Biomedical subjects

Jinyi Qi

Publications and source records attributed to Jinyi Qi.

15 recordsLinked to original sources

PubMed2007

Fabrication and characterization of a 0.5-mm lutetium oxyorthosilicate detector array for high-resolution PET applications.

UNLABELLED: With the increasing use of in vivo imaging in mouse models of disease, there are many interesting applications that demand imaging of organs and tissues with submillimeter resolution. Though there are other contributing factors, the spatial resolution in small-animal PET is still largely determined by the detector pixel dimensions. METHODS: In this work, a pair of lutetium oxyorthosilicate (LSO) arrays with 0.5-mm pixels was coupled to multichannel photomultiplier tubes and evaluated for use as high-resolution PET detectors. RESULTS: Flood histograms demonstrated that most crystals were clearly identifiable. Energy resolution varied from 22% to 38%. The coincidence timing resolution was 1.42-ns full width at half maximum (FWHM). The intrinsic spatial resolution was 0.68-mm FWHM as measured with a 30-gauge needle filled with (18)F. The improvement in spatial resolution in a tomographic setting is demonstrated using images of a line source phantom reconstructed with filtered backprojection and compared with images obtained from 2 dedicated small-animal PET scanners. Finally, a projection image of the mouse foot is shown to demonstrate the application of these 0.5-mm LSO detectors to a biologic task. CONCLUSION: A pair of highly pixelated LSO detections has been constructed and characterized for use as high-spatial-resolution PET detectors. It appears that small-animal PET systems capable of a FWHM spatial resolution of 600 microm or less are feasible and should be pursued.

Animals↗

PubMed2006

Penalized maximum-likelihood image reconstruction for lesion detection.

Detecting cancerous lesions is one major application in emission tomography. In this paper, we study penalized maximum-likelihood image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modelling the photon detection process and measurement noise in imaging systems. To explore the full potential of penalized maximum-likelihood image reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters to improve lesion detectability. We conducted computer-based Monte Carlo simulations to compare the proposed penalty function, conventional penalty function, and a penalty function for isotropic point spread function. The lesion detectability is measured by a channelized Hotelling observer. The results show that the proposed penalty function outperforms the other penalty functions for lesion detection. The relative improvement is dependent on the size of the lesion. However, we found that the penalty function optimized for a 5 mm lesion still outperforms the other two penalty functions for detecting a 14 mm lesion. Therefore, it is feasible to use the penalty function designed for small lesions in image reconstruction, because detection of large lesions is relatively easy.

Algorithms↗

PubMed2006

Iterative reconstruction techniques in emission computed tomography.

In emission tomography statistically based iterative methods can improve image quality relative to analytic image reconstruction through more accurate physical and statistical modelling of high-energy photon production and detection processes. Continued exponential improvements in computing power, coupled with the development of fast algorithms, have made routine use of iterative techniques practical, resulting in their increasing popularity in both clinical and research environments. Here we review recent progress in developing statistically based iterative techniques for emission computed tomography. We describe the different formulations of the emission image reconstruction problem and their properties. We then describe the numerical algorithms that are used for optimizing these functions and illustrate their behaviour using small scale simulations.

Algorithms↗

PubMed2006

Rapamycin inhibits growth of premalignant and malignant mammary lesions in a mouse model of ductal carcinoma in situ.

