Flurodeoxyglucose Research Articles

Commentary on “Diagnosis of Alzheimer's disease: Two decades of progress.” Central role of technology in the treatment and prevention of Alzheimer's disease

Commentary on “Diagnosis of Alzheimer's disease: Two decades of progress.” Central role of technology in the treatment and prevention of Alzheimer's disease

Camouflaged Deconvolution with Application to Blood Curve Modeling in FDG PET Studies

Camouflaged deconvolution arises when the kernel in a simple convolution model is not completely specified. We consider a situation in which the same fixed signal is repeatedly measured by separate convolutions with imprecisely known kernels. We develop a regularization methodology for application to these problems. The method involves simultaneous estimation of the target signal and the unknown parameters of the convolution kernels. Cross-validation is used to determine the degree of smoothness of the solution. We use simulation studies matched to the application to evaluate statistical performance. These simulations find that the convergence of the regularization estimator is largely unaffected by the lack of information about the convolution kernels. We illustrate the methodology by application to a blood curve modeling problem arising in the context of positron emission tomography (PET) studies with flurodeoxyglucose (FDG), a commonly used glucose tracer. The results show promise towards the goal of obtaining reliable metabolic information with much more limited blood sampling.

Camouflaged Deconvolution With Application to Blood Curve Modeling in FDG PET Studies

Abstract Camouflaged deconvolution arises when the kernel in a simple convolution model is not completely specified. We consider a situation in which the same fixed signal is repeatedly measured by separate convolutions with imprecisely known kernels. We develop a regularization methodology for application to these problems. The method involves simultaneous estimation of the target signal and the unknown parameters of the convolution kernels. Cross-validation is used to determine the degree of smoothness of the solution. We use simulation studies matched to the application to evaluate statistical performance. These simulations find that the convergence of the regularization estimator is largely unaffected by the lack of information about the convolution kernels. We illustrate the methodology by application to a blood curve modeling problem arising in the context of positron emission tomography (PET) studies with flurodeoxyglucose (FDG), a commonly used glucose tracer. The results show promise towards the goal of obtaining reliable metabolic information with much more limited blood sampling.

Magnetic Resonance and Positron Emission Tomography Imaging of the Basal Ganglia in Schizophrenia

Magnetic Resonance and Positron Emission Tomography with Flurodeoxyglucose (FDG) were used to study the size and metabolic rate of the caudate and putamen in 43 schizophrenia patients either never medicated n = 16, or off medications n = 27 for at least two weeks and 17 normal controls. During the FDG uptake period, all subjects performed a visual attention task (a degraded stimulus version of the continuous performance task.) The size of the caudate and putamen was not significantly different between the groups. In normal controls the right caudate and putamen were more active than the left but in schizophrenics the left was more active than the right. Exploratory correlations between Brief Psychiatric Rating Scale (BPRS) subscales scores and glucose metabolism revealed higher hostility and lower mannerism and posturing associated with caudate and putamen metabolism. These findings are suggestive of a right putamen pathology in schizophrenia. It is consistent with our previous findings that neuroleptics increased the metabolic rate in the right putamen but, not the left, in schizophrenics. Additionally, analyses comparing the shape of caudate and putamen will also be presented.