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Perfusion-supervoxels for DCE-MRI based tumor subregion assessment
Benjamin John Irving 1, Jolanta Mirecka1, Ana L Gomes2, Danny Allen2, Paul Kinchesh2, Veerle Kersemans2, Stuart Gilchrist2, Sean Smart2, Julia A Schnabel3, Sir J Michael Brady2, and Michael Chappell1

1Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom, 2Department of Oncology, University of Oxford, Oxford, United Kingdom, 3Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

Synopsis

Tumors exhibit chaotic and leaky vasculature, which leads to variations in perfusion, and regions of edema, hypoxia and necrosis. We develop a method to extract perfusion-supervoxels, regions of locally similar perfusion, and use these regions with k-means clustering to define tumor subregions that are robust to noise and outliers. This method offers a number of advantages over both mean tumor parameters and voxelwise clustering.

Purpose

Angiogenesis, the formation of new blood vessels, is important for tumor progression. Tumors exhibit chaotic, leaky vasculature yielding variations in perfusion, which can be assessed with dynamic contrast-enhanced (DCE-)MRI. The vascularity also forms regions of edema, hypoxia and necrosis. Because of this, subregional analysis is key to tumor assessment.

Clustering, based on quantitative imaging parameters is commonly used to define tumor subregions1,2; but depends on the distribution of image-derived features within the tumor, leading to regions that vary on a case by case basis. A cohort of cases can be jointly clustered to define representative region labels3; however, combining the voxels leads to clustering of a very large number of data points based on potentially noisy, motion-affected or partially volumed parameter maps, which affects subregion definition.

We propose a method to extract locally meaningful, contiguous, perfusion subregions from DCE-MRI scans to enable efficient, robust region comparisons across a cohort. We extend Simple Linear Iterative Clustering (SLIC)4 to perfusion scans, and cluster the derived supervoxels into representative tumor subregions. This method is evaluated using DCE-MRI scans taken daily of tumor growth, and is compared to both voxel clustering and mean tumor parameters.

Methods

MRI was performed at 4.7T on 10 female CBA mice with subcutaneously implanted CaNT tumors over 8 days to monitor tumor growth. DCE-MRI was performed using 60 repeats of a cardio-respiratory gated RF and gradient spoiled 3D gradient echo scan every 10-15s. Scan parameters: TR 1.4ms, TE 0.64ms, FA 5o and voxel size 0.42x0.42x0.42 mm3. Gd3+ was automatically infused into the lateral tail vein. Scans for which tail vein cannulation failed were excluded from analysis leaving 69 (of 80 possible) scans. T1 was determined prior to DCE-MRI from a variable flip angle (VFA) scan with 16 FAs ranging from 1o to 7o.

We apply SLIC to 4D DCE-MRI scans using principal component decomposition of perfusion for supervoxelisation5,6. Each tumor was decomposed into 100 spatially contiguous supervoxel subregions with locally-similar perfusion, Figure 1.

The Tofts model was fitted to the DCE-MRI contrast enhancement curves to calculate Ktrans and kep (Figure 2). The supervoxels from all cases were clustered (K-means) into four classes using Ktrans, kep and T1 as features, forming larger regions. The choice of four regions was based on previous studies7. For comparison, K-means was also used to cluster voxels based on the same parameters. We defined voxel contiguity8 as the proportion of neighbors with a different label ($$$l_j$$$) to each voxel of interest: $$D_i = \frac{\mathrm{26neigh \, with \, }l_j \neq l_i}{26}$$ The score for the whole tumor is: $$$D = \frac{1}{N} \sum_{i=1}^N D_i$$$ where $$$N$$$ is the total number of voxels in the tumor. $$$D$$$ ranges between 0 (completely contiguous) and 1 (no contiguity).

Results

Supervoxel clusters are shown in Figure 3. K-means clustering of perfusion-supervoxels creates less noisy, more contiguous regions — with mean (dis)contiguity scores of 0.25 and 0.40, for supervoxel and voxel clustering, respectively. The regions show consistency with tumor growth across multiple scans and days (Figure 4). Regional contiguity improves 3D visualization.