PURPOSE: Rapamycin has been shown to have antitumor effects in various tumor models. To study the effect of rapamycin at different stages of breast cancer development, we used two unique mouse models of breast cancer with activated phosphatidylinositol 3-kinase (PI3K) pathway. Met-1 tumors are highly invasive and metastatic, and mammary intraepithelial neoplasia-outgrowths (MIN-O), a model for human ductal carcinoma in situ, are transplantable premalignant mammary lesions that develop invasive carcinoma with predictable latencies. Both of these models were derived from mammary lesions in Tg(MMTV-PyV-mT) mice. EXPERIMENTAL DESIGN: Met-1 tumors were used to study the effect of rapamycin treatment on invasive disease. Transplanted MIN-O model was used to study the effect of rapamycin on premalignant mammary lesions. Animals were in vivo micro-positron emission tomography imaged to follow the lesion growth and transformation to tumor during the treatment. Cell proliferation, angiogenesis, and apoptosis was assayed by immunohistochemistry. RESULTS: Rapamycin inhibited in vitro tumor cell proliferation and in vivo Met-1 tumor growth. The growth inhibition was correlated with dephosphorylation of mammalian target of rapamycin (mTOR) targets. Rapamycin treatment significantly reduced the growth of the premalignant MIN-O lesion, as well as tumor incidence and tumor burden. Growth inhibition was associated with reduced cell proliferation and angiogenesis and increased apoptosis. CONCLUSIONS: In PyV-mT mouse mammary models, rapamycin inhibits the growth of premalignant lesions and invasive tumors. Although the inhibitory effect of rapamycin was striking, rapamycin treatment did not completely obliterate the lesions.

Animals↗

PubMed2006

Theoretical study of penalized-likelihood image reconstruction for region of interest quantification.

Region of interest (ROI) quantification is an important task in emission tomography (e.g., positron emission tomography and single photon emission computed tomography). It is essential for exploring clinical factors such as tumor activity, growth rate, and the efficacy of therapeutic interventions. Statistical image reconstruction methods based on the penalized maximum-likelihood (PML) or maximum a posteriori principle have been developed for emission tomography to deal with the low signal-to-noise ratio of the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the regularization parameter in PML reconstruction controls the resolution and noise tradeoff and, hence, affects ROI quantification. In this paper, we theoretically analyze the performance of ROI quantification in PML reconstructions. Building on previous work, we derive simplified theoretical expressions for the bias, variance, and ensemble mean-squared-error (EMSE) of the estimated total activity in an ROI that is surrounded by a uniform background. When the mean and covariance matrix of the activity inside the ROI are known, the theoretical expressions are readily computable and allow for fast evaluation of image quality for ROI quantification with different regularization parameters. The optimum regularization parameter can then be selected to minimize the EMSE. Computer simulations are conducted for small ROIs with variable uniform uptake. The results show that the theoretical predictions match the Monte Carlo results reasonably well.

Algorithms↗

PubMed2006

Simultaneous acquisition of multislice PET and MR images: initial results with a MR-compatible PET scanner.

UNLABELLED: PET and MRI are powerful imaging techniques that are largely complementary in the information they provide. We have designed and built a MR-compatible PET scanner based on avalanche photodiode technology that allows simultaneous acquisition of PET and MR images in small animals. METHODS: The PET scanner insert uses magnetic field-insensitive, position-sensitive avalanche photodiode (PSAPD) detectors coupled, via short lengths of optical fibers, to arrays of lutetium oxyorthosilicate (LSO) scintillator crystals. The optical fibers are used to minimize electromagnetic interference between the radiofrequency and gradient coils and the PET detector system. The PET detector module components and the complete PET insert assembly are described. PET data were acquired with and without MR sequences running, and detector flood histograms were compared with the ones generated from the data acquired outside the magnet. A uniform MR phantom was also imaged to assess the effect of the PET detector on the MR data acquisition. Simultaneous PET and MRI studies of a mouse were performed ex vivo. RESULTS: PSAPDs can be successfully used to read out large numbers of scintillator crystals coupled through optical fibers with acceptable performance in terms of energy and timing resolution and crystal identification. The PSAPD-LSO detector performs well in the 7-T magnet, and no visible artifacts are detected in the MR images using standard pulse sequences. CONCLUSION: The first images from the complete system have been successfully acquired and reconstructed, demonstrating that simultaneous PET and MRI studies are feasible and opening up interesting possibilities for dual-modality molecular imaging studies.

Anatomy, Cross-Sectional↗

PubMed2005

Effect of errors in the system matrix on maximum a posteriori image reconstruction.

