Int J CARS (2008) 3:105–113 DOI 10.1007/s11548-008-0212-y
ORIGINAL ARTICLE
Combined motion blur and partial volume correction for computer aided diagnosis of pulmonary nodules in PET/CT Rafael Wiemker · Timo Paulus · Sven Kabus · Thomas Bülow · Ivayla Apostolova · Ralph Buchert · Susanne Klutmann
Received: 9 January 2008 / Accepted: 6 May 2008 / Published online: 27 May 2008 © CARS 2008
Abstract Objective We present an automated scheme to correct PET max-uptake-values of small to medium-sized pulmonary nodules for motion blur and partial volume averaging. Both effects cause significant underestimation of PET max-uptakevalues, particularly in nodules below 25 mm diameter, but nodules up to 75 mm might be affected. This compromises the power of PET for the differential diagnosis of such nodules, in particular benign versus malignant. Thus, correcting PET max-uptake-values has the potential to improve the classification of PET-positive pulmonary nodules. Methods The proposed correction algorithm relies on (i) determination of the actual size and shape of the nodule by segmentation of the nodule in the CT image and (ii) estimation of the effective local point-spread-function in the PET image, taking into account not only the inherently limited spatial resolution of the PET scanner, but also respiratory motion effects. Then the expected under-estimation of the PET max-uptake value in the nodule can be computed by simulation, and the correct PET max-uptake is obtained by multiplication with the correction factor (inverse of the under-estimation/recovery factor). Results Depending on the estimated nodule shape and blur width, the resulting SUV correction factors ranged from 1.0 to 11, with an average correction factor of 3.0, with higher values for smaller nodules. In comparison to SUV correction using a simplified spherical nodule model, the true-shape R. Wiemker (B) · T. Paulus · S. Kabus · T. Bülow Philips Research Europe-Hamburg, Röntgenstrasse 24, 22335 Hamburg, Germany e-mail:
[email protected] I. Apostolova · R. Buchert · S. Klutmann Department of Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
SUV correction factors were on average 30% higher. The feasibility of the method presented here is indicated by the high correlation between fitted and observed PET image profiles for clinical cases (average 0.995). Conclusion Blur and motion correction factors for standardized PET uptake values may significantly change the differential diagnosis of small pulmonary nodules. Feasibility and stability of the proposed automated combined SUV correction method as well as ease of use of the software tool have been demonstrated by retrospective analysis of real PET/CT patient datasets from clinical routine. Keywords Solitary pulmonary nodule · Computer-aided diagnosis · PET recovery coefficient · SUV correction · Partial volume effect · Motion compensation Introduction: Correction of underestimated tracer uptake in PET of small pulmonary nodules The evaluation of pulmonary nodules is a clinical routine task, since high resolution CT scanners show nodules in as many as 50% of patients in lung-cancer high-risk groups. Fortunately a high percentage of pulmonary nodules are benign. The differential diagnosis of pulmonary nodules depends strongly on non-imaging factors such as patient age, history, and risk group. Additionally to chest X-ray and CT, and in order to avoid biopsies, increasing use is made of functional modalities such as PET/CT for non-invasive differential diagnosis. Classification of pulmonary nodules in PET/CT relies on size, shape, and localization as assessed in the CT image, and the magnitude of FDG-tracer uptake as assessed in the PET image. The tracer uptake is preferably measured using the standardized uptake value (SUV), determined by dividing the measured activity by injected tracer dose and normalizing
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underestimation of SUV-max
Fig. 1 Illustration of the underestimation of the SUV-max value by blurring. The phantom image (left) shows the influence of the size of the nodule on its apparent SUV-max value (all phantom spheres are filled with FDG-tracer of equal concentration)
