In photoacoustic tomography (PAT), measurement errors arise from optical fluence spatial and temporal variations caused by tissue optical absorption and scattering heterogeneities, system noise, and motion. These errors influence the estimation accuracy of blood oxygenation saturation (sO2). In this study, we introduce a sliding multi-pixel approach to mitigate the effect of measurement errors before computing sO2 maps. As a result, the sO2 estimation is both more accurate, as evaluated by residual fitting errors, as well as smoother. We conclude by presenting diagnostic results from PAT of 33 patients with ovarian masses imaged by our coregistered PAT and ultrasound system.
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for screening and treatment monitoring of breast cancers. However, in clinical cases, the chest wall, bad probe-tissue contact, and tissue heterogeneity can create image artifacts, causing misinterpretation of lesion images. In the current work, realistic and flexible threedimensional numerical breast phantoms were generated using the Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) tools developed by U.S. Food and Drug Administration (FDA). By selecting physical attributes and tissue optical properties, the VICTRE breast phantoms were, for the first time, adopted in DOT for in silico studies. Monte Carlo simulations were conducted to generate the forward data. Edge artifacts (hot spots on the edge of the regions of interests) were found on the reconstructed images when there was a mismatch between the lesion-side breast and the contralateral reference-side breast. We propose a fully automated, connected components analysis-based algorithm that can remove these edge artifacts and improve lesion reconstruction.
Rectal adenocarcinoma is a common cancer in the United States. Current standard of care techniques (colonoscopy and MRI) have notable drawbacks and surgeons have aggressively put most patients into surgical intervention. Here we have developed a new handheld co-registered ultrasound and acoustic-resolution photoacoustic endoscope (AR-PAE) to evaluate rectal cancer in vivo. The PAE - convolutional neuron network (PAE-CNN) models were trained, validated, and tested. Hyperparameters of PAE-CNN including convolutional kernel size, max pooling kernel size, convolution layers and fully connected layers which connect to amount of imaging information preserved were carefully tuned to optimize classification performance.
Colorectal cancer is the second most common malignancy diagnosed globally and the 4th leading cause of cancer mortality. Critical gaps exist in diagnostic and surveillance imaging modalities for colorectal neoplasia. We have conducted a pilot study using a real-time co-registered photoacoustic (PAT) and ultrasound (US) tomography system. A total of 23 ex vivo human colorectal tissue samples (19 colon and 4 rectum) were imaged immediately after surgical resection. These results indicate potential of using PAT/US for future cancer screening and post-treatment surveillance of colon and rectum. The image resolution of the current system is low (~ 250 μm axial resolution) due to the commercial endo-cavity ultrasound transducer array (6 MHz central frequency, 80% bandwidth). To solve the problem of image resolution, we decoded the pin configuration of a high-frequency transducer array (15 MHz central frequency, 9-18 MHz bandwidth) and adapted it to our home-made 128 channels ultrasound pulsing and receiving system to perform high-frequency PAT/US imaging. We achieved a lateral resolution of ~ 150 μm and axial resolution of ~ 120 μm. We also imaged a post-treated human rectum sample to evaluate the system performance.
One of the challenges of quantitative Photoacoustic (PA) imaging is unmixing the optical absorption (μa) of the tissue from system response (C) and Grüneisen parameter (Γ). In this study, we have calculated the absorption coefficient and functional parameters, i.e. total hemoglobin (tHb) and oxygen saturation (sO2) of 5 blood tubes with sO2 values ranging from 24.9% to 97.6% at different depths in intralipid solution. Beer’s law is used to calculate the optical fluence in the target area. Initial values for μa and C×Γ are found by fitting a line to the log of PA beam data. These initial values are iteratively updated using a conjugate gradient method. This process is repeated for all 11 wavelengths. The absorption coefficient spectrum follows the molar extinction coefficient spectrum of deoxy hemoglobin for lower sO2 percentages, and it becomes closer to the spectrum of oxy hemoglobin when the sO2 percentage increases. The calculated absorption coefficients at 11 wavelengths are used to estimate the absolute value of the tHb and sO2 of each blood sample at different depths. The mean error of the estimated tHb values for blood tubes at all depths with respect to the real values are less than 13%. Moreover, the largest sO2 estimation error is 7.5% for the blood sample with sO2 of 24.9%. Our quantitative PA method performed well for the data collected from blood samples. We are investigating this method on our clinical data.
Ultrasound (US) guided diffuse optical tomography has demonstrated great potential for breast cancer diagnosis, treatment monitoring, and chemotherapy response prediction. Optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps, which are then used to calculate the hemoglobin concentration distribution of the US visible lesion. Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. We propose an automated iterative perturbation correction algorithm to reduce image artifacts based on the structural similarity index (SSIM) of absorption maps of four optical wavelengths. The initial image is estimated from the truncated pseudoinverse solution. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. An absorption map is repeatedly reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors are iteratively removed until all wavelength images are structurally similar with SSIM values greater than a threshold. Clinical data demonstrate statistically significant improvement in image artifact reduction.
