Noninvasive measurement of regional cerebral blood flow change with H(2)(15)O and positron emission tomography using a mechanical injector and a standard arterial input function

To estimate changes in regional cerebral blood flow (rCBF) without arterial sampling in the study of functional-anatomical correlations in the human brain, using (15)O-labeled water and PET, a standard arterial input function was generated from the input function in 10 normal volunteers with dose calibration and peak time normalization. The speed and volume of injection were precisely controlled with a mechanical injector. After global normalization of each tissue activity image, the standard arterial input function was applied to obtain estimated CBF images. Relative changes in estimated rCBF to whole brain mean CBF(DeltaFest) and those in regional tissue activity (DeltaC) were compared with true relative rCBF changes (DeltaF) in 40 pairs of images obtained from 6 normal volunteers. DeltaFest correlated well with DeltaF, whereas DeltaC consistently underestimated DeltaF. This noninvasive method simplifies the activation studies and provides the accurate estimation of relative flow changes.     

Dynamic image data compression in the spatial and temporal domains: clinical issues and assessment

In our previous work, we developed a novel approach to dynamic image data compression, and demonstrated that very high compression ratios can be achieved while preserving relevant kinetic information. However, the technique has not yet been assessed with clinical data. Many issues need to be addressed to tailor the method for clinical use. In this paper, we apply the compression technique to dynamic [18F] 2-fluoro-deoxy-glucose (FDG) brain positron emission tomography (PET) data, using a five-parameter model to include cerebral blood volume (CBV) and partial volume (PV) effects. Functional images generated from the compressed data are compared with those from the original uncompressed data. We show that the storage requirements for a typical clinical dynamic PET image data set can be reduced by more than 95%, without degradation of image quality. Furthermore, the technique greatly reduces the computational complexity of further clinical image postprocessing such as smoothing and generation of functional images. It is expected that the compression technique will be of benefit in image data management and telemedicine.     

Simultaneous estimation of physiological parameters and the inputfunction - in vivo PET data

Dynamic imaging with positron emission tomography (PET) is widely used for the in-vivo measurement of the regional cerebral metabolic rate for glucose (rCMRGlc) with [¹⁸F]fluorodeoxy-D-glucose (FDG), and is used for the clinical evaluation of neurological diseases. However, in addition to the acquisition of dynamic images, continuous arterial blood sampling is the conventional method of obtaining the tracer time-activity curve in blood (or plasma) for the numerical estimation of rCMRGlc in mg glucose/100 g tissue/min. The insertion of arterial lines and the subsequent collection and processing of multiple blood samples are impractical for clinical PET studies because it is invasive, it has the remote (but real) potential for producing limb ischemia, and it exposes personnel to additional radiation and the risks associated with handling blood. Based on a method for extracting kinetic parameters from dynamic PET images, we developed a modified version (post-estimation method) to improve the numerical identifiability of the parameter estimates when we deal with data obtained from clinical studies. We applied both methods to dynamic neurological FDG PET studies in three adults. We found that the input function and parameter estimates obtained with our noninvasive methods agreed well with those estimated from the gold-standard method of arterial blood sampling and that rCMRGlc estimates were highly correlated. No significant difference was found between rCMRGlc estimated by our methods and the gold-standard method. We suggest that our proposed noninvasive methods may offer an advance over existing methods     

A Factor-Image Framework to Quantification of Brain Receptor Dynamic PET Studies

The positron emission tomography (PET) imaging technique enables the measurement of receptor distribution or neurotransmitter release in the living brain and the changes of the distribution with time and thus allows quantification of binding sites as well as the affinity of a radioligand. However, quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements (i.e., voxels, pixels). This effect is caused by a limited spatial resolution of the PET scanner. Spatial heterogeneity is often essential in understanding the underlying receptor binding process. Tracer kinetic modeling also often requires an intrusive collection of arterial blood samples. In this paper, we propose a likelihood-based framework in the voxel domain for quantitative imaging with or without the blood sampling of the input function. Radioligand kinetic parameters are estimated together with the input function. The parameters are initialized by a subspace-based algorithm and further refined by an iterative likelihood-based estimation procedure. The performance of the proposed scheme is examined by simulations. The results show that the proposed scheme provides reliable estimation of factor time-activity curves (TACs) and the underlying parametric images. A good match is noted between the result of the proposed approach and that of the Logan plot. Real brain PET data are also examined, and good performance is observed in determining the TACs and the underlying factor images.     

Iterative principal axes registration method for analysis of MR-PETbrain images

Computerized automatic registration of MR-PET images of the brain is of significant interest for multimodality brain image analysis. Here, the authors discuss the principal axes transformation for registration of three-dimensional MR and PET images. A new brain phantom designed to test MR-PET registration accuracy determines that the principal axes registration (PAR) method is accurate to within an average of 1.37 mm with a standard deviation of 0.78 mm. Often the PET scans are not complete in the sense that the PET volume does not match the respective MR volume. The authors have developed an iterative PAR (IPAR) algorithm for such cases. Partial volumes of PET can be accurately registered to the complete MR volume using the new iterative algorithm. The quantitative and qualitative analyses of MR-PET image registration are presented and discussed. Results show that the new PAR algorithm is accurate and practical in MR-PET correlation studies     

Segmentation of VOI from multidimensional dynamic PET images by integrating spatial and temporal features.

Segmentation of multidimensional dynamic positron emission tomography (PET) images into volumes of interest (VOIs) exhibiting similar temporal behavior and spatial features is a challenging task due to inherently poor signal-to-noise ratio and spatial resolution. In this study, we propose VOI segmentation of dynamic PET images by utilizing both the three-dimensional (3-D) spatial and temporal domain information in a hybrid technique that integrates two independent segmentation techniques of cluster analysis and region growing. The proposed technique starts with a cluster analysis that partitions the image based on temporal similarities. The resulting temporal partitions, together with the 3-D spatial information are utilized in the region growing segmentation. The technique was evaluated with dynamic 2-[18F] fluoro-2-deoxy-D-glucose PET simulations and clinical studies of the human brain and compared with the k-means and fuzzy c-means cluster analysis segmentation methods. The quantitative evaluation with simulated images demonstrated that the proposed technique can segment the dynamic PET images into VOIs of different kinetic structures and outperforms the cluster analysis approaches with notable improvements in the smoothness of the segmented VOIs with fewer disconnected or spurious segmentation clusters. In clinical studies, the hybrid technique was only superior to the other techniques in segmenting the white matter. In the gray matter segmentation, the other technique tended to perform slightly better than the hybrid technique, but the differences did not reach significance. The hybrid technique generally formed smoother VOIs with better separation of the background. Overall, the proposed technique demonstrated potential usefulness in the diagnosis and evaluation of dynamic PET neurological imaging studies.     

Dynamic image data compression in spatial and temporal domains:theory and algorithm

Advanced medical imaging requires storage of large quantities of digitized clinical data. These data must be stored in such a way that their retrieval does not impair the clinician's ability to make a diagnosis. We propose a theory and algorithm for near lossless dynamic image data compression. Taking advantage of domain-specific knowledge related to medical imaging, medical practice and the dynamic imaging modality, a compression ratio greater than 80:1 is achieved. The high compression ratios are achieved by the proposed algorithm through three stages: (1) addressing temporal redundancies in the data through application of image optimal sampling, (2) addressing spatial redundancies in the data through cluster analysis, and (3) efficient coding of image data using standard still-image compression techniques. To illustrate the practicality of the algorithm, a simulated positron emission tomography (PET) study using the fluoro-deoxy-glucose (FDG) tracer is presented. Realistic dynamic image data are generated by virtual scanning of a simulated brain phantom as a real PET scanner. These data are processed using the conventional and proposed algorithms as well as the techniques for storage and analysis. The resulting parametric images obtained from the conventional and proposed approaches are subsequently compared to evaluate the proposed compression algorithm. The storage space for dynamic image data reduced by more than 95%, without loss in diagnostic quality. Therefore, the proposed theory and algorithm are expected to be very useful in medical image database management and telecommunication     

A technique for extracting physiological parameters and therequired input function simultaneously from PET image measurements:theory and simulation study

