Non-alcoholic fatty liver disease (NAFLD) starts with the accumulation of lipids in liver tissues before progressing into liver cirrhosis and hepatocellular carcinoma. Transmission-reflection optoacoustic ultrasound (TROPUS) can simultaneously interrogate biological tissues with three ultrasound-based imaging modalities based on different contrast mechanisms. We propose TROPUS imaging for the assessment of NAFLD in vivo and ex vivo. Multispectral optoacoustic tomography resolves the oxy- and deoxy-hemoglobin, lipid and melanin content in the tissues. Reflection ultrasound computed tomography facilitates segmenting the liver by providing anatomical information. Transmission ultrasound computed tomography quantifies changes in speed of sound due to lipid accumulation.
Ultrasound (US) and optoacoustic (OA) imaging provide complementary information for quantitative analysis of the tumor microenvironment. Herein, we demonstrate the unique capabilities of transmission-reflection optoacoustic ultrasound (TROPUS) for characterizing breast cancer in tumor-bearing mice. For this, 4 different mice featuring orthotopic tumor of different sizes were scanned with a full-ring ultrasound transducer array to simultaneously render pulse-echo US images, speed of sound (SoS) maps and OA images. The tumor size, vascular density and its elastic parameters were further quantified in the images. Our results pave the way toward clinical translation of the hybrid TROPUS imaging for tumor detection and characterization.
We report on a new small animal imaging platform for concomitant noninvasive mapping of the absorbed optical energy, acoustic reflectivity, speed of sound and acoustic attenuation in whole mice with submillimeter resolution. In vivo mouse imaging experiments revealed fine details on the organ parenchyma, vascularization, tissue reflectivity, density and stiffness. The newly developed synergistic multimodal combination offers unmatched capabilities for imaging diverse tissue properties and biomarkers with high resolution, penetration and contrast.
Most current Positron Emission Tomography (PET) scanners use pixelated detector crystals, and the crystal pitch limits the sampling and the image resolution. In this paper we present a maximum-likelihood based method to go beyond the existing discrete sampling in PET scanners. After an initial standard image reconstruction, the projection of the reconstructed image is used to redistribute the counts of each original LOR among several subLORs. The new dataset with increased sampling is reconstructed again, obtaining improved image resolution without increasing the noise. The procedure can be repeated several times for further improvements, being each reconstruction a super-iteration. We validated the method with data acquired with the preclinical Super Argus PET/CT scanner. We used the NEMA NU4- 2008 for the Super Argus PET/CT scanner to quantitatively measure the image quality improvement, which resulted in a Recovery Coefficient (RC) increase of 14% for the smallest rod. Results with in-vivo acquisitions of a rat cardiac study injected with FDG also confirm the improvement in image quality. The proposed method can be considered a generalization of standard reconstruction algorithms, which is able to achieve better images at the expense of increasing the reconstruction time.
KEYWORDS: Positron emission tomography, Monte Carlo methods, Image restoration, Scanners, Matrices, 3D image processing, Image quality, Testing and analysis, Reconstruction algorithms, Medical imaging
Real-time Positron Emission Tomography (PET) has the potential to become a new imaging tool providing useful information, such as first-shot images, medical intervention guidance, information about patient position and motion, and to perform PET image guided biopsy. Fully-3D iterative reconstruction methods in PET provide highest quality images, but they are still not suitable for real-time imaging due to their large computational time requirements. On the other hand, analytical methods are much faster, but they exhibit low-quality images and artifacts when using noisy or incomplete data. We propose an alternative reconstruction method based on the pseudoinverse of the System Response Matrices (SRM), which can be very fast while yielding good quality images. The reconstruction problem is separated into two independent ones. First, the axial part of the SRM is pseudoinverted and used to rebin in the axial direction 3D data into 2D datasets with resolution recovery. The resulting 2D datasets can be reconstructed with standard analytical methods such as Filtered Back-Projection (FBP), or with another in-plane pseudoinverse algorithm. Pseudoinverse rebinning is as fast as standard Single Slice ReBinning (SSRB), but with image quality comparable to FOurierREbinning (FORE). With regards to the transaxial image reconstruction, pseudoinverse rebinning is as fast as FBP, but obtains improved resolution recovery and uniformity. Overall, the two-step psudoinverse reconstruction yields much more acceptable images than SSRB+FBP, at a rate of several frames per second, compatible with real time applications.
