Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction

Abstract

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.

Magnetic Resonance-Based Attenuation Correction and Scatter Correction in Neurological Positron Emission Tomography/Magnetic Resonance Imaging—Current Status With Emerging Applications

“…Gong et al [102] used a convolutional neural network with Dixon images only or in a combination of Dixon and ZTE images to generate a continuous valued attenuation map. Similarly, a deep convolutional neural network that derived attenuation maps based on ZTE images was shown to outperform both ZTE and atlas-based method in Blanc-Durand et al [43]. Interestingly, while most evaluations have been performed with adults with normal anatomy, Ladefoged et al [103] evaluated deep learning methods in pediatric brain tumor patients, with robust performance.…”

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

“…The only major distinction is that style transfer algorithms for natural images focus on general structures and signature properties; however, synthetic CT generation requires quantitative accuracy, thus local intensity prediction plays a key role. Overall, deep learning approaches seem to exhibit better (at least comparable) performance for PET quantification compared to existing state-of-the-art approaches in whole body Hwang et al 2019), pelvic (Arabi et al 2018;Torrado-Carvajal et al 2019), and brain imaging (Liu et al 2017;Gong et al 2018;Blanc-Durand et al 2019). These approaches require at least one MR sequence as input for CT synthesis, whereas deep learning-based joint estimation of attenuation and emission images from TOF PET raw data (Hwang et al 2019;Hwang et al 2018) and direct scatter and attenuation correction in the image domain (Yang et al 2019;Bortolin et al 2019;Shiri et al 2020a;, and sinogram domain (Arabi and Zaidi 2020) could possibly obviate the need for any structural/anatomical images.…”

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging

“…Fifth, the vendor-provided ZTE-and Atlas-MRAC are under continuous development and re ected the state of the art at the time when the study was conducted. At the time of this writing, the version of ZTE-MRAC as used in the present work (MP26) is commercially available [52], but further developments have already been proposed [53,54]. Some may have already been implemented but were outside the scope of this study.…”