Positron emission tomography (PET) is an important tool for enabling quantification of human brain function. However, quantitative studies using tracer kinetic modeling require the measurement of the tracer time-activity curve in plasma (PTAC) as the model input function. It is widely believed that the insertion of arterial lines and the subsequent collection and processing of the biomedical signal sampled from the arterial blood are not compatible with the practice of clinical PET, as it is invasive and exposes personnel to the risks associated with the handling of patient blood and radiation dose. Therefore, it is of interest to develop practical noninvasive measurement techniques for tracer kinetic modeling with PET. In this paper, a technique is proposed to extract the input function together with the physiological parameters from the brain dynamic images alone. The identifiability of this method is tested rigorously by using Monte Carlo simulation. The results show that the proposed method is able to quantify all the required parameters by using the information obtained from two or more regions of interest (ROIs) with very different dynamics in the PET dynamic images. There is no significant improvement in parameter estimation for the local cerebral metabolic rate of glucose (LCMRGlc) if there are more than three ROIs. The proposed method can provide very reliable estimation of LCMRGlc, which is our primary interest in this study     

Fast and reliable estimation of multiple parametric images using an integrated method for dynamic SPECT

Dynamic single photon emission computed tomography (SPECT) has demonstrated the potential to quantitatively estimate physiological parameters in the brain and the heart. The generalized linear least square (GLLS) method is a well-established method for solving linear compartment models with fast computational speed. However, the high level of noise intrinsic in the SPECT data leads to reliability and instability problems of GLLS for generating parametric images. An integrated method is proposed to restrict the noise in both the temporal and spatial domains to estimate multiple parametric images for dynamic SPECT. This method comprises three steps which are optimum image sampling schedule in the projection space, cluster analysis applied postreconstruction and parametric image generation with GLLS. The simulation and experimental studies for the neuronal nicotine acetylcholine receptor tracer of 5-[123I]-iodo-A-85380 were employed to evaluate the performance of the proposed method. The results of influx rate of K1 and volume of distribution of Vd demonstrated that the integrated method was successful in generating low noise parametric images for high noise SPECT data without enhancing the partial volume effect. Furthermore, the integrated method is computationally efficient for potential clinical applications.     

Characterization of dynamic 3-D PET imaging for functional brainmapping

Methods for optimizing the acquisition, reconstruction and analysis of positron emission tomography (PET) images for functional brain mapping have been investigated. The scatter fraction and noise-equivalent count rate characteristics were measured for the ECAT 951/31R PET scanner operating in septa-extended two-dimensional (2-D) and septa-retracted three-dimensional (3-D) modes. The 3-D mode is shown to provide higher signal-to-noise images than the 2-D mode at specific activities less than 30 kBq/ml. To enable increased temporal resolution in dynamic 3-D PET activation studies, a parallel version of the 3-D reconstruction algorithm was developed. Implementation of the reprojection algorithm on an 88 processor i860 supercomputer resulted in a more than tenfold increase in reconstruction speed compared to a single i860 processor system. An investigation of the optimal duration for imaging brain activations was undertaken in 12 normal subjects using repeated H₂¹⁵O slow infusions and a visually presented lexical decision task. The significance of change in regional cerebral blood flow (CBF) was determined using statistical parametric maps for images acquired during stimulation, immediately after stimulation, and commencing 1 min after cessation of the stimulus. Regions of CBF change were detected in all three images. Dynamic 3-D, or four-dimensional (4-D), PET activation scanning is shown to be practical and likely to further improve the sensitivity of PET for detection of subtle regional CBF changes in functional brain mapping research     

Effect of real-time weighted integration system for rapid calculation of functional images in clinical positron emission tomography

A system has been developed to rapidly calculate images of parametric rate constants, without acquiring dynamic frame data for clinical positron emission tomography (PET). This method is based on the weighted-integration algorithms for the two- and three-compartment models, and hardware developments (real-time operation and a large cache memory system) in a PET scanner, Headtome-IV, which enables the acquisition of multiple sinograms with independent weight integration functions. Following the administration of the radiotracer, the scan is initiated to collect multiple time-weighted, integrated sinograms with three different weight functions. These sinograms are reconstructed and the images, with the arterial blood data, are inserted into the operational equations to provide parametric rate constant images. The implementation of this method has been checked in H(2)(15 )O and (18)F-fluorophenylalanine ((18)FPhe) studies based on a two-compartment model, and in a (18)F-fluorodeoxyglucose ((18)FDG) study based on the three-compartment model. A volunteer study, completed for each compound, yielded results consistent with those produced by existing nonlinear fitting methods. Thus, this system has been developed capable of generating rapidly quantitative, physiological images, without dynamic data acquisition, which will be of great advantage to PET in the clinical environment. This system would also be of great advantage in the new generation high-resolution PET tomography, which acquire data in a 3-D, septaless mode.     

