Involuntary patient motion can happen in dynamic whole body (DWB) PET due to long scanning times, which may cause inaccurate quantification of tissue parameters. To quantify the impact on Patlak parameters, we simulated dynamic data using patient-derived motion fields, systematically introducing the motion at different passes of the dynamic scan, both inter and intra-frame. Estimated parameters are compared against the ground truth. Results show that errors can be large, even for small motion. Caution is advised when quantitatively evaluating DWB-PET images, if any motion has been detected

Biguri, Ander

Burger, Irene

Hutton, Brian F

Kotasidis, Fotis

Thielemans, Kris

Whitehead, Alexander C

English

UCL Discovery

Systematic evaluation of the impact of involuntarymotion in whole body dynamic PETAnder Biguri∗, Fotis Kotasidis†, Alexander C. Whitehead∗, Irene Burger‡, Brian F. Hutton∗ and Kris Thielemans∗∗ Institute of Nuclear Medicine, University College London, UK† GE Healthcare, Zurich, Switzerland‡ Nuclear Medicine, University Hospital Zurich, Zurich, SwitzerlandAbstract—Involuntary patient motion can happen in dynamicwhole body (DWB) PET due to long scanning times, which maycause inaccurate quantification of tissue parameters. To quantifythe impact on Patlak parameters, we simulated dynamic datausing patient-derived motion fields, systematically introducingthe motion at different passes of the dynamic scan, both interand intra-frame. Estimated parameters are compared againstthe ground truth. Results show that errors can be large, even forsmall motion. Caution is advised when quantitatively evaluatingDWB-PET images, if any motion has been detected.Index Terms—dynamic whole body PET, simulation, PatlakI . I N T R O D U C T I O NDynamic whole body positron emission tomography (DWB-PET) is a promising technique that can improve quantificationand assessment of systemic diseases[1], tumor diagnosis andcharacterization[2]. This is achieved by estimating parametricimages of the tracer uptake rate constant, which comes withmany technical challenges. Among others involuntary patientmotion, prominent due to the long scanning times required,can hinder correct quantitative parametric image reconstruction.Although motion in PET is a well–studied problem, most of theliterature concentrates on respiratory motion or rigid motionusually limited to the brain.In this work, we use simulation tools to study whole-bodyinvoluntary motion, e.g. d̃ue to patient discomfort over theacquisition period, and in particular its effect on parametrictumour quantification. Arm motion was investigated in [3],concluding that its effect can be limited by adjusting the scatterscaling. Common motion types in cardiac PET patients wereidentified in [4] and simulated with simplified motion models,showing that parametric images can be severely misquantifiedin the presence of motion, particularly if the motion happensat mid or late frame.In this work, we build a simulation pipeline that uses patientderived motion applied at different time frames to system-atically evaluate the impact of motion on Patlak parametricimages.I I . M E T H O D SThe simulation pipeline consists of dynamic PET simulationand reconstruction, while using motion derived from clinicalexams. For all the following work, the SIRF open sourcepackage [5] has been used.A. Clinical dataThe clinical data consists of dynamic whole body scans of8 different patients, acquired on a GE Discovery MI (DMI) atThis research is supported by GE Healthcare.University Hospital Zurich, Switzerland. The exam starts with10 to 15 minutes positioned over the heart to acquire an imagederived input function (IF) and a subsequent 6 − 10 wholebody passes of 35 seconds per bed position, depending on thepatient. For all, 60 minutes after the injection an SUV imagewas acquired. A CTAC was acquired before injection time.Each whole body pass was reconstructed using the standardreconstruction technique for the DMI. For this work, patientsthat show visible motion during the scanning process havebeen selected.B. Patient-derived motionPatient motion is obtained for each patient and frame byregistering it with non-rigid transformation into their corre-sponding first WB dynamic pass (this frame is closest to theCTAC acquisition). This work uses the fast free-form B-splinedeformation algorithm with normalized mutual informationas objective function, as implemented in NiftyReg [6]. Theregistration was moderately over-regularized by applying asparse 15 voxel space between B-spline control points, toensure that sharp yet localized movements do not dominate theregistration such that gross body movement was recovered.C. SimulationsA series of dynamic scans following the clinical DWB-PET protocol were generated using the XCAT phantom [7],assigning patient-derived kinetic parameters to 64 tissues and3 tumours of 1 cm diameter in the left lung, and 3 tumoursof 2.5 cm, 2 cm and 1 cm diameter in the liver [8]. An inputfunction for 18F-FDG taken from [9] was used to simulatetime activity curves (TACs) to create dynamic images.The simulated images were registered to images in 2positions: the patient’s CTAC, and the position of the patientat the SUV image stage (last frame of the dynamic scan).Two types of simulated motion