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- Axelsson, Jan, 1966-
(författare)
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Imlook4d : introducing an extendable research 4d analysis software
- 2014
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Ingår i: XII Turku PET Symposium, 24-27 May 2014, Turku, Finland. ; , s. 63-63
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Imlook4d (http://www.dicom-port.com) is a free Matlab based graphical user interface (GUI) tool useful for static, dynamic and gated PET studies. It supports reading and writing DICOM, Nifti, Analyze, ECAT. The DICOM reader is orders of magnitude faster than the Matlab imaging toolbox. Imlook4d requires no additional Matlab toolboxes.The main benefit with imlook4d is that it is easily extendable with scripts, accessing exported variables such as the image matrix (4D) and a region-of-interest (ROI) matrix. Scripts are available via a menu in the imlook4d GUI, and can be used to manipulate the image-matrix and ROI data. There is also a menu option to export and import these variables to the Matlab workspace for interactive manipulation, useful for one-off fixes or for script development. There are presently about 30 scripts in categories such as ROI, Matrix, Header info etc. There is also direct export to ImageJ [1] and import back from ImageJ, thus giving access to all tools available within ImageJ.Imlook4d has a built in volume-of-interest editor, with a brush tool for quick interactive ROI delineation, and via scripts, different ways of thresholding ROIs from parts of the image. Time activity data is saved to a tab-delimited text file.The principal-component (PC) based Hotelling filter is an integrated part of the program, which allows for interactive noise reduction without loss of quantitation [2]. A typical work flow for a dynamic data set is to turn on the filter for ROI delineation, and then there is the choice of turning it off for export of time-activity data. Also the PC images can be used to draw ROIs on, which under some circumstances gives enhanced contrast.Calculation of parametric pharmacokinetic modelling images can be performed interactively, calculated slice by slice as the user scrolls through the volume. Reference models for Patlak, Logan and Averaged Simple Flow Model [3] applied on 15O-water are implemented, and it is relatively easy to implement other kinetic models. Similarly, scripts have been developed for regional Patlak and Logan models on ROI data.[1] Rasband, WS, ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 1997-2014[2] Axelsson J, Sörensen J, The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction. BMC Med Phys. 2013 Apr 10;13:1. doi: 10.1186/1756-6649-13-1.[3] Yoshida, K, Mullani, N and Gould KL, Coronary Flow and Flow Reserve by PET Simplified for Clinical Applications Using Rubidium-82 or Nitrogen-13-Ammonia, J Nucl Med 1996; 37:1701-1712Figure 1. The imlook4d GUI with the user SCRIPTS menu selected. The group of ROI scripts was further selected. In the underlying image, a rough ROI is created.
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| 2. |
- Axelsson, Jan, 1966-, et al.
(författare)
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The 2D Hotelling filter : a quantitativenoise-reducing principal-component filter fordynamic PET data, with applications in patientdose reduction
- 2013
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Ingår i: BMC Medical Physics. - : BioMed Central (BMC). - 1756-6649. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise fromdynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. Wefurthermore show how preprocessing images with this filter improves parametric images created from suchdynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamictime-series. The Scree-plot technique was used to determine which principal components to be rejected in thefilter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors andbrain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to realPET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varyingparts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics comparable to 20MBq, 60 MBq and 200 MBq injected activity. Hotelling filter performed on slices (2D) and on volumes (3D) werecompared.Results: The 2D Hotelling filter reduces noise in the tissue uptake drastically, so that it becomes simple to manuallypick out regions-of-interest from noisy data. 2D Hotelling filter introduces less bias than 3D Hotelling filter in focalRaclopride uptake. Simulations show that the Hotelling filter is sensitive to typical blood peak in PET prior to tissueuptake have commenced, introducing a negative bias in early tissue uptake. Quantitation on real dynamic data isreliable. Two examples clearly show that pre-filtering the dynamic sequence with the Hotelling filter prior toPatlak-slope calculations gives clearly improved parametric image quality. We also show that a dramatic dosereduction can be achieved for Patlak slope images without changing image quality or quantitation.Conclusions: The 2D Hotelling-filtering of dynamic PET data is a computer-efficient method that gives visuallyimproved differentiation of different tissues, which we have observed improve manual or automated regionof-interest delineation of dynamic data. Parametric Patlak images on Hotelling-filtered data display improved clarity,compared to non-filtered Patlak slope images without measurable loss of quantitation, and allow a dramaticdecrease in patient injected dose.
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| 3. |
- Holmberg, Daniel, et al.
(författare)
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Reducing scanning time to 50% for In-111 pentetreotide SPECT when using model-based compensation
- 2012
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Ingår i: 2012 IEEE nuclear science symposium and medical imaging conference record (NSS/MIC). - : IEEE. - 9781467320306 - 9781467320283 ; , s. 2946-2949
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Konferensbidrag (refereegranskat)abstract
- In In-111-pentetreotide SPECT, it can be difficult to detect small tumors because of high noise levels and low spatial resolution. The aim of this study was to perform optimization of tumor detection in the liver, with regards to the acquisition and reconstruction protocol for In-111-pentetreotide SPECT with model-based compensation included in the OSEM reconstruction. We were also interested in the effect of performing the examination in half of the time or with half the administered activity. Image reconstruction without model-based compensation was also included for comparison. The study concentrates on the acquired number of projections and the subset size in the OSEM reconstruction, and evaluates contrast as a function of noise for a range of OSEM iterations. The raw-data projections are produced using Monte Carlo simulations of a patient-like anthropomorphic phantom with realistic In-111 pentetreotide uptake, including spherical tumors in the liver. Two collimators are evaluated, the extended low-energy general-purpose (ELEGP) and the medium-energy general-purpose (MEGP) collimator. ELEGP proved to be a better collimator when using model-based compensation. The results also indicate that a relatively low number of subsets is advantageous, and that 60 projection angles or even lower is a better choice than 120. For both collimators the time-reduced scan including model-based compensation was better compared to the full-time reconstructions without model-based compensation.
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