Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging

The authors evaluate the performance of shape-based averaging (SBA) technique for whole-body bone segmentation from MRI in the context of MRI-guided attenuation correction (MRAC) in hybrid PET/MRI. To enhance the performance of the SBA scheme, the authors propose to combine it with statistical atlas fusion techniques. Moreover, a fast and efficient shape comparison-based atlas selection scheme was developed and incorporated into the SBA method.

Clinical studies consisting of PET/CT and MR images of 21 patients were used to assess the performance of the SBA method. In addition, the authors assessed the performance of simultaneous truth and performance level estimation (STAPLE) and the selective and iterative method for performance level estimation (SIMPLE) combined with SBA. In addition, a local shape comparison scheme (L-Shp) was proposed to improve the performance of SBA. The SIMPLE method was applied globally (G-SIMPLE) while STAPLE method was employed at both global (G-STAPLE) and local (L-STAPLE) levels. The evaluation was performed based on the accuracy of extracted whole-body bones, fragmentation, and computation time achieved by the different methods. The majority voting (MV) atlas fusion scheme was also evaluated as a conventional and commonly used method. MRI-guided attenuation maps were generated using the different segmentation methods. Thereafter, quantitative analysis of PET attenuation correction was performed using CT-based attenuation correction as reference.

The SBA and MV methods resulted in considerable underestimation of bone identification (Dice ≈ 0.62) and high factious fragmentation error of contiguous structures. Applying global atlas selection or regularization (G-STAPLE and G-SIMPLE) to the SBA method enhanced bone segmentation accuracy up to a Dice = 0.66. The best results were achieved when applying the L-STAPLE method with a Dice of 0.76 and the L-Shp method with a Dice of 0.75. However, L-STAPLE required up to five-fold increased computation time compared to the L-Shp method. Moreover, both L-STAPLE and L-Shp methods resulted in less than 3% SUV mean relative error and 6% SUV mean absolute error in bony structures owing to superior bone identification accuracy. The quantitative analysis using joint histograms revealed good correlation between PET-MRAC images using the proposed L-Shp algorithm and the corresponding reference PET-CT images.

The performance of SBA was enhanced through application of local atlas weighting or regularization schemes (L-STAPLE and L-Shp). Bone recognition, fragmentation of the contiguous structures, and quantitative PET uptake recovery improved dramatically using these methods while the proposed L-Shp method significantly reduced the computation time.