Proc. SPIE. 10820, Optics in Health Care and Biomedical Optics VIII
KEYWORDS: Signal to noise ratio, Optical imaging, Super resolution, Microscopy, Molecules, Medical image reconstruction, Temporal resolution, Super resolution microscopy, Spatial resolution, Compressed sensing
By exploiting the statistics of temporal fluorescent fluctuations, super-resolution optical fluctuation imaging (SOFI) can implement a fast super-resolution microscopy imaging, which is suitable for dynamic live-cell imaging. However, the main drawback of SOFI is that the imaging spatial resolution can be surpassed by the localization-based super-resolution microscopy techniques. To address this problem, we propose a new method, which is achieved by using multiple sparse Bayesian learning (M-SBL) method. Since M-SBL method can take into account simultaneously temporal fluctuations and the sparsity priors of emitter, it provides the possibility to obtain an enhancement in spatial resolution compared to standard SOFI (only considering the temporal fluctuations). To measure the performance of our proposed method, we designed three sets of simulation experiments. Firstly, we compared the performance of M-SBL and SOFI in resolving single emitter, and simulation results have demonstrated that the M-SBL method outperforms SOFI. Furthermore, the other simulation data with varying signal to noise and frame number were used to evaluate the performance of M-SBL in resolving fine structures. And the results indicate that when using the proposed M-SBL method, the imaging spatial resolution can be improved compared to the standard SOFI method. Hence, the M-SBL method provides the potential for increasing the temporal resolution of super-resolution microscopy while maintaining a desired spatial resolution.
Ultrasound (US) imaging technique is one of the most common imaging techniques in clinical applications. However, the spatial resolution of ultrasound is limited. Recently, a fast super-resolution ultrasound imaging (SR-US) technique has been proposed to break the diffraction limit, which is implemented by using super-resolution optical fluctuation imaging (SOFI) method. Further, to reduce the nonlinear response to brightness and blinking heterogeneities in highorder SOFI image, a balanced SOFI (bSOFI) method can also be used in SR-US. It should note that when using bSOFI method, the point spread function (PSF) of the imaging system is a key factor that affect the obtained imaging performance of SR-US. However, bSOFI is a method from optical microscopy. The PSF of optical system is significantly different from PSF of US system. To better apply bSOFI method to ultrasound, in this paper, we investigate the effect of PSF on the imaging performance of SR-US. Especially, to speed up the data acquisition and further improve the temporal resolution of SR-US, here, the US data are acquired by plane wave (PW) scan. The results from the numerical simulation indicate that when considering the characteristic of PSF in ultrasound (i.e., σ <sub>x</sub>≠ σ<sub>y</sub> ), by using bSOFI method, we can greatly improve the imaging performance of US, where the smaller line structure can be effectively resolved compared to the standard US imaging method. As a result, the technique (bSOFI method combined PW scan) provide the potential in ultrafast SR-US imaging.
Super-resolution localization microscopy techniques (e.g., STORM or PALM), breaks the optics diffraction limit, making possible the observation of sub-cellular structures in vivo. However, long acquisition time is required to maintain a desired high spatial resolution. To overcome the limitation, an effective method is to increase the density of activated emitters in each frame. The high-density emitters will cause them to overlap, which makes it difficult to accurately resolve each emitter location. Although some methods have been proposed to identify the overlapped emitters, these methods are computationally intensive and parameter dependent. To address these problems, in this paper, we proposed a novel method based on convolutional neural networks (CNN) for super-resolution localization microscopy, termed as DL-SRLM. DL-SRLM is capable of learning the nonlinear mapping between a camera frame (i.e., the experimentally acquired low-resolution image) and the true locations of emitters in the corresponding image region (i.e., the recovered super-resolution image). As a result, the method provides the possibility to faster resolve the high-density emitters, without requiring the parameters. To evaluate the performance of DL-SRLM, a series of simulations with varying emitter densities, signal-to-noise ratios (SNRs), and point spread functions (PSFs) were performed. The results show that DL-SRLM can accurately resolve the locations of high-density emitters, even if when the raw measurement data contained noise or was generated by using inaccurate PSF. In addition, DL-SRLM greatly improve the computational speed (~ 15 ms/frame) compared with the current methods while avoiding the effect of the parameters on the super-resolution imaging performance.
