imglhdv - tpcclib 0.7.8 © 2022 by Turku PET Centre

Computation of parametric image of distribution volume (DV) from dynamic
PET image in ECAT, NIfTI, or Analyze format applying one- or two-tissue
compartmental model with arterial plasma input.
The compartmental models are transformed to general linear least squares
functions, which are solved using Lawson-Hanson non-negative least squares
(NNLS) algorithm (1). DV is estimated directly without division (2, 3, 4).
Vascular volume is ignored here.
Dynamic PET image and plasma time-activity curve (PTAC) must be corrected
for decay to the tracer injection time.
Usage: imglhdv [Options] ptacfile imgfile dvfile
 -1 | -2 | -A | -0
     With options -1 and -2 the one- or two-tissue compartment model can be
     forced for all image voxels; otherwise both models are fitted, and
     with option -A the model is selected based on AIC separately for each
     voxel; by default (or option -0) Akaike weighted average of the model
     parameters (5, 6) are reported.
     Programs writes the selected model number (1 or 2, or value between
     1 and 2 as an image.
     Pixels with AUC less than (threshold/100 x PTAC AUC) are set to zero
     default is 0%
 -end=<Fit end time (min)>
     Use data from 0 to end time; by default, model is fitted to all frames.
 -max=<Max value>
     Upper limit for DV values.
 -h, --help
     Display usage information on standard output and exit.
 -v, --version
     Display version and compile information on standard output and exit.
 -d[n], --debug[=n], --verbose[=n]
     Set the level (n) of debugging messages and listings.
 -q, --quiet
     Suppress displaying normal results on standard output.
 -s, --silent
     Suppress displaying anything except errors.
The unit of voxel values in the DV image is (ml blood)/(ml tissue).
  imglhdv ua3818ap.kbq ua3818dy1.v ua3818dv.v
1. Lawson CL & Hanson RJ. Solving least squares problems.
   Prentice-Hall, 1974, ISBN 0-89871-356-0.
2. Zhou Y, Brasic J, Endres CJ, Kuwabara H, Kimes A, Contoreggi C, Maini A,
   Ernst M, Wong DF. Binding potential image based statistical mapping for
   detection of dopamine release by [11C]raclopride dynamic PET.
   NeuroImage 2002;16(3):S91.
3. Zhou Y, Brasic JR, Ye W, Dogan AS, Hilton J, Singer HS, Wong DF.
   Quantification of cerebral serotonin binding in normal controls and
   subjects with Tourette's syndrome using [11C]MDL 100,907 and
   (+)[11C]McN 5652 dynamic PET with parametric imaging approach.
   NeuroImage 2004;22(Suppl 2):T98.
4. Hagelberg N, Aalto S, Kajander J, Oikonen V, Hinkka S, NĂ¥gren K,
   Hietala J, Scheinin H. Alfentanil increases cortical dopamine D2/D3
   receptor binding in healthy subjects. Pain 2004;109:86-93.
5. Turkheimer FE, Hinz R, Cunningham VJ. On the undecidability among
   kinetic models: from model selection to model averaging. J Cereb Blood
   Flow Metab 2003; 23: 490-498.
6. Sederholm K. Model averaging with Akaike weights. TPCMOD0016 2003-04-07.
See also: imgdv, imgbfbp, imgratio, img2tif, logan
Keywords: image, modelling, distribution volume, Vt, NNLS