Dynamic PET imaging is usually performed dividing the acquired data into time frames which are reconstructed independently and then fitted using a kinetic model. This approach requires many image reconstructions, and data corrections, and the use of short frames usually produces noisy images with significant positive bias. In this work we propose to use a generalized version of the method of moments (MoM), already in use in other fields such as fluorescence decay studies, to address these problems. In the MoM, the events of the list-mode data are weighted based on the time they were detected and stored in sinograms. These sinograms are reconstructed with standard algorithms, and the dynamic parameters of interest are derived from the resulting images using algebraic relations, which depend on the specific dynamic model and selected set of weights. The method was evaluated with data from preclinical and clinical scanners with several dynamical studies such as a decaying 13N phantom acquired with the Biograph TP scanner and a PatLak analysis in the myocardium region of a mouse injected with 18F-FDG, reaching in all cases similar results to the ones obtained using frames. We also successfully tested the MoM with more complex dynamic models with simulated data obtained with dPETSTEP. In summary, the MoM applied to dynamic PET has the potential to be a very effective way to reduce the computational cost and bias in many different studies.
The presence of motion during the relatively long PET acquisitions is a very common problem, especially with awake animals, infants and patients with neurological disorders. External motion can be detected based on the optical tracking of markers placed on the skin of the patient, but it needs additional hardware and a somehow complex integration with the PET data. The possibility of motion detection directly from the acquired PET data would overcome these limitations. In this work, we propose the use of the centroid of lines of response to identify long motion free frames (more than 2.5 seconds). In these frames we identify in real-time the location of 18F markers placed on the head of the rat with the radiotracer labeled with 18F. We evaluated the performance of the proposed method in a preclinical PET/CT scanner with an awake rat injected with 600 μCi and four 18F sources attached in its head. After solid rigid motion compensation, we reconstruct an image that use 70% events of the acquisition, and the resolution is comparable with the motion-free frames.
The reconstruction of acoustic attenuation maps for transmission Ultrasound Computed Tomography (USCT) based on
the standard least-squares full wave inversion method requires the accurate knowledge of the sound speed map in the
region under study. Any deviation in the reconstructed speed maps creates a very significant bias in the attenuation map,
as the standard least-squares misfit function is more sensitive to time misalignments than to amplitude differences of the
signals. In this work, we propose a generalized misfit function which includes an additional term that accounts for the
amplitude differences between the measured and the estimated signals. The functional gradients used to minimize the
proposed misfit function were obtained using an adjoint field formulation and the fractional Laplacian wave equation.
The forward and backward wave propagation was obtained with the parallelized GPU version of the software k-Wave
and the optimization was performed with a line search method. A numerical phantom simulating breast tissue and
synthetic noisy data were used to test the performance of the proposed misfit function. The attenuation was reconstructed
based on a converged speed map. An edge-preserving regularization method based on total variation was also
implemented. To quantify the quality of the results, the mean values and their standard deviations in several regions of
interest were analyzed and compared to the reference values. The proposed generalized misfit function decreases
considerably the bias in the attenuation map caused by the deviations in the speed map in all the regions of interest
analyzed.
MRI-based bone segmentation is a challenging task because bone tissue and air both present low signal intensity on MR images, making it difficult to accurately delimit the bone boundaries. However, estimating bone from MRI images may allow decreasing patient ionization by removing the need of patient-specific CT acquisition in several applications. In this work, we propose a fast GPU-based pseudo-CT generation from a patient-specific MRI T1-weighted image using a group-wise patch-based approach and a limited MRI and CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel with the patches of all MR images in the database, which lie in a certain anatomical neighborhood. The pseudo-CT is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a GPU. The use of patch-based techniques allows a fast and accurate estimation of the pseudo-CT from MR T1-weighted images, with a similar accuracy as the patient-specific CT. The experimental normalized cross correlation reaches 0.9324±0.0048 for an atlas with 10 datasets. The high NCC values indicate how our method can accurately approximate the patient-specific CT. The GPU implementation led to a substantial decrease in computational time making the approach suitable for real applications.
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