It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.
Computational speed and available memory size on a single processor are two limiting factors when using the
frequency-domain equation of radiative transport (FD-ERT) as a forward and inverse model to reconstruct
three-dimensional (3D) tomographic images. In this work, we report on a parallel, multiprocessor reducedspace
sequential quadratic programming (RSQP) approach to improve computational speed and reduce memory
requirement. To evaluate and quantify the performance of the code, we performed simulation studies employing
a 3D numerical mouse model. Furthermore, we tested the algorithm with experimental data obtained from
tumor bearing mice.
KEYWORDS: Digital signal processing, Imaging systems, Breast, Signal processing, Optical fibers, Signal detection, Breast cancer, Breast imaging, Sensors, Tissue optics
Breast cancer characteristics such as angiogenesis and hypoxia can be quantified by using optical
tomography imaging to observe the hemodynamic response to an external stimulus. A digital near-infrared
tomography system has been developed specifically for the purpose of dynamic breast imaging. It
simultaneously acquires four frequency encoded wavelengths of light at 765, 808, 827, and 905nm in order
to facilitate the functional imaging of oxy- and deoxy-hemoglobin, lipid concentration and water content.
The system uses 32 source fibers to simultaneously illuminate both breasts. There are 128 detector fibers,
64 fibers for each breast, which deliver the detected light to silicon photo-detectors. The signal is
conditioned by variable gain amplifiers and filters and is quantized by an analog to digital converter
(ADC). The sampled signal is then passed on for processing using a Digital Signal Processor (DSP) prior
to display on a host computer. The system can acquire 2.23 frames per second with a dynamic range of
236 dB.
We present an instrument for simultaneous imaging of the rodent brain with frequency-domain optical tomography and
magnetic resonance imaging. The instrument uses a custom-built fiber optic probe that allows for measurements in backreflectance
geometry. The probe consists of 13 source and 26 detector fibers and is small enough to fit inside of a microMRI RF coil with an inner diameter of 38mm. Illumination by the source fibers is time demultiplexed by an optical fiber switch. A gain-modulated image intensifier CCD camera focuses onto the endpoints of large-core gradedindex detector fibers and collects the frequency-domain data. Imaging can be performed with source-modulation frequencies up to 1 GHz. The instrument is capable of acquiring multi-frequency optical tomography data at 2 wavelengths, and the data can be used to generate 3D maps of hemoglobin concentrations. At the same time magnetic resonance images can be acquired with in-plane resolution smaller than 100 micron. To illustrate the performance of the instrument we show results of small animal studies that involve inhalation of 100% carbogen and chemically induced seizures.
KEYWORDS: Digital signal processing, Signal processing, Digital filtering, Analog electronics, Imaging systems, Sensors, Filtering (signal processing), Modulation, Signal detection, Optical filters
We describe a new dynamic optical tomography system that is, unlike currently available analog instrumentation, based on digital data-acquisition and filtering techniques. At the heart of this continuous wave instrument is a digital signal processor (DSP) that collects, collates, processes, and filters the digitized data set. A digital lock-in filter that has been designed for this particular application maximizes measurement fidelity. The synchronously-timed processes are controlled by a complex programmable logic device (CPLD) that is also used in conjunction with the DSP to orchestrate data flow. Real-time data rates as high as 140Hz can be achieved. The operation of the system is implemented through a graphical user interface designed with LabVIEW software, Performance analysis shows very low system noise (~600fW RMS noise equivalent power), excellent signal precision (<0.04% - 0.2%) and long-term system stability (<1% over 40 min). A large dynamic range (~195dB) accommodates a wide scope of measurement geometries and tissue types. First experiments on tissue phantoms show that dynamic behavior is accurately captured and spatial location can be correctly tracked using this system.
