We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.

Abstract:In this study, we in vivo examined injury progression after intracerebral haemorrhage (ICH) induced by collagenase in mice using cross-sectional photoacoustic tomography (csPAT). csPAT displayed high resolution with high sensitivity for ICH detection. The PAT images obtained showed high correlation with conventional histologic images. Quantitative analysis of the hematoma areas detected by csPAT showed high consistency with the neurologic deficit score (NDS). By utilizing the dual-wavelength method, the development of the hemoglobin area was monitored. Our results indicated that noninvasive csPAT can be used to track the dynamic progression of post-ICH, and to evaluate therapeutic interventions in preclinical ICH models. Wagner, "European Stroke Organisation (ESO) guidelines for the management of spontaneous intracerebral hemorrhage," Int. J. Stroke 9(7), 840-855 (2014). 2. C. J. van Asch, M. J. Luitse, G. J. Rinkel, I. van der Tweel, A. Algra, and C. J. Klijn, "Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis," Lancet Neurol. 9(2), 167-176 (2010). 3. J. Yang, Q. Li, Z. Wang, C. Qi, X. Han, X. Lan, J. Wan, W. Wang, X. Zhao, Z. Hou, C. Gao, J. R. Carhuapoma, S. Mori, J. Zhang, and J. Wang, "Multimodality MRI assessment of grey and white matter injury and blood-brain barrier disruption after intracerebral haemorrhage in mice," Sci.

Microwave-induced thermoacoustic tomography (TAT) is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging. While an image reconstruction algorithm is critical for the TAT, current reconstruction methods often creates significant artifacts and are computationally costly. In this work, we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image. We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy. For the scenarios where realistic training data are scarce or unavailable, we use the finite element method (FEM) to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method. The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness. Compared with other state-of-the-art reconstruction methods, the TAT-Net method can reduce the root mean square error to 0.0143, and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64, respectively. The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.