The four region labels offer an improved metric for tumor perfusion relative to using the frequently used mean tumor parameter. Figure 5 shows variation of each of the four regions for Ktrans and kep across the dataset compared to whole tumor parameters. Mean parameters for each region were similar across cases. Ktrans and kep are significantly different between the four subregion groups — using one-way ANOVA with post-hoc Tukey HSD analysis to reject the null hypothesis (p=0.01). Interestingly, while some regions show a link between parameters (both low kep and Ktrans or high kep and Ktrans), other regions show parameter decoupling (high kep, low Ktrans).

K-means voxel clustering is comparatively susceptible to outliers. Figure 5 shows regional parameter values when using voxel based clustering: whilst there were significant quantitative differences between each region, there was correlation between Ktrans and kep in each region, unlike when using the supervoxel method. R0 appears to exhibit the consequences of outliers biasing the clustering.

Discussion and Conclusion

Supervoxel-based analysis methods have a number of advantages over voxel-based analysis for the subregional assessment of tumors. These include: 1) providing spatial regularization to reduce noisy voxel outliers, and 2) reducing the representation of each tumor into contiguous supervoxels that allows efficient comparison across the large datasets of cases.

In this work we show that using perfusion-supervoxels before clustering provides an improved tumor region definition when analyzing a cohort of cases. In future, we will use these subregions to assess the spatial relationship between subregions within the tumor.

Acknowledgements

This research is supported by the CRUK/EPSRC Cancer Imaging Centre at Oxford. Support is also provided by Cancer Research UK (CRUK grants C5255/A12678, C2522/A10339), the Engineering and Physical Sciences Research Council (EPSRC grant C2522/A10339) and the Medical Research Council Unit Grant for the Oxford Institute for Radiation Oncology.

References

1. O’ Connor J, Rose C, Waterton J et al. Imaging introtumour heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res. 2014; 21(2):249-257.

2. Castellani U, Cristiani M, Daducci A et al. DCE-MRI data analysis for cancer area classification. Methods Inf Med. 2009. 48(3):248-253.

3. Henning E, Azuma C, Sotak C et al. Multispectral quantification of tissue types in a RIF-1 tumor model with histological validation. Magn Reson Med. 2007. 57(3):501-512.

4. Achanta R, Shaji A, Smith K et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Trans Pattern Anal Mach Intell. 2012. 34(11):2274-2281.

5. Irving B, Cifor A, Papiez B et al. Automated Colorectal Tumour Segmentation in DCE-MRI using Supervoxel Neighbourhood Contrast Characteristics. MICCAI. 2014. 8673:609-616.

6. Irving B, Franklin J, Papiez, B et al. Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation. Med Image Anal. 2016. 32:69-73.

7. Menze B, Jakab A, Bauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imag. 2015. 34 (10):1993-2024.

8. Theiler J and Gisel G. A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation. SPIE. 1997. 3159:108-118.

Figures

Figure 1: Contiguous perfusion-supervoxels defined on a DCE-MRI scan based on enhancement characteristics. left) shows a cross section through the tumor labelled with supervoxels, and right) shows a 3D rendering.

Figure 2: Ktrans and T1 parameter generation from DCE-MRI and VFA sequences

Figure 3: K-means clustering of each supervoxel from the 69 scan dataset (6900 supervoxels) into four labelled regions. Mean curves are shown for each of the four subregions.

Figure 4: Progression of a single tumor over 8 days in terms of Ktrans, kep and labelled supervoxel regions. For comparison, the voxels are also labelled using k-means clustering based on the same functional features, which is noisy, while clustering supervoxels using perfusion SLIC provides well defined subregions. A 3D rendering is shown (bottom) that illustrates the regional consistency with tumor growth. The yellow necrotic core expands over the 8 days; by day 8 the majority of the tumor is necrotic.

Figure 5: Boxplots of the variation each of the four labelled regions (R0-R3) over the entire dataset for both voxel clustering and supervoxel clustering, and compared to mean parameters for the entire tumor. These contiguous subregions provide a more detailed representation of the tumor than mean tumor values - including a decoupling of the parameter maps i.e. some regions exhibit show [low kep, low Ktrans], and [high kep, high Ktrans] while others exhibit [high Ktrans, low kep]. Supervoxel-clustering provides spatial regularisation to reduce the impact of outliers and, therefore, provides more stable regions compared to voxel-based clustering (note y-axis scaling difference).

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)
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