphragm dysfunction is suspected.

A Novel Framework of Speckle Reducing Scan Conversion in Ultrasound Imaging Systems

2017 ◽  
Vol 35 (6) ◽  
pp. 618-630 ◽  
Author(s):  
Debashis Nandi ◽  
Sudipta Mukhopadhyay ◽  
Dipannita Ghosh ◽  
Baisakhi Chakroborty
Keyword(s):  
Ultrasound Imaging ◽  
Imaging Systems ◽  
Scan Conversion
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