to body weight [1,2]. In order to support visual analysis of PET-positive pulmonary nodules, a quantitative measure of tracer uptake in the nodule is obtained by determination of the maximum voxel value of tracer uptake within the nodule (max-uptake-value, max-SUV). It is often assumed that an appropriate cut-off on the max-uptake-value, typically in the range between 2.0 and 3.0 SUV, allows discrimination of malignant and benign pulmonary nodules (if max-uptake is smaller than cut-off then this indicates benign, otherwise malignant) [2–6]. However, the apparent tracer uptake in the PET images underestimates the actual tracer uptake (Fig. 1) because of limited spatial resolution, partial volume effect and motion during the scan acquisition [7–11]. Affected are all structures which are smaller than about three times the spatial resolution (full width at half maximum, FWHM) in at least one direction. FWHM of modern PET systems ranges between 5 and 10 mm in practical clinical settings for whole-body imaging. Due to detector geometry, FWHM is also spatially varying within the field of view (FOV). In PET images of patients, in contrast to phantoms, there is additional blur due to motion. Within the lungs, respiratory motion cannot be avoided during the PET acquisition (≥60 s per bed position). Respiratory gating is possible but compromises either SNR or spatial resolution. Free-breathing CT examinations show that the FWHM of the respiratory motion even in flat breathing can be up to 20 mm in the basal lung area but is negligible in the apical area [12,13]. Thus, from the combination of the recovery/partial volume effect and motion blur, an effective point-spread-function (PSF) with 5–25 mm FWHM should be considered for clinical PET images of pulmonary nodules (FWHMs add quadratically). Now, for a spherical lesion with a diameter equal to the effective FWHM, the apparent max-uptake-value in the PET image is as low as 30% of the true uptake-value [7–10]. Thus, strict application of a cut-off might result in false negative findings, i.e. misclassification of a malignant nodule as benign. This is particularly relevant, as the size of pulmonary nodules for which a non-invasive differential diagnosis would be most beneficial, because their small size makes biopsies difficult and their general malignancy likelihood is below 50%, is just
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Fig. 2 Sketch of the underlying principle for the computation of the correction factor. The shape of the nodule is segmented in the CT image volume and cast into a label image volume of uniform intensity. The PET image volume is simulated by convolving this image with a Gaussian filter of a given FWHM. The best suitable blur FWHM can be obtained by comparing the simulated image to the observed PET image. The recovery coefficient (blur-caused underestimation of the peak value) can be measured in the simulated image before and after convolution
within the range ≤25 mm. Therefore, correction of apparent max-uptake-values for effective FWHM has the potential to improve the accuracy of PET, particularly its negative predictive value, in the diagnosis of pulmonary nodules [5–11,14]. The mechanism of the blurring is well understood [7,8]. For a local image region it can be approximated as a convolution of the signal with a PSF, however, this PSF is locally varying. If the local PSF is known, then it is straightforward to compute the correction factor for each individual lung nodule, once its size and shape have been determined. Earlier studies have shown that it is beneficial to use tabulated pre-computed SUV correction factors for spherical bodies [14]. In these efforts, however, apart from neglecting the nonsphericity of many nodules, the nodule diameter estimation was made by visual inspection and a global spatially invariant PET blur width was assumed. In the approach presented here the actual nodule shape is taken into account, and the effective PSF (combined from motion and limited resolution blur) is estimated locally from the observed PET image values (Fig. 2). If the blur PSF is known then in principle a deconvolution of the image is possible [15]. Image convolution, however, is known to be prone to noise and may introduce artifacts. Therefore we have limited our approach to only compute an SUV correction factor from the specific nodule shape and locally estimated PSF. We present a software tool (Fig. 3) for computer-aided diagnosis (CADx) of pulmonary nodules in PET/CT image data sets, which combines nodule-specific segmentation, volumetry, local PET/CT fine-registration, estimation of effective FWHM, and computation of the corrected maxuptake-value.
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Fig. 3 Screenshot of the graphical user interface
Methods: Combination of CT nodule segmentation and local PET blur width estimation Nodule segmentation in CT For computation of the SUV correction factor, the size and shape of the nodule are crucial. The natural choice for the segmentation of the nodule is the CT image data, as current PET image data is much too poorly resolved to allow reliable segmentation of small nodules. In contrast, in CT data sets (either from a previous diagnostic CT scan of the patient or from a combined PET/CT scanner), the nodules are better resolved both spatially and temporally and thus even small nodules can be segmented in 3D. Resampling to isotropic voxel grid CT images of combined PET/CT scanners are typically reconstructed anisotropically with relatively large slice thickness (e.g. 5 mm). Furthermore, the sampling grid is different from the coarser PET image volume. Therefore, as a preprocessing step, we are resampling a local volume-of-interest (VOI) around the selected nodule to isotropic resolution, using spline-interpolation for the CT image and nearestneighbor-interpolation for the PET data. The underlying rationale is that the nodule segmentation method used for this study works on the CT data, and is a voxel-based method relying on voxel-intensity thresholds. Therefore smooth segmentation results require the up-sampling of the originally
anisotropic data, and the smoothness and perceived accuracy of the segmentation is improved by using a more sophisticated interpolation method than simple tri-linear interpolation, although the choice of the interpolation method changed nodule volumes typically by 40 mm) and cases in which the nodule shape was so ill-defined that the automated segmentation in CT was either not possible or visual inspection was ambiguous have been excluded. The automated segmentation of the nodules in the CT images yielded volume-equivalent diameters ranging from 7 to 37 mm (average 16.5 mm, volume range 175–27,400 mm3 , see Table 1). The apparent, i.e. non-corrected max-uptakevalue (max-SUV) ranged from 0.9 to 11 SUV (4 nodules 0.9–2 SUV, 9 nodules 2–4 SUV, 3 nodules 4–8 SUV, 2 nodules 8–11 SUV). The locally fitted combined PET blur width ranged from 7 to 14 mm (FWHM), average 11 mm. The profile fit of the observed versus simulated tapering-off of the PET values around the hot spot appeared very satisfactory in most cases (average correlation of 0.995). The local registration refinement (purely translational) is an essential step and resulted in an average displacement vector of 5.8 mm (±3.1). Depending on the estimated nodule shape and blur width, the resulting SUV correction factors ranged from 1.0 to 11, with an average correction factor of 3.0, with higher values for smaller nodules. In our test series, all corrected SUV max values were larger than 3.5 SUV after the correction was applied.
One advantage of the method presented here is that the SUV-max correction factor is computed using the actual shape of the nodule of interest rather than a spherical approximation, as it could be looked up in a pre-computed table [14]. We have compared the SUV blur correction factors as computed from the actual shape of the nodule to the correction factor for a spherical body of equal volume. Since a sphere is the most compact shape, these spherical correction factors are always smaller or equal to the true-shape correction factors. On average, the true-shape correction factors were larger by 30%. Since the available CT reconstructions from clinical routine were of relatively large slice thickness (5 mm), the nodule segmentation in CT has a certain ambiguity. When the Hounsfield threshold for the automated segmentation was changed from −400 HU to −300 HU, the nodule diameter average decreased by 11%. As a result, the average SUV-max correction factor increased from 3.0 to 4.2. This is an expected systematic effect, with a high correlation (0.98) between the two different SUV correction factors for each nodule. The magnitude of this systematic effect (caused by segmentation ambiguity) will decrease substantially if thin-slice CT reconstructions are used, with a small reconstruction field of view (FOV restricted to the lungs for optimal spatial resolution).
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Table 1 Properties of tested nodule (ordered by size) Eff. diametera (mm)
Mean SD
Volumeb (mm3 )
Refinement shift inc (mm)
PET FWHM fitd (mm)
Corre
PET maxf (SUV)
Max corrg (SUV)
Correction factor CFh
CF spherei
CF dev from sphere in %j
6.9
175
4.9
7
0.9958
2.1
9.1
4.3
3.2
32
7.9
261
6.9
12
0.9970
0.9
10.2
11.2
8.8
27
8.2
292
2.6
7
0.9957
2.3
6.5
2.9
2.3
27
8.9
370
1.2