KEYWORDS: Digital signal processing, Signal processing, Sensors, Analog electronics, Signal detection, Data acquisition, Tomography, Imaging systems, Filtering (signal processing), Interference (communication)
In this paper we present a novel application of digital detection and data-acquisition techniques to a prototype
dynamic optical tomography system. The core component is a digital signal processor (DSP) that is responsible for
collecting and processing the digitized data set. Utilizing the processing power of the DSP, real-time data rates for
this 16-source, 32-detector system, can be achieved at rates as high as 140Hz per tomographic frame. Many of the
synchronously-timed processes are controlled by a complex programmable logic device (CPLD) that is used in
conjunction with the DSP to orchestrate data flow. The operation of the instrument is managed through a
comprehensive graphical user interface, which was designed using the LabVIEW software package. Performance
analysis demonstrates very low system noise (~.60pW RMS noise equivalent power) and excellent signal precision
(<0.1%) for most practical cases. First experiments on tissue phantoms show that dynamic behavior can be
accurately captured using this system.
We present a design for frequency domain instrument that allows for simultaneous gathering of magnetic resonance and
diffuse optical tomographic imaging data. This small animal imaging system combines the high anatomical resolution of
magnetic resonance imaging (MRI) with the high temporal resolution and physiological information provided by diffuse
optical tomography (DOT). The DOT hardware comprises laser diodes and an intensified CCD camera, which are
modulated up to 1 GHz by radio frequency (RF) signal generators. An optical imaging head is designed to fit inside the
4 cm inner diameter of a 9.4 T MRI system. Graded index fibers are used to transfer light between the optical hardware
and the imaging head within the RF coil. Fiducial markers are integrated into the imaging head to allow the
determination of the positions of the source and detector fibers on the MR images and to permit co-registration of MR
and optical tomographic images. Detector fibers are arranged compactly and focused through a camera lens onto the
photocathode of the intensified CCD camera.
It is well know that the inverse problem in optical tomography is highly ill-posed. The image reconstruction
process is often unstable and non-unique, because the number of the boundary measurements data is far fewer
than the number of the unknown parameters (optical properties) to be reconstructed. To overcome this problem
one can either increase the number of measurement data (e.g. multi-spectral or multi-frequency methods), or
reduce the number of unknows (e.g. using prior structural information from other imaging modalities). In
this paper, we introduce a novel approach for reducing the unknown parameters in the reconstruction process.
The discrete cosine transform (DCT), which has long been used in image compression, is here employed to
parameterize the reconstructed image. In general, only a few DCT coefficient are needed to describe the main
features in an image, and the number of unknowns in the image reconstruction process can be drastically
reduced. Numerical as well as experimental examples are shown that illustrate the performance of the new
code.
Small animal models are employed to simulate disease in humans and to study its progression, what factors are
important to the disease process, and to study the disease treatment. Biomedical imaging modalities such as magnetic
resonance imaging (MRI) and Optical Tomography make it possible to non-invasively monitor the progression of
diseases in living small animals and study the efficacy of drugs and treatment protocols. MRI is an established imaging
modality capable of obtaining high resolution anatomical images and along with contrast agents allow the studying of
blood volume. Optical tomography, on the other hand, is an emerging imaging modality, which, while much lower in
spatial resolution, can separate the effects of oxyhemoglobin, deoxyhemoglobin, and blood volume with high temporal
resolution. In this study we apply these modalities to imaging the growth of kidney tumors and then there treatment by
an anti-VEGF agent. We illustrate how these imaging modalities have their individual uses, but can still supplement
each other and cross validation can be performed.
With the advent of small animal imaging systems, it has become possible to non-invasively monitor the progression of diseases in living small animals and study the efficacy of drugs and treatment protocols. Magnetic resonance imaging (MRI) is an established imaging modality capable of obtaining high resolution anatomical images as well as studying cerebral blood volume (CBV), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO2). Optical tomography, on the other hand, is an emerging imaging modality, which, while much lower in spatial resolution and insensitive to CBF, can separate the effects of oxyhemoglobin, deoxyhemoglobin, and CBV with high temporal resolution. In this study we present our first results concerning coregistration of MRI and optical data. By applying both modalities to imaging of kidney tumors in mice that undergo VEGF treatment, we illustrate how these imaging modalities can supplement each other and cross validation can be performed.
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