Statistically based iterative image reconstruction methods have been developed for emission tomography. One important component in iterative image reconstruction is the system matrix, which defines the mapping from the image space to the data space. Several groups have demonstrated that an accurate system matrix can improve image quality in both single photon emission computed tomography (SPECT) and positron emission tomography (PET). While iterative methods are amenable to arbitrary and complicated system models, the true system response is never known exactly. In practice, one also has to sacrifice the accuracy of the system model because of limited computing and imaging resources. This paper analyses the effect of errors in the system matrix on iterative image reconstruction methods that are based on the maximum a posteriori principle. We derived an analytical expression for calculating artefacts in a reconstructed image that are caused by errors in the system matrix using the first-order Taylor series approximation. The theoretical expression is used to determine the required minimum accuracy of the system matrix in emission tomography. Computer simulations show that the theoretical results work reasonably well in low-noise situations.

Algorithms↗

PubMed2005

An approximate short scan helical FDK cone beam algorithm based on nutating curved surfaces satisfying the Tuy's condition.

Traditionally, short scan helical FDK algorithms have been implemented based on horizontal transaxial slices. However, not every point on the horizontal transaxial slice satisfies Tuy's condition for the corresponding (pi+fan angle) segment of helix, which means that some points on the horizontal slices are incompletely sampled and are impossible to be exactly reconstructed. In this paper, we propose and implement an improved but still approximate short scan helical cone beam FDK algorithm based on nutating curved surfaces satisfying the Tuy's condition. This surface is defined by averaging PI surfaces emanating the initial and final source points of a (pi+fan angle) segment of helix. One of the key characteristics of the surface is that every point on it satisfies the Tuy's condition for the corresponding (pi+fan angle) segment of helix, which means that we can potentially reconstruct every point on the surface exactly. This difference makes the proposed algorithm deliver a better-reconstructed image quality while requiring a smaller detector area than that of traditional FDK methods based on horizontal transaxial slices. Another characteristic of the proposed surface is that every point within the helix belongs to one and only one such surface. Therefore, the location of the short scan segment for the reconstruction of a point in Cartesian coordinate could be precalculated and stored in a look-up table. This enables us to perform reconstruction directly on rectangular grids. We compare the performance of the improved FDK algorithm with that of a quasi-exact algorithm based on data combination technique. The simulation results show that the reconstructed image quality of these two methods is similar.

Algorithms↗

PubMed2004

Analysis of lesion detectability in Bayesian emission reconstruction with nonstationary object variability.

Bayesian methods based on the maximum a posteriori principle (also called penalized maximum-likelihood methods) have been developed to improve image quality in emission tomography. To explore the full potential of Bayesian reconstruction for lesion detection, we derive simplified theoretical expressions that allow fast evaluation of the detectability of a lesion in Bayesian reconstruction. This work is builded on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers. We explicitly model the nonstationary variation of the lesion and background without assuming that they are locally stationary. The results can be used to choose the optimum prior parameters for the maximum lesion detectability. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predictions and the Monte Carlo results. We also demonstrate that the lesion detectability can be reliably estimated using one noisy data set.

Algorithms↗

PubMed2004

Propagation of errors from the sensitivity image in list mode reconstruction.

List mode image reconstruction is attracting renewed attention. It eliminates the storage of empty sinogram bins. However, a single back projection of all LORs is still necessary for the pre-calculation of a sensitivity image. Since the detection sensitivity is dependent on the object attenuation and detector efficiency, it must be computed for each study. Exact computation of the sensitivity image can be a daunting task for modern scanners with huge numbers of LORs. Thus, some fast approximate calculation may be desirable. In this paper, we analyze the error propagation from the sensitivity image into the reconstructed image. The theoretical analysis is based on the fixed point condition of the list mode reconstruction. The nonnegativity constraint is modeled using the Kuhn-Tucker condition. With certain assumptions and the first-order Taylor series approximation, we derive a closed form expression for the error in the reconstructed image as a function of the error in the sensitivity image. The result shows that the error response is frequency-dependent and provides a simple expression for determining the required accuracy of the sensitivity image calculation. Computer simulations show that the theoretical results are in good agreement with the measured results.

Algorithms↗

PubMed2004

Accurate estimation of the Fisher information matrix for the PET image reconstruction problem.

