Quing Zhu

Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix

Abstract. Ultrasound-guided diffuse optical tomography (DOT) is a promising imaging technique that maps hemoglobin concentrations of breast lesions to assist ultrasound (US) for cancer diagnosis and treatment monitoring. The accurate recovery of breast lesion optical properties requires an effective image reconstruction method. We introduce a reconstruction approach in which US images are encoded as prior information for regularization of the inversion matrix. The framework of this approach is based on image reconstruction package “NIRFAST.” We compare this approach to the US-guided dual-zone mesh reconstruction method, which is based on Born approximation and conjugate gradient optimization developed in our laboratory. Results were evaluated using phantoms and clinical data. This method improves classification of malignant and benign lesions by increasing malignant to benign lesion absorption contrast. The results also show improvements in reconstructed lesion shapes and the spatial distribution of absorption maps.

Laser scanning laser diode photoacoustic microscopy system

The development of low-cost and fast photoacoustic microscopy systems enhances the clinical applicability of photoacoustic imaging systems. To this end, we present a laser scanning laser diode-based photoacoustic microscopy system. In this system, a 905 nm, 325 W maximum output peak power pulsed laser diode with 50 ns pulsewidth is utilized as the light source. A combination of aspheric and cylindrical lenses is used for collimation of the laser diode beam. Two galvanometer scanning mirrors steer the beam across a focusing aspheric lens. The lateral resolution of the system was measured to be ∼21 μm using edge spread function estimation. No averaging was performed during data acquisition. The imaging speed is ∼370 A-lines per second. Photoacoustic microscopy images of human hairs, ex vivo mouse ear, and ex vivo porcine ovary are presented to demonstrate the feasibility and potentials of the proposed system.

Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging

Imaging-guided 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 breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, pre-processing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%.

Diffuse optical tomography using semiautomated coregistered ultrasound measurements

Abstract. 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.

Low-cost laser scanning photoacoustic microscopy system with a pulsed laser diode excitation source

We present a low-cost laser scanning photoacoustic microscopy system with a pulsed laser diode as the excitation source. The system utilizes a 905 nm pulsed laser diode with 120 ns pulse width and 1 KHz repetition rate. No averaging is performed in data acquisition, resulting in a short image acquisition time. The maximum field of view is 4.6 mm × 3.7 mm and the lateral resolution is 71 μm. Images of human hairs and mouse ear are presented to demonstrate the feasibility of the system in imaging biological tissue.

Classification of human ovarian tissue using full field optical coherence tomography

The feasibility of a full-field optical coherence tomography (FFOCT) system for rapid wide field optical analysis of normal and malignant human ovarian tissue pathologies was demonstrated. Five features were extracted from the normalized image histogram from 56 FFOCT images, based on the differences in the morphology of the normal and malignant tissue samples.

Preliminary results of miniaturized and robust ultrasound guided diffuse optical tomography system for breast cancer detection

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.

Two step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography

Due to the correlated nature of diffused light, the problem of reconstructing optical properties using diffuse optical tomography (DOT) is ill-posed. US-, MRI- or x-ray-guided DOT approaches can reduce the total number of parameters to be estimated and improve optical reconstruction accuracy. However, when the target volume is large, the number of parameters to estimate can exceed the number of measurements, resulting in an underdetermined imaging model. In such cases, accurate image reconstruction is difficult and regularization methods should be employed to obtain a useful solution. In this manuscript, a simple two-step reconstruction method that can produce useful image estimates in DOT is proposed and investigated. In the first step, a truncated Moore-Penrose Pseudoinverse solution is computed to obtain a preliminary estimate of the image that can be reliably determined from the measured data; subsequently, this preliminary estimate is incorporated into the design of a penalized least squares estimator that is employed to compute the final image estimate. By use of phantom data, the proposed method was demonstrated to yield more accurate images than those produced by conventional reconstruction methods. The method was also evaluated with clinical data that included 10 benign and 10 malignant cases. The capability of reconstructing high contrast malignant lesions was demonstrated to be improved by use of the proposed method.

An Automated Preprocessing Method for Diffuse Optical Tomography to Improve Breast Cancer Diagnosis

The ultrasound-guided diffuse optical tomography is a noninvasive imaging technique for breast cancer diagnosis and treatment monitoring. The technique uses a handheld probe capable of providing measurements of multiple wavelengths in a few seconds. These measurements are used to estimate optical absorptions of lesions and calculate the total hemoglobin concentration. Any measurement errors caused by low signal to noise ratio data and/or movements during data acquisition would reduce the accuracy of reconstructed total hemoglobin concentration. In this article, we introduce an automated preprocessing method that combines data collected from multiple sets of lesion measurements of 4 optical wavelengths to detect and correct outliers in the perturbation. Two new measures of correlation between each pair of wavelength measurements and a wavelength consistency index of all reconstructed absorption maps are introduced. For phantom and patients’ data without evidence of measurement errors, the correlation coefficient between each pair of wavelength measurements was above 0.6. However, for patients with measurement errors, the correlation coefficient was much lower. After applying the correction method to 18 patients’ data with measurement errors, the correlation has improved and the wavelength consistency index is in the same range as the cases without wavelength-dependent measurement errors. The results show an improvement in classification of malignant and benign lesions.

In vivo imaging of human ovarian cancer using co-registered ultrasound and photoacoustic tomography (Conference Presentation)

We have conducted a pilot study to image and characterize ovarian masses using a co-registered ultrasound (US) and photoacoustic imaging (PAI) system. A total of seven patients who have ovarian masses and scheduled for surgical removal of both ovaries and Fallopian tubes, has enrolled to the study. A standard transvaginal US probe is used first to locate ovarian masses, measure ovary sizes in long and short axises . Then a second probe with four light-delivery fibers surrounded the US probe is inserted transvaginally to perform co-registered US and photoacoustic imaging in real-time with four optical wavelengths (730, 780, 800 and 830 nm). For the total of 7 patients, one patient had high-grade serous carcinoma involving both ovaries, one patient had a 2.2 cm endometrioid adenocarcinoma in right ovary, one patient had metastatic appendiceal adenocarcinoma involving both ovaries, one patient has serous borderline carcinoma involving both ovaries and the rest three patients had either abnormal or benign ovaries. For the malignant ovaries, photoacoustic images have showed significantly higher signal levels as quantitatively evaluated from maximum signal strengths from region-of-interest identified by co-registered US. Additionally, the quantitative features extracted from co-registered US and photoacoustic images, such as spectral slope, mid-band fit and zero MHz intercept, spatial heterogeneity of PAI distribution, the blood oxygen saturation, have showed significant differences between malignant and benign ovaries. Our initial results have demonstrated that photoacoustic imaging has a great potential to aid transvaginal US to quantitatively and effectively image and diagnose ovarian cancer.