A standardized blood sampling scheme in quantitative FDG-PETstudies

Quantitative estimation of brain glucose metabolism (rCMRGlc) with positron emission tomography and fluorodeoxyglucose involves arterial blood sampling to estimate the delivery of radioactivity to the brain. Usually, for an intravenous injection of 30 s duration, an accurate input curve requires a frequency of one sample every 5 s or less to determine the peak activity in arterial plasma during the first 2 min after injection. In this work, 13 standardized sampling times were shown to be sufficient to accurately define the input curve. This standardized input curve was subsequently fitted by a polynomial function for its rising part and by spectral analysis for its decreasing part. Using the measured, the standardized, and the fitted input curves, rCMRGlc was estimated in 32 cerebral regions of interest in 20 normal volunteers. Comparison of rCMRGlc values obtained with the measured and the fitted input curves showed that both procedures gave consistent results, with a maximal relative error in mean rCMRGlc of 1% when using the autoradiographic method and 2% using kinetic analysis of dynamic data. This input-curve-fitting technique, which is not dependent on the peak time occurrence, allows an accurate determination of the input-curve shape from reduced sampling schemes.     

Performance evaluation of functional medical imaging compression via optimal sampling schedule designs and cluster analysis

In previous work we have described a technique for the compression of positron emission tomography (PET) image data in the spatial and temporal domains based on optimal sampling schedule designs (OSS) and cluster analysis. It can potentially achieve a high data compression ratio greater than 80:1. However, the number of distinguishable cluster groups in dynamic PET image data is a critical issue for this algorithm that has not been experimentally analyzed on clinical data. In this paper, the problem of experimentally determining the ideal cluster number for the algorithm for PET brain data is addressed.     

Volume rendering of multimodal images: application to MRI and PET imaging of the human brain

A procedure for combining and visualizing complementary structural and functional information from magnetic resonance imaging (MRI) and positron emission tomography (PET) is described. MR and PET images of the human brain were obtained and correlated to form three-dimensional volumes of image data. Volume rendering and solid-texturing concepts were combined to develop a new volume imaging technique for ;volume texture-mapping' brain glucose metabolism (from PET) onto brain anatomy (from MRI). The technique was used to produce sequences of three-dimensional views: these sequences were dynamically displayed in a ;cine-loop' to better visualize the three-dimensional relationship between brain structure and function. The techniques provide a means of presenting vast amounts of multidimensional data in a form that is easily understood, and the resulting images are essential to an understanding of the normal and pathologic states of the human brain.     

A new method for volume segmentation of PET images, based on possibility theory

18F-fluorodeoxyglucose positron emission tomography (18FDG PET) has become an essential technique in oncology. Accurate segmentation and uptake quantification are crucial in order to enable objective follow-up, the optimization of radiotherapy planning, and therapeutic evaluation. We have designed and evaluated a new, nearly automatic and operator-independent segmentation approach. This incorporated possibility theory, in order to take into account the uncertainty and inaccuracy inherent in the image. The approach remained independent of PET facilities since it did not require any preliminary calibration. Good results were obtained from phantom images [percent error =18.38% (mean) ± 9.72% (standard deviation)]. Results on simulated and anatomopathological data sets were quantified using different similarity measures and showed the method was efficient (simulated images: Dice index =82.18% ± 13.53% for SUV =2.5 ). The approach could, therefore, be an efficient and robust tool for uptake volume segmentation, and lead to new indicators for measuring volume of interest activity.     

Optimal image sampling schedule: a new effective way to reduce dynamic image storage space and functional image processing time

An optimal image sampling schedule for tracer dynamic studies with positron emission tomography (PET) is proposed. This schedule incorporates the characteristics of PET measurement and uses a new cost function and the D-optimal criterion. A detailed case study of the estimation of the local cerebral metabolic rate of glucose (LCMRGLc) using the tracer fluorodeoxyglucose (FDG) and the four-parameter FDG model is presented. As the sampling schedule designed requires only four dynamic images, the storage space and data processing time are greatly reduced, while the precision of the parameter estimates is almost the same as that achieved with a commonly used schedule. The effects of intersubject and intrasubject parameter variations on parameter estimation with the use of this optimal sampling schedule are investigated by computer simulation. The simulation results show that the estimation of parameters is sufficiently robust with respect to these intersubject and intrasubject variations. The optimal sampling schedule is quite suitable therefore for PET regional parameter estimation, as well as for image-wide parameter estimation, for different subjects.     