were then introduced forthe dynamic data: step-like and impulse-like motion. Step-likemotion uses dynamic images from a single position until, at aspecified frame, the patient position is changed. Alternatively,impulse-like motion introduces a single moved frame. Both ofthese experiments were run over the 14 frames of the dynamicsimulation and motion was systematically introduced in eachof the frames. For step-like motion, intraframe motion is alsosimulated, where the patient moves mid-frame.Non-TOF sinogram data were simulated using resolutionmodelling (using a 6 cm FWHM Gaussian filter), and usingthe repositioned attenuation maps. Randoms and scatter werenot included.Finally, all datasets were reconstructed using 10 iterationswith 17 subsets of OSEM, with the attenuation map fixed atFig. 1. Left: Composite image of CT XCAT phantom registered to first andlast dynamic frame of patient A. Middle: reconstructed time frame at SUVtime without motion or attenuation mismatch. Right: the same time framewith motion and attenuation mismatchFig. 2. Patlak slope (Ki) and intercept (Vb) value (SUVpeak) against framewhere step-like motion (top) and impulse-like motion (bottom) was introduced,for tumours in the liver using motion from patient A. The dashed line showsthe correct values of the tumour, for a simulation without motion.the first (CTAC) position. See Figure 1 for an illustration of themotion and the impact of the attenuation mismatch in a singletime frame. We noticed that the mismatch of the AC generates5− 10% error on concentration values when the mismatch islarge.For each of the parametric Patlak images, each tumour’sslope and intercept were quantified using ROImax, ROIpeak(as per EANM guideline for SUVpeak [10]) and ROImean (bythresholding at 42% of the maximum value and averaging).I I I . R E S U LT SAs all quantitative measures show a similar behaviour,only results of SUVpeak are presented. Figure 2 shows thechange of value on the slope and intercept against the framenumber where the motion was introduced in the indirect Patlakreconstruction. Intraframe motion (not shown) follows the samecurve.General analysis of motion is not trivial, as the patientderived motion is different for each patient in form andmagnitude. In Figure 3 the percentage change in Ki of eachtumour in the liver and lung in all patients is shown, plottedFig. 3. Relative change of SUVpeak vs motion in the tumour, for step-likemotion (left) and impulse-like motion (right). This plot is for motion introducedat frame 10 for step size motion and frame 16 for impulse-like motion, as itis the point where the differences were largest (see Figure 2).against the average motion in mm of each tumour. Similarbehaviour was obtained at other time frames, but with smallererror. For reference, the pixel size is 2.2× 2.2× 2.76 mm.I V. D I S C U S S I O N A N D C O N C L U S I O N SResults suggest that even small amounts of motion can leadto large changes in tumour quantification. Patlak estimation hasbeen shown to amplify errors caused by attenuation mismatch[11], and simulation presented here show that it also amplifiesmotion artifacts greatly. Albeit impulse-like motion simulationshows a more moderate misquantification of tumour values,it is higher than 10% when the tumour moves around 5 mm(50% of its size for the lung), this motion mode is expected tobe less representative of the clinic, as patients are unlikely tocome back to their original position with millimeter precision.Future work will include studying the impact on direct Patlakreconstruction and looking at line fitting residuals as motionmarkers, with more patient data.The presented results reinforce that motion correction isa key factor in quantitative PET analysis, and that accuratemethods for motion detection, modelling and correction areessential for intra- and inter-patient dynamic PET studies.R E F E R E N C E S[1] A. Rahmim et al., eng, Eur. J. Nucl. Med. Mol. Imaging, 2019.D O I: 10.1007/s00259-018-4153-6.[2] A. Dimitrakopoulou-Strauss, L. Pan, and C. Sachpekidis, en,Eur. J. Nucl. Med. Mol. Imaging, 2021. D O I: 10.1007/s00259-020-04843-6.[3] M. A. Lodge et al., en, J. Nucl. Med., 2011. D O I: 10.2967/jnumed.111.093583.[4] C. R. R. N. Hunter et al., en, Med. Phys., 2016, eprint:https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4943565.D O I: https://doi.org/10.1118/1.4943565.[5] E. Ovtchinnikov et al., Computer Physics Communications,2020. D O I: https://doi.org/10.1016/j.cpc.2019.107087.[6] M. Modat et al., eng, Comput. Meth. Prog. Bio., 2010. D O I:10.1016/j.cmpb.2009.09.002.[7] W. P. Segars et al., eng, Med. Phys., 2010. D O I: 10.1118/1.3480985.[8] G. Wu et al., English, Cancer. Manag. Res., 2018, Publisher:Dove Press. D O I: 10.2147/CMAR.S177663.[9] J. W. Långsjö et al., eng, Anesthesiology, 2004. D O I: 10.1097/00000542-200405000-00006.[10] R. Boellaard et al., Eur. J. Nucl. Med. Mol. Imaging, 2015.[11] B. F. Holman et al., Phys. Med. Biol., 2016. D O I: 10.1088/0031-9155/61/8/3148.

Systematic Evaluation of the Impact of Involuntary Motion in Whole Body Dynamic PET

https://discovery.ucl.ac.uk/10156723/1/MIC2021_systematic_motion.pdf

Systematic Evaluation of the Impact of Involuntary Motion in Whole Body Dynamic PET

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