Aaron C. Chan

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimators performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise.

Dual-Band Time-Multiplexing Swept-Source Optical Coherence Tomography Based on Optical Parametric Amplification

We report a high-speed time-multiplexing dual wavelength band swept laser source based on an optical parametric amplifier. A dual-band swept-source optical coherence tomography (OCT) system is implemented to demonstrate the advantage of a second wavelength band for fast spectroscopic OCT (SOCT). The innovative time-multiplexing architecture greatly reduces the complexity of the coupling and detecting configuration in comparison with the previous dual-band swept-source setup. We demonstrate the optical parametric amplification’s characteristics as a dual-band generator and applied the source to firstly achieve the SOCT around 1550 nm.

A novel de novo BRCA1 mutation in a Chinese woman with early onset breast cancer

Germline mutations in the two breast cancer susceptibility genes, BRCA1 and BRCA2 account for a significant portion of hereditary breast/ovarian cancer. De novo mutations such as multiple exon deletion are rarely occurred in BRCA1 and BRCA2. During our mutation screening for BRCA1/2 genes to Chinese women with risk factors for hereditary breast/ovarian cancer, we identified a novel germline mutation, consisting of a deletion from exons 1 to 12 in BRCA1 gene, in a patient diagnosed with early onset triple negative breast cancer with no family history of cancer. None of her parents carried the mutation and molecular analysis showed that this novel de novo germline mutation resulted in down-regulation of BRCA1 gene expression.

Maximum Likelihood Doppler Frequency Estimation Under Decorrelation Noise for Quantifying Flow in Optical Coherence Tomography

Recent hardware advances in optical coherence tomography (OCT) have led to ever higher A-scan rates. However, the estimation of blood flow axial velocities is limited by the presence and type of noise. Higher acquisition rates alone do not necessarily enable precise quantification of Doppler velocities, particularly if the estimator is suboptimal. In previous work, we have shown that the Kasai autocorrelation estimator is statistically suboptimal under conditions of additive white Gaussian noise. In addition, for practical OCT measurements of flow, decorrelation noise affects Doppler frequency estimation by broadening the signal spectrum. Here, we derive a general maximum likelihood estimator (MLE) for Doppler frequency estimation that takes into account additive white noise as well as signal decorrelation. We compare the decorrelation MLE with existing techniques using simulated and flow phantom data and find that it has better performance, achieving the Cramer-Rao lower bound. By making an approximation, we also provide an interpretation of this method in the Fourier domain. We anticipate that this estimator will be particularly suited for estimating blood flow in in vivo scenarios.

Maximum likelihood estimation of blood velocity using Doppler optical coherence tomography

A recent trend in optical coherence tomography (OCT) hardware has been the move towards higher A-scan rates. However, the estimation of axial blood flow velocities is affected by the presence and type of noise, as well as the estimation method. Higher acquisition rates alone do not enable the accurate quantification of axial blood velocity. Moreover, decorrelation is an unavoidable feature of OCT signals when there is motion relative to the OCT beam. For in-vivo OCT measurements of blood flow, decorrelation noise affects Doppler frequency estimation by broadening the signal spectrum. Here we derive a maximum likelihood estimator (MLE) for Doppler frequency estimation that takes into account spectral broadening due to decorrelation. We compare this estimator with existing techniques. Both theory and experiment show that this estimator is effective, and outperforms the Kasai and additive white Gaussian noise (AWGN) ML estimators. We find that maximum likelihood estimation can be useful for estimating Doppler shifts for slow axial flow and near transverse flow. Due to the inherent linear relationship between decorrelation and Doppler shift of scatterers moving relative to an OCT beam, decorrelation itself may be a measure of flow speed.

Doppler frequency estimators under additive and multiplicative noise

In optical coherence tomography (OCT), unbiased and low variance Doppler frequency estimators are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible. However, it is known that the Kasai autocorrelation estimator, unexpectedly, performs worse as acquisition rates increase. Here we suggest that maximum likelihood estimators (MLEs) that utilize prior knowledge of noise statistics can perform better. We show that the additive white Gaussian noise maximum likelihood estimator (AWGN MLE) has a superior performance to the Kasai autocorrelation estimate under additive shot noise conditions. It can achieve the Cramer-Rao Lower Bound (CRLB) for moderate data lengths and signal-to-noise ratios (SNRs). However, being a parametric estimator, it has the disadvantages of sensitivity to outliers, signal contamination and deviations from noise model assumptions. We show that under multiplicative decorrelation noise conditions, the AWGN MLE performance deteriorates, while the Kasai estimator still gives reasonable estimates. Hence, we further develop a multiplicative noise MLE for use under multiplicative noise dominant conditions. According to simulations, this estimator is superior to both the AWGN MLE and the Kasai estimator under these conditions, but requires knowledge of the decorrelation statistics. It also requires more computation. For actual data, the decorrelation MLE appears to perform adequately without parameter optimization. Hence we conclude that it is preferable to use a maximum likelihood approach in OCT Doppler frequency estimation when noise statistics are known or can be accurately estimated.

