Two-step optimization for accelerating deep image prior-based PET image reconstruction.

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction.

[Direct Positron Emission Imaging Using Ultrafast Timing Performance Detectors].

This is an explanatory paper on Sun Il Kwon et al., Nat. Photon.

ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection.

This study aims to introduce a novel back projection-induced U-Net-shaped architecture, called ReconU-Net, based on the original U-Net architecture for deep learning-based direct positron emission tomography (PET) image reconstruction. Additionally, our objective is to visualize the behavior of direct PET image reconstruction by comparing the proposed ReconU-Net architecture with the original U-Net architecture and existing DeepPET encoder-decoder architecture without skip connections..

Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.

Purpose: Measurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL); however, the correction performance was validated through internal cross-validation. This cross-sectional study aimed to validate this approach using an external validation dataset.

Whole Reconstruction-Free System Design for Direct Positron Emission Imaging From Image Generation to Attenuation Correction.

Direct positron emission imaging (dPEI), which does not require a mathematical reconstruction step, is a next-generation molecular imaging modality. To maximize the practical applicability of the dPEI system to clinical practice, we introduce a novel reconstruction-free image-formation method called direct μ imaging, which directly localizes the interaction position of Compton scattering from the annihilation photons in a three-dimensional space by utilizing the same compact geometry as that for dPEI, involving ultrafast time-of-flight radiation detectors. This unique imaging method not only provides the anatomical information about an object but can also be applied to attenuation correction of dPEI images.

Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm.

. Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction method, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function.

List-Mode PET Image Reconstruction Using Deep Image Prior.

List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible solution to enhance the quality of PET image reconstruction. However, the application of deep learning techniques to list-mode PET image reconstruction has not been progressed because list data is a sequence of bit codes and unsuitable for processing by convolutional neural networks (CNN).

Performance evaluation of dedicated brain PET scanner with motion correction system.

Objective: Various motion correction (MC) algorithms for positron emission tomography (PET) have been proposed to accelerate the diagnostic performance and research in brain activity and neurology. We have incorporated MC system-based optical motion tracking into the brain-dedicated time-of-flight PET scanner. In this study, we evaluate the performance characteristics of the developed PET scanner when performing MC in accordance with the standards and guidelines for the brain PET scanner.

Ultrafast timing enables reconstruction-free positron emission imaging.

X-ray and gamma-ray photons are widely used for imaging but require a mathematical reconstruction step, known as tomography, to produce cross-sectional images from the measured data. Theoretically, the back-to-back annihilation photons produced by positron-electron annihilation can be directly localized in three-dimensional space using time-of-flight information without tomographic reconstruction. However, this has not yet been demonstrated due to the insufficient timing performance of available radiation detectors.

Deep-learning-based fast TOF-PET image reconstruction using direction information.

Although deep learning for application in positron emission tomography (PET) image reconstruction has attracted the attention of researchers, the image quality must be further improved. In this study, we propose a novel convolutional neural network (CNN)-based fast time-of-flight PET (TOF-PET) image reconstruction method to fully utilize the direction information of coincidence events. The proposed method inputs view-grouped histo-images into a 3D CNN as a multi-channel image to use the direction information of such events.

Unbiased TOF estimation using leading-edge discriminator and convolutional neural network trained by single-source-position waveforms.

Convolutional neural networks (CNNs) are a strong tool for improving the coincidence time resolution (CTR) of time-of-flight (TOF) positron emission tomography detectors. However, several signal waveforms from multiple source positions are required for CNN training. Furthermore, there is concern that TOF estimation is biased near the edge of the training space, despite the reduced estimation variance (i.

Anatomical-guided attention enhances unsupervised PET image denoising performance.

Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs. Herein, we propose an unsupervised 3D PET image denoising method based on an anatomical information-guided attention mechanism. The proposed magnetic resonance-guided deep decoder (MR-GDD) utilizes the spatial details and semantic features of MR-guidance image more effectively by introducing encoder-decoder and deep decoder subnetworks.

Correction for the Influence of Cataract on Macular Pigment Measurement by Autofluorescence Technique Using Deep Learning.

Purpose: Measurements of macular pigment optical density (MPOD) by the autofluorescence technique yield underestimations of actual values in eyes with cataract. We applied deep learning (DL) to correct this error.

Subjects And Methods: MPOD was measured by SPECTRALIS (Heidelberg Engineering, Heidelberg, Germany) in 197 eyes before and after cataract surgery.

Deep learning-based attenuation correction for brain PET with various radiotracers.

Objectives: Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate μ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challenging. Therefore, to overcome these problems, we developed a deep learning-based PET AC (deep AC) framework to synthesize transmission computed tomography (TCT) images from non-AC (NAC) PET images using a convolutional neural network (CNN) with a huge dataset of various radiotracers for brain PET imaging.

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.

Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervised training of the CNN requires a pair of large, high-quality PET image datasets. As an unsupervised learning method, a deep image prior (DIP) has recently been proposed; it can perform denoising with only the target image. In this study, we propose an innovative procedure for the DIP approach with a four-dimensional (4D) branch CNN architecture in end-to-end training to denoise dynamic PET images.

Chemical and molecular bases of dome formation in human colorectal cancer cells mediated by sulphur compounds from Cucumis melo var. conomon.

Colorectal cancer was the third most commonly diagnosed malignant tumor and the fourth leading cause of cancer deaths worldwide in 2012. A human colorectal cancer cell line, RCM-1, was established from a colon cancer tissue diagnosed as a well-differentiated rectum adenocarcinoma. RCM-1 cells spontaneously form 'domes' (formerly designated 'ducts') resembling villiform structures.

Single-base deletion in increases the genistein-to-daidzein ratio in soybean seed.

Novel mutant alleles related to isoflavone content are useful for breeding programs to improve the disease resistance and nutritional content of soybean. However, identification of mutant alleles from high-density mutant libraries is expensive and time-consuming because soybean has a large, complicated genome. Here, we identified the gene responsible for increased genistein-to-daidzein ratio in seed of the mutant line F333ES017D9.

Identification of novel MYB transcription factors involved in the isoflavone biosynthetic pathway by using the combination screening system with agroinfiltration and hairy root transformation.

Soybean isoflavones are functionally important secondary metabolites that are mainly accumulated in seeds. Their biosynthetic processes are regulated coordinately at the transcriptional level; however, screening systems for key transcription factors (TFs) are limited. Here we developed a combination screening system comprising a simple agroinfiltration assay and a robust hairy root transformation assay.

Imaging mitochondrial complex I activation during a vibrotactile stimulation: A PET study using [F]BCPP-EF in the conscious monkey brain.

In order to evaluate the capability of 2--butyl-4-chloro-5-{6-[2-(2-[F]fluoroethoxy)-ethoxy]-pyridin-3-ylmethoxy}-2H-pyridazin-3-one ([F]BCPP-EF), a novel positron emission tomography (PET) probe for mitochondrial complex I (MC-I) activity, to assess neuronal activation, an activation PET study was conducted in the conscious monkey brain with a continuous unilateral vibrotactile stimulation. PET scans with [O]HO, [F]BCPP-EF, or 2-deoxy-2-[F]fluoroglucose ([F]FDG) were conducted under: (1) resting conditions; (2) a continuous vibration stimulation; (3) a continuous vibration stimulation after 15-min pre-vibration; and (4) a continuous vibration stimulation after 30-min pre-vibration. The contralateral/ipsilateral ratio (CIR) in the somatosensory cortex showed significant increases in the uptake of [O]HO, [F]BCPP-EF, and [F]FDG with the vibration stimulation.