Biomedical subjects

W C Siu

Publications and source records attributed to W C Siu.

5 recordsLinked to original sources

PubMed1999

GLLS for optimally sampled continuous dynamic system modeling: theory and algorithm.

The original generalized linear least squares (GLLS) algorithm was developed for non-uniformly sampled biomedical system parameter estimation using finely sampled instantaneous measurements (D. Feng, S.C. Huang, Z. Wang, D. Ho, An unbiased parametric imaging algorithm for non-uniformly sampled biomedical system parameter estimation, IEEE Trans. Med. Imag. 15 (1996) 512-518). This algorithm is particularly useful for image-wide generation of parametric images with positron emission tomography (PET), as it is computationally efficient and statistically reliable (D. Feng, D. Ho, Chen, K., L.C. Wu, J.K. Wang, R.S. Liu, S.H. Yeh, An evaluation of the algorithms for determining local cerebral metabolic rates of glucose using positron emission tomography dynamic data, IEEE Trans. Med. Imag. 14 (1995) 697-710). However, when dynamic PET image data are sampled according to the optimal image sampling schedule (OISS) to reduce memory and storage space (X. Li, D. Feng, K. Chen, Optimal image sampling schedule: A new effective way to reduce dynamic image storage space and functional image processing time, IEEE Trans. Med. Imag. 15 (1996) 710-718), only a few temporal image frames are recorded (e.g. only four images are recorded for the four parameter fluoro-deoxy-glucose (FDG) model). These image frames are recorded in terms of accumulated radio-activity counts and as a result, the direct application of GLLS is not reliable as instantaneous measurement samples can no longer be approximated by averaging of accumulated measurements over the sampling intervals. In this paper, we extend GLLS to OISS-GLLS which deals with the fewer accumulated measurement samples obtained from OISS dynamic systems. The theory and algorithm of this new technique are formulated and studied extensively. To investigate statistical reliability and computational efficiency of OISS-GLLS, a simulation study using dynamic PET data was performed. OISS-GLLS using 4-measurement samples was compared to the non-linear least squares (NLS) method using 22-measurement samples, GLLS using 22-measurement samples and OISS-NLS using 4-measurement samples. Results demonstrated that OISS-GLLS was able to achieve parameter estimates of equivalent accuracy and reliability in comparison to NLS or GLLS using finely sampled measurements (22-measurement samples), or OISS-NLS using optimally sampled measurements (4-measurement samples). Further more, as fewer measurement samples are used in OISS-GLLS, this algorithm is computationally faster than NLS or GLLS. Therefore, OISS-GLLS is well-suited for image-wide parameter estimation when PET image data are recorded according to the optimal image sampling schedule.

Algorithms↗

PubMed1998

Generalized linear least squares algorithm for non-uniformly sampled biomedical system identification with possible repeated eigenvalues.

The recently developed generalized linear least squares (GLLS) algorithm has been found very useful in non-uniformly sampled biomedical signal processing and parameter estimation. However, the current version of the algorithm cannot deal with signals and systems containing repeated eigenvalues. In this paper, we extend the algorithm, so that it can be used for non-uniformly sampled signals and systems with/without repeated eigenvalues. The related theory and detailed derivation of the algorithm are given. A case study is presented, which demonstrates that the extended algorithm can provide more choices for system identification and is able to select the most suitable model for the system from the non-uniformly sampled noisy signal.

Algorithms↗

PubMed1998

Optimized acquisition time and image sampling for dynamic SPECT of Tl-201.

With the recent development in scatter and attenuation correction algorithms, dynamic single photon emission computerized tomography (SPECT) can potentially yield physiological parameters, with tracers exhibiting suitable kinetics such as thallium-201 (Tl-201). A systematic way is proposed to investigate the minimum data acquisition times and sampling requirements for estimating physiological parameters with quantitative dynamic SPECT. Two different sampling schemes were investigated with Monte Carlo simulations: 1) Continuous data collection for total study duration ranging from 30-240 min. 2) Continuous data collection for first 10-45 min followed by a delayed study at approximately 3 h. Tissue time activity curves with realistic noise were generated from a mean plasma time activity curve and rate constants (K1 - k4) derived from Tl-201 kinetic studies in 16 dogs. Full dynamic sampling schedules (DynSS) were compared to optimum sampling schedules (OSS). We found that OSS can reliably estimate the blood flow related K1 and Vd comparable to DynSS. A 30-min continuous collection was sufficient if only K1 was of interest. A split session schedule of a 30-min dynamic followed by a static study at 3 h allowed reliable estimation of both K1 and Vd avoiding the need for a prolonged (>60-min) continuous dynamic acquisition. The methodology developed should also be applicable to optimizing sampling schedules for other SPECT tracers.

Animals↗

PubMed1998

Dynamic imaging and tracer kinetic modeling for emission tomography using rotating detectors.

When performing dynamic studies using emission tomography the tracer distribution changes during acquisition of a single set of projections. This is particularly true for some positron emission tomography (PET) systems which, like single photon emission computed tomography (SPECT), acquire data over a limited angle at any time, with full projections obtained by rotation of the detectors. In this paper, an approach is proposed for processing data from these systems, applicable to either PET or SPECT. A method of interpolation, based on overlapped parabolas, is used to obtain an estimate of the total counts in each pixel of the projections for each required frame-interval, which is the total time to acquire a single complete set of projections necessary for reconstruction. The resultant projections are reconstructed using traditional filtered backprojection (FBP) and tracer kinetic parameters are estimated using a method which relies on counts integrated over the frame-interval rather than instantaneous values. Simulated data were used to illustrate the technique's capabilities with noise levels typical of those encountered in either PET or SPECT. Dynamic datasets were constructed, based on kinetic parameters for fluoro-deoxy-glucose (FDG) and use of either a full ring detector or rotating detector acquisition. For the rotating detector, use of the interpolation scheme provided reconstructed dynamic images with reduced artefacts compared to unprocessed data or use of linear interpolation. Estimates for the metabolic rate of glucose had similar bias to those obtained from a full ring detector.

Algorithms↗

PubMed1997

A technique for extracting physiological parameters and the required 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 (ROI's) 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 the number of ROI's are more than three. The proposed method can provide very reliable estimation of LCMRGlc, which is our primary interest in this study.

Brain↗