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Deep residual inception encoder‐decoder network for amyloid PET harmonization

Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy.

including the Alzheimer's Disease Neuroimaging Initiative, 6 the Dominantly Inherited Alzheimer's Network 7 and others. 8,9 It is determined that amyloid plaques can be detected at least 15 years prior to AD symptom onset 10 and the prevalence of amyloid positivity increases with age from approximately 10% at age 50% to 44% at age 90 in cognitively normal populations. 11 Imaging measurements of brain amyloid and tau pathology help to define AD in its preclinical stage and allow the investigation of the genesis and progression of AD. 12 Many clinical trials have been designed to include amyloid and tau PET imaging for the assessment of treatment efficacy and target engagement as surrogate biomarkers. [13][14][15][16] Human amyloid imaging started more than 15 years ago with the development of Pittsburgh Compound-B (PIB), 17 and has since been widely adopted by many research groups. 18,19 Because of its short half-life (20 min), the use of PIB is limited to large research centers with access to onsite cyclotron and experienced radiochemistry teams. A number of F18 labeled amyloid tracers were later developed to address this limitation including florbetapir (FBP), 20 florbetaben (FBB), 21 flutemetamol, 22 and NAV4694, 23 with the first three subsequently receiving FDA approval for amyloid imaging. With multiple PET tracers designed for the same target pathology, each tracer has its own target binding affinity, tracer kinetic behavior, non-specific binding, and tissue retention, hence the imaging data that are acquired display tracer-dependent characteristics. Recent crosssectional comparison studies demonstrated that the global amyloid burden measures derived from PIB and FBP have a shared variance ranging from approximately 70% to 90% depending on the quantification pipelines and cohorts. [24][25][26] These tracers also show different levels of variability in the amyloid burden measurements. [24][25][26] Intertracer variability leads to inconsistent amyloid positivity threshold and poses challenges for multicenter studies. A mean cortical FBP standard uptake value ratio (SUVR) cutoff of 1.17 was determined to detect moderate to frequent brain amyloid burden based on pathological assessment 27 and this can be converted to a Centiloid (CL) cutoff of 37.1 CL using published equations 24 ; a recent study based on PIB imaging found a threshold of 20.1 CL to be optimal 28 ; and a FBB based study determined a threshold of 19 CL. 29 The CL approach 30 was proposed to define a common numerical scale hoping to unify the global amyloid measures derived from different tracers and analysis pipelines. However, the amyloid measurements still have the same level of correlation between tracers, and the inherent signal to noise property also remains the same. Differences in amyloid measurements across tracers also pose problems for longitudinal studies. The tracer difference results in different capabilities of tracking longitudinal amyloid accumulation which is especially important in clinical trials. In our recent study, we estimated that the sample size needed to detect a 20% reduction in the rate of amyloid accumulation was 305 per arm when PIB is used as the amyloid tracer while a sample size of 2156 is needed for FBP. 25 Furthermore, strategies enabling the detection of focused changes and investigating the spatial patterns of pathological changes which require regional and voxel-level details are currently lacking. One viable solution may be the emerging Artificial Intelligence technology: deep learning (DL).
Deep learning has been successfully implemented in computer vision domains for decades. Only recently has it become a technique

RESEARCH-IN-CONTEXT
1. Systematic review: To quantify beta-amyloid deposition in the brain, the use of multiple amyloid tracers with varied characteristics poses a major challenge to interpretation, to the ability to combine results from crosscenter studies, and to efforts to define a common positivity threshold. We propose a deep learning (DL) model as a harmonization strategy to generate imputed amyloid PET images of one amyloid tracer to the images of another.  34 is an end-to-end framework for prediction and biomarker discovery. Most recently, one DL approach is gaining traction to generate synthetic images of a missing modality based on input images of a related but different modality. 33,35 This approach was initially developed to address the missing data problem 33 and later adopted in estimating the attenuation map from magnetic resonance (MR) imaging data to allow accurate attenuation correction for PET/MR hybrid scanners. 35 We previously developed a new model termed Residual Inception Encoder-Decoder Neural Network (RIED-Net) 36 to render enhanced images from Contrast-Enhanced Digital Mammography and support breast cancer diagnosis. The success of RIED-Net motivated this research to explore its applicability to harmonizing PET imaging from different tracers, specifically, generating synthetic PIB images from FBP data and evaluate its performance using two independent datasets, one for training, validation, and testing, and one for external validation.

Participants
From the Open Access Series of Imaging Studies-3 dataset, 37

Imaging
For the OASIS dataset, dynamic PIB PET scan was acquired on a Siemens Biograph 40 PET/CT or a Siemens/CTI EXACT HR+ scanner for 60 minutes after tracer administration and reconstructed using standard iterative methods with attenuation and scatter correction.
Dynamic FBP PET was acquired on a Siemens Biograph mMR scanner for 70 minutes after FBP administration and reconstructed using an OSEM algorithm and attenuation/scatter corrected using a separately acquired low dose CT scan. For each participant, a T1-weighted MR scan was also acquired using a 3T MR scanner. All imaging was conducted at the Washington University in St. Louis, and individual scans for each participant were completed within 3 months. The imaging acquisition information for the GAAIN dataset has been previously described in. 24 Briefly, PIB PET was acquired between 50 and 70 minutes post-injection, and FBP PET was acquired 50 to 60 minutes. The imaging pair was obtained on average of 18 days apart, and a 3T T1 MR image was obtained for each subject within 6 months of PET acquisition. One participant in the GAAIN cohort was excluded from further analysis due to poor quality of the T1 MR scan.
The T1-weighted MR data were analyzed using FreeSurfer (Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA) to define anatomical regions. Amyloid PET imaging quantification was then performed using our standard protocols that included scanner harmonization, motion correction, target registration, and regional value extraction 38,39 using a PET unified pipeline. The output included a SUVR image using cerebellar cortex as the reference region and a mean cortical SUVR (MCSUVR) as the global index of brain amyloid burden. 38

