AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols.

Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level.

Purpose: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges estimated from measured images were deviated because of limited spatial resolutions of the developed x-ray camera as well as statistical noise in the images.

NEMA NU 4-2008 performance evaluation and MR compatibility tests of an APD-based small animal PET-insert for simultaneous PET/MR imaging.

An avalanche photodiode (APD)-based small animal positron emission tomography (PET)-insert was fully evaluated for its PET performance, as well as potential influences on magnetic resonance imaging (MRI) performance. This PET-insert has an extended axial field of view (FOV) compared with the previous design to increase system sensitivity, as well as an updated cooling and temperature regulation to enable stable and reproducible PET acquisitions. The PET performance was evaluated according to the National Electrical Manufacturers Association NU4-2008 protocol.

Performance evaluation of dual-ended readout PET detectors based on BGO arrays with different reflector arrangements.

Objective: Dual-ended readout depth-encoding detectors based on bismuth germanate (BGO) scintillation crystal arrays are good candidates for high-sensitivity small animal positron emission tomography used for very-low-dose imaging. In this paper, the performance of three dual-ended readout detectors based on 15 × 15 BGO arrays with three different reflector arrangements and 8 × 8 silicon photomultiplier arrays were evaluated and compared.

Approach: The three BGO arrays, denoted wo-ILG (without internal light guide), wp-ILG (with partial internal light guide), and wf-ILG (with full internal light guide), share a pitch size of 1.

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner.

Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow.

Purpose: Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB x-ray images.

A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.

Purpose: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the distribution and the Bragg peak (BP) position. In this work, we investigated the feasibility of using a deep learning approach to convert PG images to PD distributions.

Design and evaluation of gapless curved scintillator arrays for simultaneous high-resolution and high-sensitivity brain PET.

Brain PET scanners that simultaneously provide high-resolution across the field-of-view and high-sensitivity can be constructed using detectors based on SiPM arrays coupled to both ends of scintillator arrays with finely segmented and long detector elements. To reduce the dead space between detector modules and hence improve the sensitivity of PET scanners, crystal arrays with curved surfaces are proposed. In this paper, the performance of a proof-of-concept detector module with nine detector submodules based on SiPMs coupled to both ends of a curved LYSO array with a pitch size of 1.

Partial-ring PET image restoration using a deep learning based method.

PET scanners with partial-ring geometry have been proposed for various imaging purposes. The incomplete projection data obtained from this design cause undesirable artifacts in the reconstructed images. In this study, we investigated the performance of a deep learning (DL) based method for the recovery of partial-ring PET images.

Higher SNR PET image prediction using a deep learning model and MRI image.

PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. Our proposed DNN model consists of three modified U-Nets (3U-net).

PET Image Denoising Using a Deep Neural Network Through Fine Tuning.

Positron emission tomography (PET) is a functional imaging modality widely used in clinical diagnosis. In this work, we trained a deep convolutional neural network (CNN) to improve PET image quality. Perceptual loss based on features derived from a pre-trained VGG network, instead of the conventional mean squared error, was employed as the training loss function to preserve image details.

Indirect methods for improving parameter estimation of PET kinetic models.

Purpose: Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images.

Improving Edge Crystal Identification in Flood Histograms Using Triangular Shape Crystals.

This work presents a method to improve the separation of edge crystals in PET block detectors. As an alternative to square-shaped crystal arrays, we used an array of triangular-shaped crystals. This increases the distance between the crystal centres at the detector edges potentially improving the separation of edge crystals.

A general-threshold filtering method for improving intravoxel incoherent motion parameter estimates.

In this study, we present an image denoising method for diffusion-weighted magnetic resonance imaging (DW-MRI) data. Our aim is to improve the estimation of intravoxel incoherent motion (IVIM) parameters using denoised DW-MRI data. A general-threshold filtering (GTF) reconstruction via total variation minimization has been proposed to improve image quality in few-view computed tomography.

A low-count reconstruction algorithm for Compton-based prompt gamma imaging.

The Compton camera is an imaging device which has been proposed to detect prompt gammas (PGs) produced by proton-nuclear interactions within tissue during proton beam irradiation. Compton-based PG imaging has been developed to verify proton ranges because PG rays, particularly characteristic ones, have strong correlations with the distribution of the proton dose. However, accurate image reconstruction from characteristic PGs is challenging because the detector efficiency and resolution are generally low.

Simultaneous PET-MRI reveals brain function in activated and resting state on metabolic, hemodynamic and multiple temporal scales.

Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) is a new tool to study functional processes in the brain. Here we study brain function in response to a barrel-field stimulus simultaneously using PET, which traces changes in glucose metabolism on a slow time scale, and functional MRI (fMRI), which assesses fast vascular and oxygenation changes during activation. We found spatial and quantitative discrepancies between the PET and the fMRI activation data.