The Fisher information matrix (FIM) plays a key role in the analysis and applications of statistical image reconstruction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform image resolution in low count situations.

Algorithms↗

PubMed2003

A unified noise analysis for iterative image estimation.

Iterative image estimation methods have been widely used in emission tomography. Accurate estimation of the uncertainty of the reconstructed images is essential for quantitative applications. While both iteration-based noise analysis and fixed-point noise analysis have been developed, current iteration-based results are limited to only a few algorithms that have an explicit multiplicative update equation and some may not converge to the fixed-point result. This paper presents a theoretical noise analysis that is applicable to a wide range of preconditioned gradient-type algorithms. Under a certain condition, the proposed method does not require an explicit expression of the preconditioner. By deriving the fixed-point expression from the iteration-based result, we show that the proposed iteration-based noise analysis is consistent with fixed-point analysis. Examples in emission tomography and transmission tomography are shown. The results are validated using Monte Carlo simulations.

Algorithms↗

PubMed2003

Theoretical evaluation of the detectability of random lesions in Bayesian emission reconstruction.

Detecting cancerous lesion is an important task in positron emission tomography (PET). Bayesian methods based on the maximum a posteriori principle (also called penalized maximum likelihood methods) have been developed to deal with the low signal to noise ratio in the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the prior parameters in Bayesian reconstruction control the resolution and noise trade-off and hence affect detectability of lesions in reconstructed images. Bayesian reconstructions are difficult to analyze because the resolution and noise properties are nonlinear and object-dependent. Most research has been based on Monte Carlo simulations, which are very time consuming. Building on the recent progress on the theoretical analysis of image properties of statistical reconstructions and the development of numerical observers, here we develop a theoretical approach for fast computation of lesion detectability in Bayesian reconstruction. The results can be used to choose the optimum hyperparameter for the maximum lesion detectability. New in this work is the use of theoretical expressions that explicitly model the statistical variation of the lesion and background without assuming that the object variation is (locally) stationary. The theoretical results are validated using Monte Carlo simulations. The comparisons show good agreement between the theoretical predications and the Monte Carlo results.

Algorithms↗

PubMed2002

Scatter correction for positron emission mammography.

In this paper we present a scatter correction method for a regularized list mode maximum likelihood reconstruction algorithm for the positron emission mammograph (PEM) that is being developed at our laboratory. The scatter events inside the object are modelled as additive Poisson random variables in the forward model of the reconstruction algorithm. The mean scatter sinogram is estimated using a Monte Carlo simulation program. With the assumption that the background activity is nearly uniform, the Monte Carlo scatter simulation only needs to run once for each PEM configuration. This saves computation time. The crystal scatters are modelled as a shift-invariant blurring in image domain because they are more localized. Thus, the useful information in the crystal scatters can be deconvolved in high-resolution reconstructions. The propagation of the noise from the estimated scatter sinogram into the reconstruction is analysed theoretically. The results provide an easy way to calculate the required number of events in the Monte Carlo scatter simulation for a given noise level in the image. The analysis is also applicable to other scatter estimation methods, provided that the covariance of the estimated scatter sinogram is available.

Algorithms↗

PubMed2002

Spatiotemporal reconstruction of list-mode PET data.

We describe a method for computing a continuous time estimate of tracer density using list-mode positron emission tomography data. The rate function in each voxel is modeled as an inhomogeneous Poisson process whose rate function can be represented using a cubic B-spline basis. The rate functions are estimated by maximizing the likelihood of the arrival times of detected photon pairs over the control vertices of the spline, modified by quadratic spatial and temporal smoothness penalties and a penalty term to enforce nonnegativity. Randoms rate functions are estimated by assuming independence between the spatial and temporal randoms distributions. Similarly, scatter rate functions are estimated by assuming spatiotemporal independence and that the temporal distribution of the scatter is proportional to the temporal distribution of the trues. A quantitative evaluation was performed using simulated data and the method is also demonstrated in a human study using 11C-raclopride.

Algorithms↗