Iterative Image Processing for Early Diagnostic of Beta-Amyloid Plaque Deposition in Pre-Clinical Alzheimer's Disease Studies.

PURPOSE: To test and evaluate an efficient iterative image processing strategy to improve the quality of sub-optimal pre-clinical PET images. A novel iterative resolution subsets-based method to reduce noise and enhance resolution (RSEMD) has been demonstrated on examples of PET imaging studies of Alzheimer's disease (AD) plaques deposition in mice brains. MATERIALS AND METHODS: The RSEMD method was applied to imaging studies of non-invasive detection of beta-amyloid plaque in transgenic mouse models of AD. Data acquisition utilized a Siemens Inveon® micro PET/CT device. Quantitative uptake of the tracer in control and AD mice brains was determined by counting the extent of plaque deposition by histological staining. The pre-clinical imaging software inviCRO® was used for fitting the recovery PET images to the mouse brain atlas and obtaining the time activity curves (TAC) from different brain areas. RESULTS: In all of the AD studies the post-processed images proved to have higher resolution and lower noise as compared with images reconstructed by conventional OSEM method. In general, the values of SNR reached a plateau at around 10 iterations with an improvement factor of about 2 over sub-optimal PET brain images. CONCLUSIONS: A rapidly converging, iterative deconvolution image processing algorithm with a resolution subsets-based approach RSEMD has been used for quantitative studies of changes in Alzheimer's pathology over time. The RSEMD method can be applied to sub-optimal clinical PET brain images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of AD progression at the earliest stages.

Image improvement method for positron emission mammography.

PURPOSE: To evaluate in clinical use a rapidly converging, efficient iterative deconvolution algorithm (RSEMD) for improving the quantitative accuracy of previously reconstructed breast images by a commercial positron emission mammography (PEM) scanner. MATERIALS AND METHODS: The RSEMD method was tested on imaging data from clinical Naviscan Flex Solo II PEM scanner. This method was applied to anthropomorphic like breast phantom data and patient breast images previously reconstructed with Naviscan software to determine improvements in image resolution, signal to noise ratio (SNR) and contrast to noise ratio (CNR). RESULTS: In all of the patients' breast studies the improved images proved to have higher resolution, contrast and lower noise as compared with images reconstructed by conventional methods. In general, the values of CNR reached a plateau at an average of 6 iterations with an average improvement factor of about 2 for post-reconstructed Flex Solo II PEM images. Improvements in image resolution after the application of RSEMD have also been demonstrated. CONCLUSIONS: A rapidly converging, iterative deconvolution algorithm with a resolution subsets-based approach (RSEMD) that operates on patient DICOM images has been used for quantitative improvement in breast imaging. The RSEMD method can be applied to PEM images to enhance the resolution and contrast in cancer diagnosis to monitor the tumor progression at the earliest stages.

Iterative reconstruction method for light emitting sources based on the diffusion equation.

Bioluminescent imaging (BLI) of luciferase-expressing cells in live small animals is a powerful technique for investigating tumor growth, metastasis, and specific biological molecular events. Three-dimensional imaging would greatly enhance applications in biomedicine since light emitting cell populations could be unambiguously associated with specific organs or tissues. Any imaging approach must account for the main optical properties of biological tissue because light emission from a distribution of sources at depth is strongly attenuated due to optical absorption and scattering in tissue. Our image reconstruction method for interior sources is based on the deblurring expectation maximization method and takes into account both of these effects. To determine the boundary of the object we use the standard iterative algorithm-maximum likelihood reconstruction method with an external source of diffuse light. Depth-dependent corrections were included in the reconstruction procedure to obtain a quantitative measure of light intensity by using the diffusion equation for light transport in semi-infinite turbid media with extrapolated boundary conditions.

Semi-automated Image Processing for Preclinical Bioluminescent Imaging.

OBJECTIVE: Bioluminescent imaging is a valuable noninvasive technique for investigating tumor dynamics and specific biological molecular events in living animals to better understand the effects of human disease in animal models. The purpose of this study was to develop and test a strategy behind automated methods for bioluminescence image processing from the data acquisition to obtaining 3D images. METHODS: In order to optimize this procedure a semi-automated image processing approach with multi-modality image handling environment was developed. To identify a bioluminescent source location and strength we used the light flux detected on the surface of the imaged object by CCD cameras. For phantom calibration tests and object surface reconstruction we used MLEM algorithm. For internal bioluminescent sources we used the diffusion approximation with balancing the internal and external intensities on the boundary of the media and then determined an initial order approximation for the photon fluence we subsequently applied a novel iterative deconvolution method to obtain the final reconstruction result. RESULTS: We find that the reconstruction techniques successfully used the depth-dependent light transport approach and semi-automated image processing to provide a realistic 3D model of the lung tumor. Our image processing software can optimize and decrease the time of the volumetric imaging and quantitative assessment. CONCLUSION: The data obtained from light phantom and lung mouse tumor images demonstrate the utility of the image reconstruction algorithms and semi-automated approach for bioluminescent image processing procedure. We suggest that the developed image processing approach can be applied to preclinical imaging studies: characteristics of tumor growth, identify metastases, and potentially determine the effectiveness of cancer treatment.

An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study.

PURPOSE: To study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed. MATERIALS AND METHODS: The RSEMD method was tested on in-silico, Catphan(®)500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver. RESULTS: In all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms. CONCLUSIONS: In this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.

A Multi-Camera System for Bioluminescence Tomography in Preclinical Oncology Research.

Bioluminescent imaging (BLI) of cells expressing luciferase is a valuable noninvasive technique for investigating molecular events and tumor dynamics in the living animal. Current usage is often limited to planar imaging, but tomographic imaging can enhance the usefulness of this technique in quantitative biomedical studies by allowing accurate determination of tumor size and attribution of the emitted light to a specific organ or tissue. Bioluminescence tomography based on a single camera with source rotation or mirrors to provide additional views has previously been reported. We report here in vivo studies using a novel approach with multiple rotating cameras that, when combined with image reconstruction software, provides the desired representation of point source metastases and other small lesions. Comparison with MRI validated the ability to detect lung tumor colonization in mouse lung.

Practical method for radioactivity distribution analysis in small-animal PET cancer studies.

We present a practical method for radioactivity distribution analysis in small-animal tumors and organs using positron emission tomography imaging with a calibrated source of known activity and size in the field of view. We reconstruct the imaged mouse together with a source under the same conditions, using an iterative method, Maximum likelihood expectation-maximization with system modeling, capable of delivering high-resolution images. Corrections for the ratios of geometrical efficiencies, radioisotope decay in time and photon attenuation are included in the algorithm. We demonstrate reconstruction results for the amount of radioactivity within the scanned mouse in a sample study of osteolytic and osteoblastic bone metastasis from prostate cancer xenografts. Data acquisition was performed on the small-animal PET system, which was tested with different radioactive sources, phantoms and animals to achieve high sensitivity and spatial resolution. Our method uses high-resolution images to determine the volume of organ or tumor and the amount of their radioactivity has the possibility of saving time, effort and the necessity to sacrifice animals. This method has utility for prognosis and quantitative analysis in small-animal cancer studies, and will enhance the assessment of characteristics of tumor growth, identifying metastases, and potentially determining the effectiveness of cancer treatment. The possible application for this technique could be useful for the organ radioactivity dosimetry studies.