Image Derived Input Functions: Effects of Motion on Tracer Kinetic Analyses

Purpose: To quantify the effects of motion affected image-derived input functions (IDIF) on the outcome of tracer kinetic analyses. Procedures: Two simulation studies, one based on high and the other on low cortical uptake, were performed. Different degrees of rotational and axial translational motion were added to the final frames of simulated dynamic positron emission tomography scans. Extracted IDIFs from motion affected simulated scans were compared to original IDIFs and to outcome of tracer kinetic analysis (volume of distribution, V T). Results: Differences in IDIF values of up to 239 % were found for the last frames. Patient motion of more than 6 ° or 5 mm resulted in at least 10 % higher or lower VT values for the high cortical tracer. Conclusion: The degrees of motion studied are commonly observed in clinical studies and hamper the extraction of accurate IDIFs. Therefore, it is essential to ensure that patient motion is minimal and corrected for

Cerebral blood flow and glucose metabolism in healthy volunteers measured using a high-resolution PET scanner

BACKGROUND: Positron emission tomography (PET) allows for the measurement of cerebral blood flow (CBF; based on [(15)O]H(2)O) and cerebral metabolic rate of glucose utilization (CMR(glu); based on [(18) F]-2-fluoro-2-deoxy-d-glucose ([(18) F]FDG)). By using kinetic modeling, quantitative CBF and CMR(glu) values can be obtained. However, hardware limitations led to the development of semiquantitive calculation schemes which are still widely used. In this paper, the analysis of CMR(glu) and CBF scans, acquired on a current state-of-the-art PET brain scanner, is presented. In particular, the correspondence between nonlinear as well as linearized methods for the determination of CBF and CMR(glu) is investigated. As a further step towards widespread clinical applicability, the use of an image-derived input function (IDIF) is investigated. METHODS: Thirteen healthy male volunteers were included in this study. Each subject had one scanning session in the fasting state, consisting of a dynamic [(15)O]H(2)O scan and a dynamic [(18) F]FDG PET scan, acquired at a high-resolution research tomograph. Time-activity curves (TACs) were generated for automatically delineated and for manually drawn gray matter (GM) and white matter regions. Input functions were derived using on-line arterial blood sampling (blood sampler derived input function (BSIF)). Additionally, the possibility of using carotid artery IDIFs was investigated. Data were analyzed using nonlinear regression (NLR) of regional TACs and parametric methods. RESULTS: After quality control, 9 CMR(glu) and 11 CBF scans were available for analysis. Average GM CMR(glu) values were 0.33 ± 0.04 μmol/cm(3) per minute, and average CBF values were 0.43 ± 0.09 mL/cm(3) per minute. Good correlation between NLR and parametric CMR(glu) measurements was obtained as well as between NLR and parametric CBF values. For CMR(glu) Patlak linearization, BSIF and IDIF derived results were similar. The use of an IDIF, however, did not provide reliable CBF estimates. CONCLUSION: Nonlinear regression analysis, allowing for the derivation of regional CBF and CMR(glu) values, can be applied to data acquired with high-spatial resolution current state-of-the-art PET brain scanners. Linearized models, applied to the voxel level, resulted in comparable values. CMR(glu) measurements do not require invasive arterial sampling to define the input function. TRIAL REGISTRATION: ClinicalTrials.gov NCT0062608

Image-derived input function in dynamic human PET/CT: methodology and validation with 11C-acetate and 18F-fluorothioheptadecanoic acid in muscle and 18F-fluorodeoxyglucose in brain

International audienc

Image-Derived Input Function for Human Brain Using High Resolution PET Imaging with [11C](R)-rolipram and [11C]PBR28

The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods. values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling

Optimal reconstruction algorithms for high-resolution positron emission tomography

Lammertsma, A.A. [Promotor]Boellaard, R. [Copromotor

IMAGE DERIVED INPUT FUNCTION FOR BRAIN [11C]TMSX PET IMAGING

Modeling and analysis of positron emission tomography (PET) imaging data requires the critical information of the input function of the radioligand used. In PET imaging of the brain, the ‘gold standard’ for acquiring input function is to estimate the metabolite corrected arterial plasma input of the used radioligand as a function of time via arterial cannulation, i.e. to use the so-called original arterial input function (OAIF) method. Arterial cannulation, however, is unpleasant for the patient, invasive, requires expertise and additional resources. Consequently, it discourages patients and healthy subjects to enroll into clinical PET studies. To counter these problems, the feasibility of an alternative method for acquiring input function from the PET image, image-derived input function (IDIF), was evaluated for brain PET imaging with [11C]TMSX radioligand in this thesis. [11C]TMSX is a radioligand binding selectively to adenosine A2A-receptors. The method was implemented on data from 45 study subjects (9 healthy controls, 19 Parkinson’s disease patients and 17 multiple sclerosis patients) imaged with [11C]TMSX and High Resolution Research Tomograph (HRRT) PET scanner in earlier studies in Turku PET Centre. The results showed significant differences between the level of IDIF and OAIF values although with a high correlation. Image derived input function acquisition method that was used in this study is therefore not reliable enough to substitute original arterial input function. Alternative IDIF extraction methods should be investigated for this purpose in the future