Super-resolution ultrasound (SR-US) imaging can achieve a ten-fold resolution improvement compared with the traditional ultrasound technique, which is important for the medical diagnosis and treatment. However, challenges remain in SR-US imaging. In this paper, on one hand, a Gaussian fitting method, derived from optical localization microscopy, is used to improve the imaging spatial resolution of the SR-US. On the other hand, a plane wave technique is also used in US imaging for improving the imaging speed of the SR-US. To evaluate the performance of the proposed method, the numerical simulation was performed based on a phantom model. The experimental results indicate that by the use of a Gaussian fitting location method, combined with a plane wave transmission technique, we can accurately image the movement of microbubble in the phantom at a high frame rate, compared to the conventional B-model imaging. Hence, the technique makes it possible to achieve fast SR-US imaging.
Ultrasound (US) imaging technique is currently one of the most common imaging techniques in clinical application, but the spatial resolution is low. Recently, with the aid of contrast agents, super-resolution ultrasound imaging technique has been proposed, which can overcome the diffraction limit in US by using the super-localization method, similar to superresolution optical microscopy. But, there is still a trade-off between spatial and temporal resolution in super-resolution US imaging. To address the problem, inspired by super-resolution optical fluctuation imaging (SOFI), in this paper, we apply SOFI to US imaging to achieve a good imaging performance. Further, to cancel the nonlinear response to brightness in SOFI, the balanced SOFI (bSOFI) is also used in this paper, which allows to achieve the higher spatial resolution. To evaluate the feasibility of the proposed method, the numerical simulation was performed based on a dynamic phantom model, which was scanned by synthetic transmit aperture (STA) technique. The result indicates that by using the proposed method (SOFI or bSOFI), the imaging performance of US can be improved compared to STA. In addition, when using bSOFI method, the imaging performance of super-resolution US can be further improved, compared with SOFI method.
Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (<5%), by a short (e.g., 6 frame) projection sequence, we can resolve the poly(DL-lactic-coglycolic acid)/indocynaine green (PLGA/ICG) distributions in heart and lungs, even though there are great variances in optical properties. In contrast, when 20% Gaussian noise is added to the input data, it hardly resolves the distributions of PLGA/ICG in heart and lungs even though accurate optical properties are used. However, with an increased number of frames, the resolving performance of PCA may gradually recover.
Fluorescence diffuse optical tomography (FDOT) plays an important role in studying physiological and pathological
processes of small animals in vivo. The low spatial resolution, however, limits the ability of FDOT in resolving the biodistributions
of fluorescent markers. The anatomical information provided by X-ray computed tomography (CT) can be
used to improve the image quality of FDOT. However, in most hybrid FDOT/CT systems, the projection data sets of
optics and X-ray are acquired sequentially, which increases the acquisition time and bring in the unwanted soft tissue
displacement. In this paper, we evaluate the performance of a synchronous FDOT/CT system, which allows for faster
and concurrent imaging. Compared with previous FDOT/CT systems, the two subsystems (FDOT and CT) acquire
projection images in synchronous mode, so the body position can keep consistent in the same projection data acquired by
both subsystems. The experimental results of phantom and in vivo experiments suggest that the reconstruction quality of
FDOT can be significantly improved when structural a priori information is utilized to constrain the reconstruction
process. On the other hand, the synchronous FDOT/CT system decreases the imaging time.
Developmental dysplasia of the hip is a congenital hip joint malformation affecting the proximal femurs and acetabulum
that are subluxatable, dislocatable, and dislocated. Conventionally, physicians made diagnoses and treatments only based
on findings from two-dimensional (2D) images by manually calculating clinic parameters. However, anatomical
complexity of the disease and the limitation of current standard procedures make accurate diagnosis quite difficultly. In
this study, we developed a system that provides quantitative measurement of 3D clinical indexes based on computed
tomography (CT) images. To extract bone structure from surrounding tissues more accurately, the system firstly
segments the bone using a knowledge-based fuzzy clustering method, which is formulated by modifying the objective
function of the standard fuzzy c-means algorithm with additive adaptation penalty. The second part of the system
calculates automatically the clinical indexes, which are extended from 2D to 3D for accurate description of spatial
relationship between femurs and acetabulum. To evaluate the system performance, experimental study based on 22
patients with unilateral or bilateral affected hip was performed. The results of 3D acetabulum index (AI) automatically
provided by the system were validated by comparison with 2D results measured by surgeons manually. The correlation
between the two results was found to be 0.622 (p<0.01).