Calculating parametric BPND and DVR image
- Copy the dynamic PET image file from PETPACS to your local disk
- Find the SIF file for this image; if SIF file does not exist, then create it
- Draw ROI on the reference region, and calculate and save its time-activity curve (TAC) in a TAC file (do not include any other regional TACs in the file)
- Check the θ3 constraints for this study protocol (see below)
- Execute program imgbfbp in command prompt window.
Remember to enter the θ3 constraints using options
-max, and preferably also threshold limit 10% using option
Include also option
-DVR, if DVR image is needed instead of
Suggested constraints in Turku PET Centre
|PET tracer||Parametric images||Regional TACs|
(1 Fixed limits in RPM software, used and discussed by Cselényi et al., 2006.
Constraints for θ3 may need to be re-evaluated when imaging parameters that affect the noise properties of the PET image are substantially changed (scanner, reconstruction, framing, study length, injected dose, ...).
If your PET tracer is not in the table
Ask Jouni Tuisku to determine the constraints for the tracer. For this purpose, you must provide a couple of dynamic images (control subject and patient, basal state and intervention etc), TAC of reference region, and preferably ROI files of all regions that may be of interest in regional or SPM analysis.
Issues to consider when determining the constraints for θ3
- Minimum of theta;3 must always be higher that the decay constant (λ) of the isotope that was used to label the tracer; λ for F-18 is 0.0063 min-1 and for C-11 0.034 min-1.
- Regional BF-SRTM (using bfmsrtm) must fit well all the regional TACs that may be of interest in any analysis
- The results of BF-SRTM are very sensitive to the weights, if model does not fit the data well.
- The quality of parametric image is usually not very dependent on the θ3max, as long as it is high enough; however, it should not be set to any higher value as necessary, because then more basis functions must be used, which will slow down the calculation.
- The BPND or DVR image will look the better (less noisy) the higher value for the θ3min is set. However, it may be that the highest BPND or DVR values are subsequently cut off, causing negative bias in regions of high BPND or DVR.
- Low θ3min leads to higher noise, which may be skewed and cause positive bias in regions of high BPND or DVR.
- Check that regional distribution of BPND or DVR pixel values is not extremely skewed
- BPND and DVR images must always be verified to provide similar regional results as the ones calculated from regional average TACs using fit_srtm or lhsrtm
- Initial estimates of suitable constraints for θ3 can be reached from θ3 images produced optionally using imgsrtm or from k2/(1+BPND) values (add λ) from lhsrtm.
Program imgsrtm transforms the
multilinear model, and uses non-negative
least squares method (NNLS) to estimate the model parameters.
It can be used to compute BPND, DVR, R1,
k2, and k2' images.
-SRTM2 it uses the modification of simplified reference tissue model
SRTM2 or MRTM2, where the original SRTM method is
used first to calculate median k2' (k2 of reference region)
from all pixels where BPND, and thereafter SRTM is run another time with fixed
- Instruction by tracer
- Binding potential
- Reference tissue input compartmental models
- Parametric image
- Computation of parametric images
Cselényi Z, Olsson H, Halldin C, Gulyás B, Farde L. A comparison of recent parametric neuroreceptor mapping approaches based on measurements with the high affinity PET radioligands [11C]FLB 457 and [11C]WAY 100635. Neuroimage 2006; 32: 1690-1708. doi: 10.1016/j.neuroimage.2006.02.053.
Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage 1997; 6: 279-287. doi: 10.1006/nimg.1997.0303.
Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. Neuroimage 1996; 4: 153-158. doi: 10.1006/nimg.1996.0066.
Liukko K. Using basis function method for parametric mapping. TPCMOD0027.
Rizzo G, Turkheimer FE, Bertoldo A. Multi-scale hierarchical approach for parametric mapping: assessment on multi-compartmental models. NeuroImage 2013; 67: 344-353. doi: 10.1016/j.neuroimage.2012.11.045.
Sederholm K. Study on basis function methods reliance on θ3 parameter limits - study with simulated [11C]raclopride images. TPCMOD0028.
Updated at: 2020-01-01
Created at: 2006-05-23
Written by: Vesa Oikonen, Kaisa Liukko, Jouni Tuisku