In this study, a low-cost procedure using evaporated milk is followed to make a gelatin-based phantom with ultrasound and optical properties close to soft tissues. To find out the effect of concentrations of gelatin and evaporated milk on the ultrasound properties, we first made two sets of phantoms. The first set was made by mixing different amounts of gelatin with deionized water (no evaporated milk in this set), while in the second set, evaporated milk concentration was changed (constant gelatin concentration). We measured the ultrasound attenuation of these phantoms at low and high frequency ranges and show that when the gelatin concentration is kept at 5 %, the ultrasound attenuation can vary from 0.4 to 0.6 dB/MHz/cm as the evaporated milk concentration increases from 20 % to 50 %. After getting some idea about the proper concentrations of evaporated milk and gelatin on ultrasound properties, n-propanol alcohol, glass microspheres, and Germall plus preservative were added to our recipe. We then measured the optical properties of the resulted phantom. A diffuse optical tomography system (DOT) was employed for this purpose to measure the optical absorption and reduced scattering coefficients of our phantom at four different wavelengths.
Diffuse Optical Tomography (DOT) provides quantitative information about optical absorption and total hemoglobin concentration of breast tumors which is directly related to tumor angiogenesis. However, measurements errors caused by tissue heterogeneity may introduce artifacts in reconstructed absorption and total hemoglobin maps and therefore cause errors in quantitative characterization of lesions. With ultrasound-guided DOT, these artifacts can be recognized if they are isolated and located at edges of the absorption maps. However, effectively recognize image artifacts and automatically remove them is a challenge because artifacts can be merged partially with lesions maps. A new two-step algorithm is proposed to iteratively identify and remove measurement outliers based on assignment of local outlier factors and hence to reduce image artifacts and produce more consistent absorption maps among different wavelengths. In first step, perturbation measurements were ranked based on data density and the local outlier factor which is the probability of each measurement being an outlier. In second step, outliers are iteratively removed until normalized pattern correlation of different wavelength absorption maps is beyond a specified threshold. The proposed algorithm is evaluated on 20 clinical cases and it has demonstrated its capability to automatically reconstruct more consistent images for different wavelengths. The improvement on characterizing benign breast lesions is more dramatic because outliers can cause the reconstructed benign lesions with higher optical absorption and therefore high hemoglobin contrast.
Near-infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response in patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In this study, we introduce our recently developed ultrasound-guided DOT system, which has been improved in system compactness, robustness, and user-friendliness by custom-designed electronics, automated data preprocessing, and implementation of a new two-step reconstruction algorithm. The system performance has been tested with several sets of solid and blood phantoms and the results show accuracy in reconstructed absorption coefficients as well as blood oxygen saturation. A clinical example of a breast cancer patient, who was undergoing neoadjuvant chemotherapy, is given to demonstrate the system performance.
Diffuse optical tomography (DOT) has demonstrated huge potential in breast cancer diagnosis and treatment monitoring. DOT image reconstruction guided by ultrasound (US) improves the diffused light localization and lesion reconstruction accuracy. However, DOT reconstruction depends on tumor geometry provided by coregistered US. Experienced operators can manually measure these lesion parameters; however, training and measurement time are needed. The wide clinical use of this technique depends on its robustness and faster imaging reconstruction capability. This article introduces a semiautomated procedure that automatically extracts lesion information from US images and incorporates it into the optical reconstruction. An adaptive threshold-based image segmentation is used to obtain tumor boundaries. For some US images, posterior shadow can extend to the chest wall and make the detection of deeper lesion boundary difficult. This problem can be solved using a Hough transform. The proposed procedure was validated from data of 20 patients. Optical reconstruction results using the proposed procedure were compared with those reconstructed using extracted tumor information from an experienced user. Mean optical absorption obtained from manual measurement was 0.21±0.06 cm−1 for malignant and 0.12±0.06 cm−1 for benign cases, whereas for the proposed method it was 0.24±0.08 cm−1 and 0.12±0.05 cm−1, respectively.
According to the World Health Organization, breast cancer is the most common cancer among women worldwide, claiming the lives of hundreds of thousands of women each year. Near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In the past, our group have developed three frequency domain prototype systems which were used in several clinical studies. In this study, we introduce our newly under development US-guided DOT system which is being improved in terms of size, robustness and user friendliness by several custom electronic and mechanical design. A new and robust probe designed to reduce preparation time in clinical process. The processing procedure, data selection and user interface software also updated. With all these improvements, our new system is more robust and accurate which is one step closer to commercialization and wide use of this technology in clinical settings. This system is aimed to be used by minimally trained user in the clinical settings with robust performance. The system performance has been tested in the phantom experiment and initial results are demonstrated in this study. We are currently working on finalizing this system and do further testing to validate the performance of this system. We are aiming toward use of this system in clinical setting for patients with breast cancer.
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