Automatic Cancer Tissue Detection Using Multispectral Photoacoustic Imaging

Convolutional neural networks (CNNs) have become increasingly popular in recent years because of their ability to tackle complex learning problems such as object detection, and object localization. They are being used for a variety of tasks, such as tissue abnormalities detection and localization, with an accuracy that comes close to the level of human predictive performance in medical imaging. The success is primarily due to the ability of CNNs to extract the discriminant features at multiple levels of abstraction. Photoacoustic (PA) imaging is a promising new modality that is gaining significant clinical potential. The availability of a large dataset of three dimensional PA images of ex-vivo human prostate and thyroid specimens has facilitated this current study aimed at evaluating the efficacy of CNN for cancer diagnosis. In PA imaging, a short pulse of near-infrared laser light is sent into the tissue, but the image is created by focusing the ultrasound waves that are photoacoustically generated due to the absorption of light, thereby mapping the optical absorption in the tissue. By choosing multiple wavelengths of laser light, multispectral photoacoustic (MPA) images of the same tissue specimen can be obtained. The objective of this thesis is to implement deep learning architecture for cancer detection using the MPA image dataset. In this study, we built and examined a fully automated deep learning framework that learns to detect and localize cancer regions in a given specimen entirely from its MPA image dataset. The dataset for this work consisted of samples with size ranging from 12 × 45 × 200 pixels to 64 × 64 × 200 pixels at five wavelengths namely, 760 nm, 800 nm, 850 nm, 930 nm, and 970 nm. The proposed algorithms first extract features using convolutional kernels and then detect cancer tissue using the softmax function, the last layer of the network. The AUC was calculated to evaluate the performance of the cancer tissue detector with a very promising result. To the best of our knowledge, this is one of the first examples of the application of deep 3D CNN to a large cancer MPA dataset for the prostate and thyroid cancer detection. While previous efforts using the same dataset involved decision making using mathematically extracted image features, this work demonstrates that this process can be automated without any significant loss in accuracy. Another major contribution of this work has been to demonstrate that both prostate and thyroid datasets can be combined to produce improved results for cancer diagnosis

Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

none4openZaffino, Paolo; Moccia, Sara; De Momi, Elena; Spadea, Maria FrancescaZaffino, Paolo; Moccia, Sara; De Momi, Elena; Spadea, Maria Francesc

A practical guide to photoacoustic tomography in the life sciences

The life sciences can benefit greatly from imaging technologies that connect microscopic discoveries with macroscopic observations. One technology uniquely positioned to provide such benefits is photoacoustic tomography (PAT), a sensitive modality for imaging optical absorption contrast over a range of spatial scales at high speed. In PAT, endogenous contrast reveals a tissue's anatomical, functional, metabolic, and histologic properties, and exogenous contrast provides molecular and cellular specificity. The spatial scale of PAT covers organelles, cells, tissues, organs, and small animals. Consequently, PAT is complementary to other imaging modalities in contrast mechanism, penetration, spatial resolution, and temporal resolution. We review the fundamentals of PAT and provide practical guidelines for matching PAT systems with research needs. We also summarize the most promising biomedical applications of PAT, discuss related challenges, and envision PAT's potential to lead to further breakthroughs

Single-breath-hold photoacoustic computed tomography of the breast

We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to reveal detailed angiographic structures in human breasts. SBH-PACT features a deep penetration depth (4 cm in vivo) with high spatial and temporal resolutions (255 µm in-plane resolution and a 10 Hz 2D frame rate). By scanning the entire breast within a single breath hold (~15 s), a volumetric image can be acquired and subsequently reconstructed utilizing 3D back-projection with negligible breathing-induced motion artifacts. SBH-PACT clearly reveals tumors by observing higher blood vessel densities associated with tumors at high spatial resolution, showing early promise for high sensitivity in radiographically dense breasts. In addition to blood vessel imaging, the high imaging speed enables dynamic studies, such as photoacoustic elastography, which identifies tumors by showing less compliance. We imaged breast cancer patients with breast sizes ranging from B cup to DD cup, and skin pigmentations ranging from light to dark. SBH-PACT identified all the tumors without resorting to ionizing radiation or exogenous contrast, posing no health risks

Multimodal assessment of non-alcoholic fatty liver disease with transmission-reflection optoacoustic ultrasound

Non-alcoholic fatty liver disease (NAFLD) is an umbrella term referring to a group of conditions associated to fat deposition and damage of liver tissue. Early detection of fat accumulation is essential to avoid progression of NAFLD to serious pathological stages such as liver cirrhosis and hepatocellular carcinoma. Methods: We exploited the unique capabilities of transmission-reflection optoacoustic ultrasound (TROPUS), which combines the advantages of optical and acoustic contrasts, for an early-stage multi-parametric assessment of NAFLD in mice. Results: The multispectral optoacoustic imaging allowed for spectroscopic differentiation of lipid content, as well as the bio-distributions of oxygenated and deoxygenated hemoglobin in liver tissues in vivo. The pulse-echo (reflection) ultrasound (US) imaging further provided a valuable anatomical reference whilst transmission US facilitated the mapping of speed of sound changes in lipid-rich regions, which was consistent with the presence of macrovesicular hepatic steatosis in the NAFLD livers examined with ex vivo histological staining. Conclusion: The proposed multimodal approach facilitates quantification of liver abnormalities at early stages using a variety of optical and acoustic contrasts, laying the ground for translating the TROPUS approach toward diagnosis and monitoring NAFLD in patients