Deep learning model for PET harmonization
RIED-Net was designed to estimate the voxel-wise non-linear mapping between input (FBP) and output (PIB) images. In this mapping, letting the FBP image be ∈ R m × n , and the PIB image be O ∈ R m × n , the relationship between the two can be defined as where S : R m × n → R m × n denotes the nonlinear mapping between the FBP-PIB pair. The image synthesis problem is to make an estimation of the function The overall architecture of RIED-Net is shown in Figure 1. It consists of nine residual blocks, where the encoding path consists of five blocks and the decoding path of the remaining four blocks using an architecture similar to U-Net 41 and with the addition of a residual inception shortcut path that has been shown to improve training efficiency. 42 Each block has a conventional convolution/deconvolution path with two 3 × 3 convolutional layers, and in parallel a 1 × 1 convolution path, and the output matrices from these two parallel paths are summed together and down/up sampled by a factor of 2 to serve as the input to the next block. Additional technical details are provided in Supplementary Material.
We used 10-fold cross validation in the training. Specifically, for the OASIS dataset, we shuffled the dataset randomly and created 10 different groups of the dataset; for an even split, we decided to use 90 out of 92 total samples (excluding the last two participants according to alphabetical order) and created 10 different folds of size 81:9 (total 90), where 81 were used for training and validation, and nine were used for testing. These folds are generated such that there is no overlap among the training and testing samples and the test dataset in each fold is The RIED-Net model in this work adopted a U-Net-like architecture with the addition of a residual inception short-cut path which has been shown to improve the efficiency of model training. The overall model has five encoding blocks and four decoding blocks. Each blue rectangle represents a data matrix generated from the convolution operations (arrows) within the encoding blocks, and the number below each rectangle indicates the number of channels within each matrix. The leftmost thin blue rectangle represents the input data, a 2D slice from the florbetapir image (256 × 256 × 1 matrix, i.e., one channel). Similarly, each green rectangle represents a data matrix generated from the convolution/deconvolution operations within the decoding blocks, and again the number below indicates the number of channels within the corresponding matrix. Notice as the input of each decoding layer, a blue rectangle from the matching encoding block is appended by a green rectangle from the output of the previous block. Each brown arrow represents a multi-channel convolutional operation with a 3 × 3 kernel and a rectified linear unit (ReLu) as the activation function (Conv 3×3, ReLu). Each orange arrow denotes a 1×1 convolutional operation with a ReLu as the activation function (Conv 1 × 1). The single purple arrow (representing the last step in this network) denotes a 1×1 convolutional operation that generates the output of synthetic 2D Pittsburgh Compound-B slice. Each black dotted arrow denotes a copying operation. Each red arrow denotes a 3×3 convolutional operation (stride = 2, with a ReLu as the activation function), and each green arrow denotes a 3×3 deconvolutional operation (stride = 2, with a ReLu as the activation function

Statistical analysis
We assessed the performance of RIED-Net in harmonizing the PIB and   The RIED-Net model is readily applicable to new FBP scans, provided that the FBP scans are fully processed following the same procedure described in our Methods section. Therefore, our proposed DL technique is a promising approach for the harmonization of PET imaging data obtained from different tracers targeting the same underlying pathophysiology.

RESULTS
Compared to DL models from the literature, RIED-Net has two major advantages. First, existing methods use patch-based approaches to alleviate computational burden, but this sacrifices synthesis performance at the voxel level. RIED-Net was designed focusing on voxel mapping with its performance proven to be satisfactory using two separate datasets for validation and testing in this study. The second advantage is from the residual inception block which made RIED-Net computational affordable, thus it has the potential to perform PET harmonization task in 3D which is one ongoing effort. Recent studies [45][46][47] have also proposed to use Generative Adversarial Networks (GANs) for image-to-image translation and generating highly realistic images.
GAN models estimate complex non-linear relationships by learning estimation of joint probability distribution of the paired images at the whole image scale, but we contend the performance of GAN models on voxel-to-voxel level translation may be questionable. In our current implementation, a 2D RIED-net model was adopted to work with the limited tracer comparison data that are currently available to train a DL model which typically requires thousands of images. Operating in 2D mode, a moderate sized dataset like the OASIS cohort we used in this study with 92 subjects provided more than 20 thousand slices In this work, SUVR images were first transformed into template space before feeding to the RIED-net model and only slices within a common field of view were used so that the model was less affected by the variability of patient orientation and field of view coverage.
When native space images were used to train the RIED-net model, similar performance was achieved within the cross-validation process but failed to generalize to the independent testing data. This suggests spatial normalization as a necessary preprocessing step to obtain favorable results, at least with the moderate amount of data available. Further investigation is warranted to determine whether rigid transformation or a full nonlinear spatial normalization procedure would further improve the model. In summary, we demonstrated for the first time that a DL approach can be used to harmonize amyloid PET imaging data from two different tracers to provide highly interchangeable amyloid measurement.
This approach may also become invaluable for addressing similar problems such as the harmonization of tau PET imaging data from different tracers.

ACKNOWLEDGMENTS
The research is supported in part by R01AG031581, R01AG069453,