Front-End Electronics Design for 3-D Position Sensitive TOF-PET Detector That Achieves ~120-ps CTR and ~1.2-mm DOI Resolution

Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study

Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN

Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning

The effects of back‐projection variants in BPF‐like TOF PET reconstruction using CNN filtration – Based on simulated and clinical brain data

AbstractBackgroundThe back‐projection strategies such as confidence weighting (CW) and most likely annihilation position (MLAP) have been adopted into back‐projection‐and‐filtering‐like (BPF‐like) deep reconstruction model and shown great potential on fast and accurate PET reconstruction. Although the two methods degenerate to an identical model at the time resolution of 0 ps, they represent two distinct approaches at the realistic time resolutions of current commercial systems. There is a lack of a systematic and fair assessment on these differences.PurposeThis work aims to analyze the impact of back‐projection variants on CNN‐based PET image reconstruction to find the most effective back‐projection model, and ultimately contribute to accurate PET reconstruction.MethodsDifferent back‐projection strategies (CW and MLAP) and different angular view processing methods (view‐summed and view‐grouped) were considered, leading to the comparison of four back‐projection variants integrated with the same CNN filtration model. Meanwhile, we investigated two strategies of physical effect compensation, either introducing pre‐corrected data as the input or adding a channel of attenuation map to the CNN model. After training models separately on Monte‐Carlo‐simulated BrainWeb phantoms with full dose (events = 3×107), we tested them on both simulated phantoms and clinical brain scans with two dosage levels. For the performance assessment, peak signal‐to‐noise ratio (PSNR) and root mean square error (RMSE) were used to evaluate the pixel‐wise error, structural similarity index (SSIM) to evaluate the structural similarity, and contrast recovery coefficient (CRC) in manually selected ROI to compare the region recovery.ResultsCompared to two MLAP‐based histo‐image reconstruction models, two CW‐based back‐projected image methods produced clearer, sharper, and more detailed images, from both simulated and clinical data. For angular view processing methods, view‐grouped histo‐image improved image quality, while view‐grouped cwbp‐image showed no advantage except for contrast recovery. Quantitative analysis on simulated data demonstrated that the view‐summed cwbp‐image model achieved the best PSNR, RMSE, SSIM, while the 8‐view cwbp‐image model achieved the best CRC in lesions and the white matter. Additionally, the multi‐channel input model including the back‐projection image and attenuation map was proved to be the most efficient and simplest method for compensating for physical effects for brain data. Applying Gaussian blur to the histo‐image yielded images with limited improvement. All above results hold for both the half‐dose and the full‐dose cases.ConclusionFor brain imaging, the evaluation based on metrics PSNR, RMSE, SSIM, and CRC indicates that the view‐summed CW‐based back‐projection variant is the most effective input for the BPF‐like reconstruction model using CNN filtration, which can involve the attenuation map through an additional channel to effectively compensate for physical effects.

A Total-Body Ultralow-Dose PET Reconstruction Method via Image Space Shuffle U-Net and Body Sampling

A deep learning method for total-body dynamic PET imaging with dual-time-window protocols

Multi-modality deep learning-based [68Ga]Ga-DOTA-FAPI-04 PET polar map generation: potential value in detecting reactive fibrosis after myocardial infarction

Microglial Activation Imaging Using ¹⁸F-DPA-714 PET/MRI for Detecting Autoimmune Encephalitis

Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis

Multimodal Fusion Network for Detecting Hyperplastic Parathyroid Glands in SPECT/CT Images

A Shortened Model for Logan Reference Plot Implemented via the Self-Supervised Neural Network for Parametric PET Imaging

An Analytical Algorithm for Tensor Tomography From Projections Acquired About Three Axes

Machine Learning-Based Noninvasive Quantification of Single-Imaging Session Dual-Tracer ¹⁸F-FDG and ⁶⁸Ga-DOTATATE Dynamic PET-CT in Oncology

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization

A back‐projection‐and‐filtering‐like (BPF‐like) reconstruction method with the deep learning filtration from listmode data in TOF‐PET

AbstractPurposeThe time‐of‐flight (TOF) information improves signal‐to‐noise ratio (SNR) for positron emission tomography (PET) imaging. Existing analytical algorithms for TOF PET usually follow a filtered back‐projection process on reconstructing images from the sinogram data. This work aims to develop a back‐projection‐and‐filtering‐like (BPF‐like) algorithm that reconstructs the TOF PET image directly from listmode data rapidly.MethodsWe extended the 2D conventional non‐TOF PET projection model to a TOF case, where projection data are represented as line integrals weighted by the one‐dimensional TOF kernel along the projection direction. After deriving the central slice theorem and the TOF back‐projection of listmode data, we designed a deep learning network with a modified U‐net architecture to perform the spatial filtration (reconstruction filter). The proposed BP‐Net method was validated via Monte Carlo simulations of TOF PET listmode data with three different time resolutions for two types of activity phantoms. The network was only trained on the simulated full‐dose XCAT dataset and then evaluated on XCAT and Jaszczak data with different time resolutions and dose levels.ResultsReconstructed images show that when compared with the conventional BPF algorithm and the MLEM algorithm proposed for TOF PET, the proposed BP‐Net method obtains better image quality in terms of peak signal‐to‐noise ratio, relative mean square error, and structure similarity index; besides, the reconstruction speed of the BP‐Net is 1.75 times faster than BPF and 29.05 times faster than MLEM using 15 iterations. The results also indicate that the performance of the BP‐Net degrades with worse time resolutions and lower tracer doses, but degrades less than BPF or MLEM reconstructions.ConclusionIn this work, we developed an analytical‐like reconstruction in the form of BPF with the reconstruction filtering operation performed via a deep network. The method runs even faster than the conventional BPF algorithm and provides accurate reconstructions from listmode data in TOF‐PET, free of rebinning data to a sinogram.