Abstract
Abstract—Given the complicated relationship between the
Magnetic Resonance Imaging (MRI) signals and the attenuation
values, the attenuation correction in hybrid Positron Emission
Tomography (PET)/MRI systems remains a challenging task.
Currently, existing methods are either time-consuming or require
sufficient samples to train the models. In this work, an efficient
approach for predicting pseudo computed tomography (CT)
images from T1- and T2-weighted MRI data with limited data is
proposed. The proposed approach uses improved neighborhood
anchor regression (INAR) as a baseline method to pre-calculate
projected matrices to flexibly predict the pseudo CT patches.
Techniques, including the augmentation of the MR/CT dataset,
learning of the nonlinear descriptors of MR images, hierarchical
search for nearest neighbors, data-driven optimization, and
multi-regressor ensemble, are adopted to improve the effectiveness of the proposed approach. In total, 22 healthy subjects were
enrolled in the study. The pseudo CT images obtained using
INAR with multi-regressor ensemble yielded mean absolute error
(MAE) of 92.73 ± 14.86 HU, peak signal-to-noise ratio of 29.77
± 1.63 dB, Pearson linear correlation coefficient of 0.82 ± 0.05,
dice similarity coefficient of 0.81 ± 0.03, and the relative mean
absolute error (rMAE) in PET attenuation correction of 1.30 ±
0.20% compared with true CT images. Moreover, our proposed
INAR method, without any refinement strategies, can achieve
considerable results with only seven subjects (MAE 106.89 ±
14.43, rMAE 1.51 ± 0.21%). The experiments prove the superior
performance of the proposed method over the six innovative
methods. Moreover, the proposed method can rapidly generate
the pseudo CT images that are suitable for PET attenuation
correction.
Index Terms—pseudo CT prediction, PET attenuation correction, neighborhood anchor regression, data-driven optimization.
Modules
Algorithms
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL