Determination of ovarian status and follicle monitoring are common methods of diagnosing female infertility. We
evaluated the suitability of selective plane illumination microscopy (SPIM) for the study of ovarian follicles. Owing to
the large field of view and fast acquisition speed of our newly developed SPIM system, volumetric image stacks from
entire intact samples of pig ovaries have been rendered demonstrating clearly discernible follicular features like follicle
diameters (70 μm - 2.5 mm), size of developing Cumulus oophorus complexes (COC ) (40 μm - 110 μm), and follicular
wall thicknesses (90 μm-120 μm). The observation of clearly distinguishable COCs protruding into the follicular antrum
was also shown possible, and correlation with the developmental stage of the follicles was determined. Follicles of all
developmental stages were identified, and even the small primordial follicle clusters forming the egg nest could be
observed. The ability of the system to non-destructively generate sub-cellular resolution 3D images of developing
follicles, with excellent image contrast and high throughput capacity compared to conventional histology, suggests that it
can be used to monitor follicular development and identify structural abnormalities indicative of ovarian ailments.
Accurate folliculometric measurements provided by SPIM images can immensely help the understanding of ovarian
physiology and provide important information for the proper management of ovarian diseases.
In optoacoustic imaging, the resolution and image quality in a certain imaging position usually cannot be enhanced without changing the imaging configuration. Post-reconstruction image processing methods offer a new possibility to improve image quality and resolution. We have developed a geometrical super-resolution (GSR) method which uses information from spatially separated frames to enhance resolution and contrast in optoacoustic images. The proposed method acquires several low resolution images from the same object located at different positions inside the imaging plane. Thereafter, it applies an iterative registration algorithm to integrate the information in the acquired set of images to generate a single high resolution image. Herein, we present the method and evaluate its performance in simulation and phantom experiments, and results show that geometrical super-resolution techniques can be a promising alternative to enhance resolution in optoacoustic imaging.
The speed of sound (SoS) in the imaged sample and in the coupling medium is an important parameter in optoacoustic tomography that must be specified in order to accurately restore maps of local optical absorbance. In this work, several hybrid focusing functions are described that successfully determine the most suitable SoS based on post-reconstruction images. The SoS in the coupling medium (water) can be determined from temperature readings. Thereby, this value is suggested to be used as an initial guess for faster SoS calibration in the reconstruction of tissues having a different SoS than water.
Reconstruction in multispectral optoacoustic tomography has become an critical area of importance, given the development of real-time imaging and visualization techniques. Speed of sound calibration is an intrinsic problem associated with the reconstruction process. Traditionally, the calibration has been user mediated, making it a tedious and offline affair. In this manuscript, we aim to introduce autofocusing and wavelet based measures to automatically calibrate the speed of sound. Further, it is observed that the temperature of the coupling medium (water) often drift during the signal acquisition, severely straining the image quality. The measures address these problems by iteratively determining the speeds with the changing boundary conditions with time.