Wavelet analysis for brain-function imaging

The authors present a new algorithmic procedure for the analysis of brain images. This procedure is specifically designed to image the activity and functional organization of the brain. The authors' results are tested on data collected and previously analyzed with the technique known as in vivo optical imaging of intrinsic signals. The authors' procedure enhances the applicability of this technique and facilitates the extension of the underlying ideas to other imaging problems (e.g., functional MRI). The authors' thrust is two fold. First, they give a systematic method to control the blood vessel artifacts which typically reduce the dynamic range of the image. They propose a mathematical model for the vibrations in time of the veins and arteries and they design a new method for cleaning the images of the vessels with the highest time variations. This procedure is based on the analysis of the singularities of the images. The use of wavelet transform is of crucial importance in characterizing the singularities and reconstructing appropriate versions of the original images. The second important component of the authors' work is the analysis of the time evolution of the fine structure of the images. They show that, once the images have been cleaned of the blood vessel vibrations/variations, the principal component of the time evolutions of the signals is due to the functional activity following the stimuli. The part of the brain where this function takes place can be localized and delineated with precision.     

Comparison of list-mode and DIRECT approaches for time-of-flight PET reconstruction

Early clinical results with time-of-flight (TOF) positron emission tomography (PET) systems have demonstrated the advantages of TOF information in PET reconstruction. Reconstruction approaches in TOF-PET systems include list-mode and binned iterative algorithms as well as confidence-weighted analytic methods. List-mode iterative TOF reconstruction retains the resolutions of the data in the spatial and temporal domains without any binning approximations but is computationally intensive. We have developed an approach [DIRECT (direct image reconstruction for TOF)] to speed up TOF-PET reconstruction that takes advantage of the reduced angular sampling requirement of TOF data by grouping list-mode data into a small number of azimuthal views and co-polar tilts and depositing the grouped events into histo-images, arrays with the sampling and geometry of the final image. All physical effects are included in the system model and deposited in the same histo-image structure. Using histo-images allows efficient computation during reconstruction without ray-tracing or interpolation operations. The DIRECT approach was compared with 3-D list-mode TOF ordered subsets expectation maximization (OSEM) reconstruction for phantom and patient data taken on the University of Pennsylvania research LaBr (3) TOF-PET scanner. The total processing and reconstruction time for these studies with DIRECT without attention to code optimization is approximately 25%-30% that of list-mode TOF-OSEM to achieve comparable image quality. Furthermore, the reconstruction time for DIRECT is independent of the number of events and/or sizes of the spatial and TOF kernels, while the time for list-mode TOF-OSEM increases with more events or larger kernels. The DIRECT approach is able to reproduce the image quality of list-mode iterative TOF reconstruction both qualitatively and quantitatively in measured data with a reduced time.     

A Clinical Application of Ensemble ICA to the Quantification of Myocardial Blood Flow in Dynamic $$ H^{{15}}_{2} O $$ PET

Ensemble independent component analysis (ICA) is a Bayesian multivariate data analysis method which allows various prior distributions for parameters and latent variables, leading to flexible data fitting. In this paper we apply ensemble ICA with a rectified Gaussian prior to dynamic \( H^{{15}}_{2} O \) positron emission tomography (PET) image data, emphasizing its clinical usefulness by showing that major cardiac components are successfully extracted in an unsupervised manner and myocardial blood flow can be estimated in 15 among 20 patients. Detailed experiments and results are illustrated.     

Mathematical simplification of a PET blood flow model

The positron emission tomography (PET) H(2)(15)O bolus injection model for cerebral blood flow (CBF) requires calculation of a certain double integral that, when calculated, provides the pixel values of a reconstructed image (PET number) in terms of the tissue flow, the arterial input function, a decay constant for (15)O, the partition coefficient and a camera calibration constant that relates the flow-dependent integrated tissue activity to the measured PET number (cts/pixel). The tissue activity is assumed to be zero at the time of injection. A mathematical simplification, changing the order of integration, enabled the integration with respect to time to be performed analytically before the integration of the arterial input function. As a result of this simplification, only single integrals remain to be calculated numerically; cubic spline integration was used to calculate numerically these remaining integrals. This technique increases the accuracy and speed of evaluating blood flow without making simplifying assumptions. Similar simplifications may be applicable to other physiological models.