Curvilinear endocavity ultrasound images capture a wide field of view with a miniature probe. In adapting photoacoustic imaging (PAI) to work with such ultrasound systems, light delivery is challenged by the tradeoff between image quality and laser safety concerns. Here, we present two novel designs based on cylindrical lenses that are optimized for transvaginal PAI B-scan imaging. Our simulation and experimental results demonstrate that, compared to conventional light delivery methods for PAI imaging, the proposed designs are safer for higher pulse energies and provide deeper imaging and a wider lateral field of view. The proposed designs could also improve the performance of endoscopic co-registered ultrasound/ photoacoustic imaging in other clinical applications.
KEYWORDS: Acquisition tracking and pointing, Machine learning, Medical image reconstruction, Interpolation, Image restoration, Ultrasound transducers, Signal detection, Reconstruction algorithms, Monte Carlo methods, 3D vision
Photoacoustic tomography (PAT) is a valuable tool in characterizing ovarian lesions for accurate diagnosis. However, limited view problem degrades the quality of PAT reconstruction severely, especially for transvaginal transducer which only partially encloses the target. To address this issue, we compensated limited view information loss by co-registered PAT and US machine learning method. The simulation and phantom results showed that the details of the target were recovered by proposed method, compared with delay-and-sum reconstruction method.
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated success in breast cancer diagnosis. However, DOT data pre-processing and reconstruction still require some level of manual operation, for example, contralateral reference selection and data cleaning. In this study, we introduce an automated data pre-processing and reconstruction pipeline to accelerate the DOT clinical translation. The pipeline has integrated several data pre-processing modules and reconstruction methods that are adapted to data. The pipeline is implemented using a graphical user interface. Initial testing has shown that it can automate DOT right after the data acquisition and provides an accurate diagnostic score on cancer vs. benign probability.
Significance: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue.
Aim: We aim to reduce the chest wall’s effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction.
Approach: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall.
Results: The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth.
Conclusions: Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.
A machine learning (ML) model with physical constraints is introduced to perform diffuse optical tomography (DOT) reconstruction. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) An unsupervised auto-encoder with transfer learning is adopted for clinical data without a ground truth, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of the existing model. In a phantom study, compared with the Born conjugate gradient descent (CGD) reconstruction method, the ML method improves the reconstructed maximum absorption coefficient by 18.3% on high contrast phantom and by 61.3% on low contrast phantom, with improved depth distribution of absorption maps. In a clinical study, better contrast was obtained from a treated breast cancer pre- and post- treatment.
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