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.

Photoacoustic imaging in biomedicine

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

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018171820

Current Applications and Future Impact of Machine Learning in Radiology.

A Review of Clinical Photoacoustic Imaging: Current and Future Trends

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.

Mammographic density and the risk and detection of breast cancer

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.

/pdf/review-of-quantitative-ultrasound-envelope-statistics-and-38wfwoxzlm.pdf

Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound

Sound velocity measurement is of great importance to the application of biomedical especially in the research of acoustic detection and acoustic tomography. Using correct sound velocities in each medium other than one unified sound propagation speed, we can effectively enhance sound based imaging resolution. Photoacoustic tomography (PAT), is defined as cross-sectional or three-dimensional (3D) imaging of a material based on the photoacoustic effect and it is a developing, non-invasive imaging method in biomedical research. This contribution proposes a method to concurrently calculate multiple acoustic speeds in different mediums. Firstly, we get the size of infra-structure of the target by B-mode ultrasonic imaging method. Then we build the photoacoustic (PA) image of the same target with different acoustic speed in different medium. By repeatedly evaluate the quality of reconstruct PA image, we dynamically calibrate the acoustic speeds in different medium to build a finest PA image. Thus, we take these speeds of sound as the correct acoustic propagation velocities in according mediums. Experiments show that our non-invasive method can yield correct speed of sound with less than 0.3% error which might benefit future research in biomedical science.

Automatic speed of sound correction with photoacoustic image reconstruction

Osteoporosis is a progressive bone disease, which is characterized by a decrease in the bone mass and deterioration in bone micro-architecture. In theory, photoacoustic (PA) analysis has the potential to obtain the characteristics of the bone effectively. In this study, we try to compare the PA spectral analysis (PASA) method with the quantitative ultrasound (QUS) method in osteoporosis assessment. We compare the quantified parameter slope from the PASA and broadband ultrasound attenuation from QUS among different bone models, respectively. Both the simulation and ex vivo experiment results show that bone with lower bone mineral density has the higher slope in the PASA method. Our comparison study proves that the PASA method has the same efficiency as QUS in osteoporosis assessment.

Comparison study of photoacoustic and ultrasound spectrum analysis in osteoporosis detection

The diagnosis of osteoporosis is eventually converted to the measurement of bone mineral density (BMD) in clinical trials. Since our previous work had proved the ability of using photoacoustic spectral analysis (PASA) to efficiently detect osteoporosis, in this contribution, we proposed a fully connected multi-layer deep neural network combined with PASA to semi-quantify BMD values corresponding to varying degrees of bone loss and to further evaluate the degree of osteoporosis. Experiments were carried out on swine femur heads, and the performance of our proposed method is satisfying for future clinical screening.

/pdf/bone-mineral-density-value-evaluation-based-on-photoacoustic-53hj57ftsq.pdf

Bone mineral density value evaluation based on photoacoustic spectral analysis combined with deep learning method

The distribution of speed of sound (SOS) in biomedical tissue and delay compensation (DC) have significant impact on the image quality of photoacoustic tomography (PAT). When imaging human peripheral joints, using fixed SOS and DC can only ensure that the reconstructed images are focused in a limited depth range, whereas they are defocused at other depths, which cause severe artifacts and blurring. In this work, a linear-DC based reconstruction approach is proposed to focus the whole PAT image region. It is proved by two in vivo experiments that, compared with traditional delay-and-sum back projection algorithms, the proposed method can effectively optimize the image quality of articular tissues in PAT.

Jie Yuan

Papers

Automatic speed of sound correction with photoacoustic image reconstruction

Comparison study of photoacoustic and ultrasound spectrum analysis in osteoporosis detection

Bone mineral density value evaluation based on photoacoustic spectral analysis combined with deep learning method

Novel Image Optimization Method for Joint Photoacoustic Tomography

Curvelet transform based adaptive image deblocking method