MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning

Abstract

Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based method to derive patient-specific computed tomography (CT) maps from routine T1-weighted MRI in their native space for attenuation correction of brain PET. We developed a machine-learning-based method using a sequence of alternating random forests under the framework of an iterative refinement model. Anatomical feature selection is included in both training and predication stages to achieve optimal performance. To evaluate its accuracy, we retrospectively investigated 17 patients, each of which has been scanned by PET/CT and MR for brain. The PET images were corrected for attenuation on CT images as ground truth, as well as on pseudo CT (PCT) images generated from MR images. The PCT images showed mean average error of 66.1 ± 8.5 HU, average correlation coefficient of 0.974 ± 0.018 and average Dice similarity coefficient (DSC) larger than 0.85 for air, bone and soft tissue. The side-by-side image comparisons and joint histograms demonstrated very good agreement of PET images after correction by PCT and CT. The mean differences of voxel values in selected VOIs were less than 4%, the mean absolute difference of all active area is around 2.5%, and the mean linear correlation coefficient is 0.989 ± 0.017 between PET images corrected by CT and PCT. This work demonstrates a novel learning-based approach to automatically generate CT images from routine T1-weighted MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. Reconstructed PET images using the PCT exhibit errors well below accepted test/retest reliability of PET/CT indicating high quantitative equivalence.

Figure 1.

The flow chart of the proposed learning-based PET/MRI attenuation correction.

Figure 2.

Schematic flow chart of the ACM used in our proposed algorithm for MRI-based PCT generation. The left part of this figure shows the training stage of our proposed method, which consisted of k random forests training and context information f_PCT extraction. The right part of this figure shows the prediction stage. In the prediction stage, a new MR image follows the similar sequence of the left part to generate a PCT image.

Figure 3.

The axial views of one patient at different slices. Rows (a)–(c) show MR, CT and PCT, respectively. Note the head holder visible in the CT images was added to the PCT data prior to reconstruction of MR-PET data.

Figure 4.

MAE (a), DSC (b) and correlation coefficient (c) between PCT and CT images among all 17 patients.

Figure 5.

PET images after correction on the axial (a) and (d), sagittal (b) and (e) and coronal (c) and (f) planes. Left (a)–(c) and right (d)–(f) demonstrate images from two patients. CT-PET and MR-PET images are shown in column (1) and (2), respectively. The relative difference maps between (1) and (2) are shown in (3). The yellow dotted lines on (a1) and (d1) indicate the positions of profiles displayed in figure 6.

Figure 6.

Comparison of PET image profiles and joint histograms between CT-PET and MR-PET. Upper (a) and bottom (b) correspond to the results from two patients (i.e. left (a)–(c) and right (d)–(f) in figure 5), respectively. The positions of profiles (a1) and (b1) are indicated by yellow dotted lines in figure 5. In (a2) and (b2), the blue scattered dots are joint histograms with the identity line in red for reference.

Figure 7.

Correlation coefficients between MR-PCT and CT-PCT images among all 14 patients.

Figure 8.

Percentage differences within VOIs between CT-PET and MR-PET among 14 patients. The central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the ‘+’ symbol. The indices of VOIs are indicated in table 2.