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Jie Yuan

Bio: Jie Yuan is an academic researcher from Nanjing University. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 12, co-authored 69 publications receiving 667 citations. Previous affiliations of Jie Yuan include Tongji University & University of Michigan.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This paper proposes to use convolutional neural networks (CNNs) for segmenting breast ultrasound images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three‐dimensional (3D) Breast ultrasound images.

151 citations

Journal ArticleDOI
TL;DR: The development of LED-based PA imaging integrated with B-mode ultrasound, which could be a promising tool for several clinical applications, such as assessment of peripheral microvascular function and dynamic changes, diagnosis of inflammatory arthritis, and detection of head and neck cancer.
Abstract: Using low cost and small size light emitting diodes (LED) as the alternative illumination source for photoacoustic (PA) imaging has many advantages, and can largely benefit the clinical translation of the emerging PA imaging technology. Here, we present our development of LED-based PA imaging integrated with B-mode ultrasound. To overcome the challenge of achieving sufficient signal-to-noise ratio by the LED light that is orders of magnitude weaker than lasers, extensive signal averaging over hundreds of pulses is performed. Facilitated by the fast response of the LED and the high-speed driving as well as the high pulse repetition rate up to 16 kHz, B-mode PA images superimposed on gray-scale ultrasound of a biological sample can be achieved in real-time with frame rate up to 500 Hz. The LED-based PA imaging could be a promising tool for several clinical applications, such as assessment of peripheral microvascular function and dynamic changes, diagnosis of inflammatory arthritis, and detection of head and neck cancer.

114 citations

Journal ArticleDOI
TL;DR: The results supported the hypothesis that the PASA allows quantitative identification of the microstructural changes that differentiate normal from fatty livers and compared with that at 532 nm, P ASA at 1200 nm is more reliable for fatty liver diagnosis.
Abstract: Photoacoustic spectrum analysis at either 1200 nm or 532 nm can allow differentiation of fatty from normal liver tissue in a mouse model by showing the microstructural changes associated with lipid and hemoglobin in the liver tissue.

91 citations

Journal ArticleDOI
TL;DR: A fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction, and display for both photoacoustic (PA) and US imaging all in a truly real-time manner, is reported.
Abstract: Photoacoustic tomography (PAT) offers structural and functional imaging of living biological tissue with highly sensitive optical absorption contrast and excellent spatial resolution comparable to medical ultrasound (US) imaging. We report the development of a fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction, and display for both photoacoustic (PA) and US imaging all in a truly real-time manner. The back-projection (BP) algorithm for PA image reconstruction is optimized to reduce the computational cost and facilitate parallel computation on a state of the art graphics processing unit (GPU) card. For the first time, PAT and US imaging of the same object can be conducted simultaneously and continuously, at a real-time frame rate, presently limited by the laser repetition rate of 10 Hz. Noninvasive PAT and US imaging of human peripheral joints in vivo were achieved, demonstrating the satisfactory image quality realized with this system. Another experiment, simultaneous PAT and US imaging of contrast agent flowing through an artificial vessel, was conducted to verify the performance of this system for imaging fast biological events. The GPU-based image reconstruction software code for this dual-modality system is open source and available for download from http://sourceforge.net/projects/patrealtime.

65 citations

Journal ArticleDOI
TL;DR: An automated algorithm to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer is proposed.

61 citations


Cited by
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Journal Article
01 Jan 2008-Physics
TL;DR: In this paper, the authors provide an overview of the rapidly developing field of photoacoustic imaging, which is a promising method for visualizing biological tissues with optical absorbers, compared with optical imaging and ultrasonic imaging.
Abstract: Photoacoustic imaging is a promising method for visualizing biological tissues with optical absorbers. This article provides an overview of the rapidly developing field of photoacoustic imaging. Photoacoustics, the physical basis of photoacoustic imaging, is analyzed briefly. The merits of photoacoustic technology, compared with optical imaging and ultrasonic imaging, are described. Various imaging techniques are also discussed, including scanning tomography, computed tomography and original detection of photoacoustic imaging. Finally, some biomedical applications of photoacoustic imaging are summarized.

618 citations

Journal ArticleDOI
TL;DR: Examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology and the future impact and natural extension of these techniques in radiology practice are discussed.
Abstract: Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018

501 citations

Journal ArticleDOI
TL;DR: This review provides an overview of the rapidly expanding clinical applications of photoacoustic imaging including breast imaging, dermatologic imaging, vascular imaging, carotid artery imaging, musculoskeletal imaging, gastrointestinal imaging and adipose tissue imaging and the future directives utilizing different configurations of photoACoustic imaging.

416 citations

Journal ArticleDOI
15 Jul 2016
TL;DR: The conclusion of the current study was that the frequency of screening might be dependent on breast density and in such cases diagnostic techniques such as “digital mammography, ultra sonography and magnetic resonance imaging” may prove to be better detection tools.
Abstract: With the increase in breast cancer risk over the years, there are many factors estimated that lead to it. However, till date which factor is majorly involved in development of breast cancer or which factor accounts more is not clearly evident. Mammography technique accounting for 80-90% of cancer being detected is believed to be the best method of detection. While mammographic density is manifested by increased proliferation of fat, stoma, epithelium and connective tissue, it is considered to be a risk factor for development of breast cancer. The current study was thus conducted to find out whether the mammographic density is actually a risk factor for development of breast cancer and to find out the better detection tool available. For this, the methodology adopted was review of various journals and studies already published with respect to mammographic density and its risk on development of breast cancer. The conclusion of the current study as well as from another comparable study was that the frequency of screening might be dependent on breast density and in such cases diagnostic techniques such as “digital mammography, ultra sonography and magnetic resonance imaging” may prove to be better detection tools. Moreover, recent studies have also suggested that mammographic density as a marker for risk of developing breast cancer holds true however, this fact needs to be evaluated further. Article DOI: https://dx.doi.org/10.20319/lijhls.2016.22.4854 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

317 citations

Journal ArticleDOI
TL;DR: Qualitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound, and successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics are demonstrated.
Abstract: Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

249 citations