KEYWORDS: Magnetic resonance imaging, Sensors, Signal to noise ratio, Magnetism, Imaging systems, Resonators, Light sources, Scanners, Spatial resolution, Magnetic sensors
A noncontact optical detector for in vivo imaging has been developed that is compatible with magnetic resonance imaging (MRI). The optical detector employs microlens arrays and might be classified as a plenoptic camera. As a resulting of its design, the detector possesses a slim thickness and is self-shielding against radio frequency (RF) pulses. For experimental investigation, a total of six optical detectors were arranged in a cylindrical fashion, with the imaged object positioned in the center of this assembly. A purposely designed RF volume resonator coil has been developed and is incorporated within the optical imaging system. The whole assembly was placed into the bore of a 1.5 T patient-sized MRI scanner. Simple-geometry phantom studies were performed to assess compatibility and performance characteristics regarding both optical and MR imaging systems. A bimodal ex vivo nude mouse measurement was conducted. From the MRI data, the subject surface was extracted. Optical images were projected on this surface by means of an inverse mapping algorithm. Simultaneous measurements did not reveal influences from the magnetic field and RF pulses onto optical detector performance (spatial resolution, sensitivity). No significant influence of the optical imaging system onto MRI performance was detectable.
KEYWORDS: Sensors, Microlens, Data acquisition, Image sensors, In vivo imaging, 3D image processing, Geometrical optics, Imaging systems, Optical engineering, Binary data
This article proposes a surface reconstruction method from multiview projectional data acquired by means of a rotationally mounted microlens array-based light detector (MLA-D). The technique is adapted for in vivo small animal imaging, specifically for imaging of nude mice, and does not require an additional imaging step (e.g., by means of a secondary structural modality) or additional hardware (e.g., laser-scanning approaches). Any potential point within the field of view (FOV) is evaluated by a proposed photo-consistency measure, utilizing sensor image light information as provided by elemental images (EIs). As the superposition of adjacent EIs yields depth information for any point within the FOV, the three-dimensional surface of the imaged object is estimated by a graph cuts-based method through global energy minimization. The proposed surface reconstruction is evaluated on simulated MLA-D data, incorporating a reconstructed mouse data volume as acquired by x-ray computed tomography. Compared with a previously presented back projection-based surface reconstruction method, the proposed technique yields a significantly lower error rate. Moreover, while the back projection-based method may not be able to resolve concave surfaces, this approach does. Our results further indicate that the proposed method achieves high accuracy at a low number of projections.
A micro-lens array based optical detector (MLA-D) has been developed for preclinical in vivo optical imaging applications. While primarily intended for detecting signals from molecular optical probes within living subjects (mice), the MLA-D also can be used effectively to capture the surface of the imaged object in three dimensions from only a few projection angles - a feature that is very important for in vivo optical imaging. In order to study the shape recognition ability of the MLA-D design we have developed a ray-tracing simulation framework. The impact of the following physical MLA-D parameters on surface recognition efficiency can be studied: micro-lens diameter, micro-lens focal length, and sensor pixel size. By using this framework the performance of two surface recognition algorithms - the optical flow method and the multi-projection surface reconstruction (back-projection) method - has been assessed within the specific context of preclinical imaging application. By way of example, the commonly used DigiMouse dataset is adopted to generate simulated raw image data. Results of the simulation framework conform well with the depth-of-field theory, and both surface recognition methods yield comparable, but unsatisfactory results. Whereas the optical flow method reveals the relative shape of the phantom at a comparatively lesser spatial and depth resolution, the back-projection method, while providing higher resolution data, could not resolve concave regions in all cases which needs further investigation. Very promising preliminary results have been attained, however, with the multi-view stereo algorithm that has been implemented most recently.
KEYWORDS: Sensors, Spatial resolution, Modulation transfer functions, Spatial frequencies, In vivo imaging, Prototyping, Geometrical optics, Radio optics, Solids, Cameras
In order to validate and to optimize the imaging capabilities of a micro-lens-array (MLA) based optical detector dedicated for preclinical in-vivo small animal imaging applications a numeric investigation framework is developed. The framework is laid-out to study the following MLA detector parameters: micro-lens diameter (D) and focal length (f), as well as sensor pixel size (A). Two mathematical models are implemented for light modeling: line-based and cone-based ray projections. Since the MLA detector requires mathematical postprocessing, specifically inverse mapping for image formation, the framework is fully integrated into such approach. MLA detector designs have been studied within valid parameter ranges yielding sub-millimeter spatial resolution for in vivo imaging of mice for detector-object-distances (t) up to 50 mm. In summary, there is a non-linear dependency of the detector's spatial resolution, scaling with D and f, for any respective t. On the other hand, detector efficiency is strongly dependent on f. Regardless of mathematical postprocessing the following set of intrinsic detector parameters had been found optimal for the intended application: D = 0.336 mm, f = 4.0 mm, A = 0.048 mm.
When mathematical postprocessing is involved, particularly three-dimensional surface recognition, increasing f (cf. decreasing D) yields solid angles of the incoming rays closer to 90° and, thus, will decrease spatial depth information from the elementary images. Hence, a setup with D not larger than 0.5 mm and f between 2.0 mm and 3.0 mm is recommended.
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