10
0.9951
2.9
15.5
5.3
4.0
32 23
11.6
816
4.8
12
0.9979
0.9
3.9
4.2
3.4
12.0
914
6.3
11
0.9990
2.2
10.5
4.7
2.6
83
12.3
972
5.3
11
0.9999
1.6
4.7
3.0
2.5
21
12.4
997
5.2
11
0.9979
4.3
13.8
3.2
2.4
32 25
13.4
1248
1.7
9
0.9958
2.3
4.3
1.9
1.5
14.7
1,665
3.7
9
0.9836
3.4
7
2.0
1.3
55
15.1
1,787
7.1
12
0.9974
1.5
3.6
2.4
2.0
20
19.5
3,907
12.1
14
0.9993
8.4
15.4
1.8
1.6
15
19.6
3,913
6.4
10
0.9901
3.2
4.2
1.3
1.1
21
22.4
5,908
9.6
11
0.9993
4.8
6.2
1.3
1.1
23
23.2
6,507
6.4
11
0.9982
6.5
7.9
1.2
1.0
17
23.5
6,825
10.9
13
0.9799
3.6
4.9
1.3
1.1
21
27.9
11,410
1.2
11
0.9890
2.8
3.7
1.3
1.0
32
37.4
27,388
7.8
13
0.9959
10.6
10.9
1.0
1.0
4,186.389
5.7833333
10.77778
0.99482
3.572222
7.905556
3.016667
2.327778
28.2
6,555.248
3.1079121
0.005603
2.592706
4.021921
2.442335
1.861311
17.2
16.49444 8.045347
1.895988
2
a
Effective diameter (i.e. volume-equivalent diameter) from CT segmentation Volume from CT segmentation c Length of displacement vector of local fine registration between CT-VOI and PET-VOI around the nodule (rigid translational, without rotation or scaling) d Result of the fitting of the PET-PSF to the ‘onion-skin-profile’: the FWHM (in mm) which yielded the optimal correlation between observed; from exhaustive search in 1 mm steps e Optimal correlation of PET-PSF-fit, between observed and simulated onion-skin-profile f Maximum PET standard-uptake-value (SUV) inside the CT-segmented nodule volume (after local reg.) g Max-SUV in inside nodule volume after application of the correction factor h Correction factor computed from nodule segmentation and PSF fit (inverse of recovery coefficient) i Correction factor computed for a spherical nodule of the same volume j Percentage deviation between correction factor using actual shape versus spherical simplification b
Conclusions The blur-related underestimation of the PET max-uptakevalue of pulmonary nodules might be effectively corrected for by modeling the blurring using CT nodule segmentation and deriving the blur width from the PET data. We do not try to separate the two blur effects (motion and partial volume effect), but rather determine their combined effective magnitude locally. We see three main advantages over the use of precomputed tabulated SUV correction factors for spherical nodules: (i) the automated 3D-nodule segmentation is more accurate and reproducible than visual diameter measurement, (ii) the non-sphericity of many nodules leads to higher correction factors, and (iii) the PET blur width (FWHM) varies
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locally throughout the lung and the scanner FOV. The stability of the proposed correction algorithm presented here is indicated by the high correlation between fitted and observed PET image values for clinical cases (average 0.995). Our results show that the SUV correction factors in themselves as well as the deviation from spherical correction factors are non-negligible. The graphical user interface requires only very few interactions of the user after a nodule has been selected. Current theoretical limitations of the method include the assumption that the tracer uptake is uniform over the whole nodule, or alternatively, modulated by the CT density. While this assumption most likely is valid for very small nodules it might be violated in larger nodules. However, the assumption leads to a conservative estimate of the SUV correction factor
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which would be even higher if only a small radio-active hot spot were embedded in a larger lesion. The main practical limitation of the tool lies in the fact that some nodules are not clearly delineated and their segmentation is ambiguous. The potential of the correction of max-uptake-values to improve diagnostic accuracy is currently evaluated using biopsy and/or follow-up as gold standard. We believe that the corrected SUV-max values, even though based on several assumptions, are better then the currently used uncorrected ones [11]. However, multi-center studies are necessary to show whether clinical guide lines have to evolve accordingly. Acknowledgements We would like to thank Winfried Brenner and Janos Mester (University Medical Center Hamburg-Eppendorf), Joel Karp (University of Pennsylvania, Philadelphia), Mark Smith and Vasken Dilsizian (University of Maryland, Baltimore), Bernd Gagel (University Medical Center Aachen), Steffen Renisch, Roland Opfer, Lothar Spies and Ralph Brinks (Philips Research Europe, Hamburg and Aachen), Amy Perkins, Piotr Maniawski, Patrick Olivier and Daniel Gagnon (Philips Medical Systems Nuclear Medicine, Philadelphia and Cleveland) for providing clinical data and fruitful discussions.
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