The value of convolutional neural networks-based deep learning model in differential diagnosis of space-occupying brain diseases.

Objectives: It is still a challenge to differentiate space-occupying brain lesions such as tumefactive demyelinating lesions (TDLs), tumefactive primary angiitis of the central nervous system (TPACNS), primary central nervous system lymphoma (PCNSL), and brain gliomas. Convolutional neural networks (CNNs) have been used to analyze complex medical data and have proven transformative for image-based applications. It can quickly acquire diseases' radiographic features and correct doctors' diagnostic bias to improve diagnostic efficiency and accuracy.

Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study.

Background: Single-photon emission computed tomography (SPECT) is widely used in the early diagnosis of major diseases such as cardiovascular disease and cancer. High-resolution (HR) imaging requires HR projection data, which typically comes with high costs. This study aimed to obtain HR SPECT images based on a deep learning algorithm using low-resolution (LR) detectors.

Super-resolution acquisition and reconstruction for cone-beam SPECT with low-resolution detector.

Background And Objective: Single-photon emission computed tomography (SPECT) imaging, which provides information that reflects the human body's metabolic processes, has unique application value in disease diagnosis and efficacy evaluation. The imaging resolution of SPECT can be improved by exploiting high-performance detector hardware, but this exploitation generates high research and development costs. In addition, the inherent hardware structure of SPECT requires the use of a collimator, which limits the resolution in SPECT.

A feasibility study for in vivo treatment verification of IMRT using Monte Carlo dose calculation and deep learning-based modelling of EPID detector response.

Background: This paper describes the development of a predicted electronic portal imaging device (EPID) transmission image (TI) using Monte Carlo (MC) and deep learning (DL). The measured and predicted TI were compared for two-dimensional in vivo radiotherapy treatment verification.

Methods: The plan CT was pre-processed and combined with solid water and then imported into PRIMO.

A method for in vivo treatment verification of IMRT and VMAT based on electronic portal imaging device.

Background: Intensity-modulated radiation therapy (IMRT) and volume-modulated arc therapy (VMAT) are rather complex treatment techniques and require patient-specific quality assurance procedures. Electronic portal imaging devices (EPID) are increasingly used in the verification of radiation therapy (RT). This work aims to develop a novel model to predict the EPID transmission image (TI) with fluence maps from the RT plan.

Development of an Electronic Portal Imaging Device Dosimetry Method.

Support arm backscatter and off-axis effects of an electronic portal imaging device (EPID) are challenging for radiotherapy quality assurance. Aiming at the issue, we proposed a simple yet effective method with correction matrices to rectify backscatter and off-axis responses for EPID images. First, we measured the square fields with ionization chamber array (ICA) and EPID simultaneously.

Applications of artificial intelligence in nuclear medicine image generation.

Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation. It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality.

Improved detection of focal cortical dysplasia in normal-appearing FLAIR images using a Bayesian classifier.

Purpose: Focal cortical dysplasia (FCD) is a malformation of cortical development that often causes pharmacologically intractable epilepsy. However, FCD lesions are frequently characterized by minor structural abnormalities that can easily go unrecognized, making diagnosis difficult. Therefore, many epileptic patients have had pathologically confirmed FCD lesions that appeared normal in pre-surgical fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) studies.

Automatic localization and segmentation of focal cortical dysplasia in FLAIR-negative patients using a convolutional neural network.

Purpose: Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult.

Correction to: Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness.

It was highlighted that the original article [1] contained an error in the Quantitative evaluation of Methods. A bracket was misplaced in the formula. This Correction article shows the incorrect and correct formula.

Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness.

Background: Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. However, many focal abnormalities remain undetected during routine visual inspection, and many patients with histologically confirmed FCD have normal fluid-attenuated inversion recovery (FLAIR-negative) images. The aim of this study was to quantitatively evaluate the changes in cortical thickness with magnetic resonance (MR) imaging of patients to identify FCD lesions from FLAIR-negative images.

Analytical cone-beam SPECT reconstruction algorithm with non-uniform attenuation for general non-circular orbit.

In single photon emission computed tomography (SPECT), due to the attenuation of gamma photons, the analytical reconstruction is complicated, where attenuation should be compensated to obtain quantitative results. We know that the resolution of SPECT is low. The cone-beam SPECT reconstruction can improve the photon density and spatial resolution of the reconstructed image.

Super resolution SPECT reconstruction with non-uniform attenuation.

In this paper, based on Novikov's explicit inversion formula for the attenuated Radon transform, we present a super resolution SPECT reconstruction algorithm with compensation for non-uniform attenuation. Unlike the former methods improving the medical image resolution via super resolution (SR) in the reconstructed image, the proposed method apply the SR algorithm in the low resolution (LR) sinogram, which needs only 1-D shift of the detector, and the PSF is easy to obtain. Simulation results show that our reconstruction algorithm is effective.

A wavelet-based SPECT reconstruction algorithm for nonuniformly attenuated Radon transform.

Purpose: Single photon emission computed tomography (SPECT) is a tomography technique that can greatly show information about the metabolic activity in the body and improve the clinical diagnosis. In SPECT, because of photoelectric absorption and Compton scattering, the emitted gamma photons are attenuated inside the body before arriving at the detector. The goal of quantitative SPECT reconstruction is to obtain an accurate image of the radioactivity distribution in the interested area of a human body, so the compensation for nonuniform attenuation and the treatment of Poisson noise are necessary in the quantitative SPECT reconstruction.

A SPECT reconstruction method for extending parallel to non-parallel geometries.

Due to its simplicity, parallel-beam geometry is usually assumed for the development of image reconstruction algorithms. The established reconstruction methodologies are then extended to fan-beam, cone-beam and other non-parallel geometries for practical application. This situation occurs for quantitative SPECT (single photon emission computed tomography) imaging in inverting the attenuated Radon transform.

Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography.

In this paper, we propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multiresolution analysis on the sinogram. Specifically, the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution levels.

An inversion formula for the exponential radon transform in spatial domain with variable focal-length fan-beam collimation geometry.

Inverting the exponential Radon transform has a potential use for SPECT (single photon emission computed tomography) imaging in cases where a uniform attenuation can be approximated, such as in brain and abdominal imaging. Tretiak and Metz derived in the frequency domain an explicit inversion formula for the exponential Radon transform in two dimensions for parallel-beam collimator geometry. Progress has been made to extend the inversion formula for fan-beam and varying focal-length fan-beam (VFF) collimator geometries.

An efficient reconstruction method for nonuniform attenuation compensation in nonparallel beam geometries based on Novikov's explicit inversion formula.

This paper investigates an accurate reconstruction method to invert the attenuated Radon transform in nonparallel beam (NPB) geometries. The reconstruction method contains three major steps: 1) performing one-dimensional phase-shift rebinning; 2) implementing nonuniform Hilbert transform; and 3) applying Novikov's explicit inversion formula. The method seems to be adaptive to different settings of fan-beam geometry from very long to very short focal lengths without sacrificing reconstruction accuracy.

Evaluation of an efficient compensation method for quantitative fan-beam brain SPECT reconstruction.

Fan-beam collimators are designed to improve the system sensitivity and resolution for imaging small objects such as the human brain and breasts in single photon emission computed tomography (SPECT). Many reconstruction algorithms have been studied and applied to this geometry to deal with every kind of degradation factor. This paper presents a new reconstruction approach for SPECT with circular orbit, which demonstrated good performance in terms of both accuracy and efficiency.