Doppler OCT and OCT Angiography for In Vivo Imaging of Vascular Physiology

Optical imaging methods (Grinvald et al., 1986; Villringer and Chance, 1997; Wilt et al., 2009) have had a significant impact on the field of neuroimaging and are now widely used in studies of both cellular and vascular physiology and pathology. Currently, in vivo optical imaging modalities can be broadly classified into two groups: macroscopic methods using diffuse light (optical intrinsic signal imaging (Grinvald et al., 1986), laser Doppler imaging (Dirnagl et al., 1989), laser speckle imaging (Dunn et al., 2001), diffuse optical imaging (Villringer and Chance, 1997), and laminar optical tomography (Hillman et al., 2004)) which achieve spatial resolutions of hundreds of microns to millimeters, and microscopic methods (two photon and confocal microscopy) which achieve micron-scale resolutions. Two-photon microscopy (Denk et al., 1990), in particular, is widely used in structural and functional imaging at the cellular and subcellular levels. While macroscopic imaging methods using diffuse light can achieve high penetration depths and large fields of view, they do not provide high spatial resolution. While two-photon microscopy achieves subcellular spatial resolution, the imaging speed, penetration depth, and field of view are limited.

N‑Myc downstream‑regulated gene 2 restrains glycolysis and glutaminolysis in clear cell renal cell carcinoma

Glycolysis and glutaminolysis are heavily involved in the metabolic reprogramming of cancer cells. The activation of oncogenes and inactivation of tumor suppressor genes has a marked effect on the cellular metabolic processes glycolysis and glutaminolysis. N-Myc downstream-regulated gene 2 (NDRG2) is a tumor suppressor gene that previous studies have demonstrated can inhibit the growth, proliferation and metastasis of clear cell renal cell carcinoma (ccRCC) cells. However, the function of NDRG2 in ccRCC metabolism remains unknown. In the present study, NDRG2 significantly inhibited the consumption of glucose and glutamine, as well as the production of lactate and glutamate in ccRCC. NDRG2 significantly suppressed the expression of glucose transporter 1, hexokinase 2, pyruvate kinase M2, lactate dehydrogenase A, glutamine transporter ASC amino acid transporter 2 and glutaminase 1 at the mRNA (by quantitative polymerase chain reaction) and protein level (by western blot analysis), all of which are key regulators and enzymes in glycolysis and glutaminolysis. Data from the present study also revealed that overexpression of NDRG2 suppressed cell proliferation in ccRCC in vitro and in vivo, demonstrated by colony formation assays, wound healing assay and nude mouse transplantation tumor experiment. The present findings demonstrate for the first time that NDRG2 acts as a key inhibitor of glycolysis and glutaminolysis in ccRCC and could be a promising target for the metabolic treatment of ccRCC.

Image Refocus in Geometrical Optical Phase Space

We introduce matrix optics and phase space methods as general modeling methods to analyze problems involving 4-D light field imaging systems. Specifically we demonstrate the use of these methods for analyzing the image refocus process.

Abstract 3027: Plasma microRNA as a potential marker for breast cancer detection

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC Background: Recently, the emergence of small non-protein-coding RNAs, so-called microRNAs (miRNAs), playing important roles in oncogenesis has opened new opportunities for early cancer diagnosis. We have previously shown that microRNAs (miRNAs) in plasma are promising biomarkers for colorectal cancer detection. Here, we investigated whether plasma miRNAs could discriminate patients with and without breast cancer (BC). Methods: Using TaqMan-based low density miRNA array, miRNAs were profiled from cancerous and adjacent non-cancerous breast tissues, corresponding plasma of 5 BC patients, along with plasma from 5 healthy controls. Marker selection and validation were performed by real-time quantitative RT-PCR on a small set of plasma. Independent set of plasma from 80 BC patients, 20 gastric cancer, 20 lung cancer, 20 colorectal cancer, 20 hepatocellular carcinoma and 50 healthy controls were further validated. Results: Of the panel of 377 miRNAs analyzed, 8 miRNAs (miR-16, miR-21, miR-27a, miR-141, miR-191, miR-200c, miR-210, miR-451) were up-regulated both in plasma and tissue samples of 5 BC patients. All 8 putative miRNA markers were validated on the plasma of 15 BC patients and 15 healthy controls. Only three miRNAs were significantly elevated in this cohort of BC patients (p<0.0005). The plasma levels of the three markers in those 15 BC patients were significantly reduced after surgery (p<0.05). Further validation with an independent set of plasma samples (n=210) indicated that two markers differentiate BC from normal subjects, colorectal cancer, gastric cancer, hepatocellular carcinoma and lung cancer. These two markers yielded a combined receiver operating characteristic curve area of 88.5%, the sensitivity was 92% and the specificity was 72% in discriminating BC from control subjects. Conclusions: Plasma miRNAs significantly elevated in BC patients are identified. This can be a novel noninvasive molecular marker for BC screening. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3027.