Deep learning study on the mechanism of edge artifacts in point spread function reconstruction for numerical brain images.

Objective: Non-blinded image deblurring with deep learning was performed on blurred numerical brain images without point spread function (PSF) reconstruction to obtain edge artifacts (EA)-free images. This study uses numerical simulation to investigate the mechanism of EA in PSF reconstruction based on the spatial frequency characteristics of EA-free images.

Methods: In 256 × 256 matrix brain images, the signal values of gray matter (GM), white matter, and cerebrospinal fluid were set to 1, 0.

Compressed sensing reconstruction shortens the acquisition time for myocardial perfusion imaging: a simulation study.

Compressed sensing (CS) has been used to improve image quality in single-photon emission tomography (SPECT) imaging. However, the effects of CS on image quality parameters in myocardial perfusion imaging (MPI) have not been investigated in detail. This preliminary study aimed to compare the performance of CS-iterative reconstruction (CS-IR) with filtered back-projection (FBP) and maximum likelihood expectation maximization (ML-EM) on their ability to reduce the acquisition time of MPI.

Combination of compressed sensing-based iterative reconstruction and offset acquisition for I-123 FP-CIT SPECT: a simulation study.

Objectives: The purpose of this study was to validate undersampled single-photon emission computed tomography (SPECT) imaging using a combination of compressed sensing (CS) iterative reconstruction (CS-IR) and offset acquisition.

Methods: Three types of numerical phantoms were used to evaluate image quality and quantification derived from CS with offset acquisition. SPECT images were reconstructed using filtered back-projection (FBP), maximum likelihood-expectation maximization (ML-EM), CS-IR, and CS-IR with offset acquisition.

A novel few-views arrangement of the fixed X-ray tubes for tomosynthesis.

Purpose: Tomosynthesis is a technique that reconstructs a volume image from limited-angle projection data. In conventional tomosynthesis, the examination time is long, so it can be difficult for patients to hold their breath during certain examinations, such as chest imaging. Few-views tomosynthesis, which uses a linear arrangement of fixed X-ray tubes and enables an image to be obtained within 1 s, was found to be useful in the clinical setting in our previous study.

[Incident Photon Number and Reconstructed Linear Attenuation Coefficients in Iterative CT Image Reconstruction].

[Purpose] The iterative CT image reconstruction (IR) method has been successfully incorporated into commercial CT scanners as a means to promote low-dose CT with high image quality. However, the algorithm of the IR method has not been made publicly available by scanner manufacturers. Kudo reviewed the fundamentals of IR methods on the basis of the articles published by the joint research group of each manufacture that were released before and during product development (Med Imag Tech 32: 239-248, 2014).

[An Error Evaluation of Iterative Image Reconstruction Methods Using Chi-Square (χ) Statistic Minimization for Poisson-Distributed Projection Data].

[Purpose] Iterative image reconstruction (IR) methods using Neyman's chi-square statistic (χ) or Pearson's chi-square statistic (χ) have been investigated in nuclear medicine. However, these chi-square statistic-based image reconstructions have never been installed on clinical nuclear medicine instruments. Mighell developed another chi-square statistic (χ).

[Implementation of Statistically-Based Image Reconstruction Algorithms for CT and Numerical Evaluation of Image Quality].

[Purpose] Statistically-based image reconstruction (SIR) methods that have been incorporated into commercial CT scanners have succeeded in promoting low-dose CT with high image quality in comparison with scanners using the filtered back-projection (FBP) method. Not only researchers but also medical doctors and technologists engaged in CT studies have an interest in the algorithms of the SIR methods, however, the algorithms have not been made available to users by the CT manufacturers. Kudo reviewed the fundamentals of SIR methods on the basis of the articles published by the joint research group of each manufacturer released before product development (Med Imag Tech 32: 239-248, 2014).

Performance of compressed sensing-based iterative reconstruction for single-photon emission computed tomography from undersampled projection data: a simulation study in 123I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane imaging.

Objective: The aim of this study was to investigate the efficacy of compressed sensing (CS)-based iterative reconstruction (CS-IR) from undersampled projection data in I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography (SPECT).

Materials And Methods: We used the cylinder/sphere and the striatal digital phantom models. The number of projections was set at 120, 90, 60, 40, and 30 projections.

[Evaluation of the Image Registration Program for Portal Images Using CR and DRR Images in Radiation Therapy].

In this study, computer simulations and experiments were used to verify the accuracy of a two-dimensional image registration program (program) for portal images that we previously developed. The program used a computed radiography cassette system and digitally reconstructed radiography images as planning images for external beam radiation therapy. Using this program, we also investigated the reason two-dimensional automatic image registration images experienced large misregistration in clinical practice using commercial image registration systems.

Undersampling patterns in k-space for compressed sensing MRI using two-dimensional Cartesian sampling.

In compressed sensing magnetic resonance imaging (CS-MRI), undersampling of k-space is performed to achieve faster imaging. For this process, it is important to acquire data randomly, and an optimal random undersampling pattern is required. However, random undersampling is difficult in two-dimensional (2D) Cartesian sampling.

[Numerical and Visual Evaluations of Compressed Sensing MRI Using 2D Radial Sampling].

Two-dimensional radial MRI using compressed sensing (2D radial CS) enables incoherence sampling in k space unlike conventional Cartesian MRI, however 2D radial CS has not been sufficiently investigated. Numerical and visual evaluations of 2D radial CS were performed in this paper. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weigthted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for the numerical evaluation.

[Numerical and Visual Evaluations of Compressed Sensing MRI Using 2D Cartesian Sampling].

This paper describes numerical and visual evaluations of compressed sensing MRI (CS-MRI) using 2D Cartesian sampling by numerical simulation. The BrainWeb MRI Data Base was used for test images. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weighted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for the numerical evaluation.

[Numerical and Visual Evaluation of Compressed Sensing MRI Using 3D Cartesian Sampling].

We performed numerical and visual evaluation of compressed sensing MRI (CS-MRI) using 3D Cartesian sampling by numerical simulation. Three brain anatomical ROIs (white matter, gray matter, cerebrospinal fluid) of a T1-weighted image (T1WI), a T2-weighted image (T2WI) and a proton density-weighted image (PDWI) were used for numerical evaluation. Sampling ratio of the Cartesian grid was 30%.

Iterative image reconstruction that includes a total variation regularization for radial MRI.

This paper presents an iterative image reconstruction method for radial encodings in MRI based on a total variation (TV) regularization. The algebraic reconstruction method combined with total variation regularization (ART_TV) is implemented with a regularization parameter specifying the weight of the TV term in the optimization process. We used numerical simulations of a Shepp-Logan phantom, as well as experimental imaging of a phantom that included a rectangular-wave chart, to evaluate the performance of ART_TV, and to compare it with that of the Fourier transform (FT) method.

[Development of a Text-Data Based Learning Tool That Integrates Image Processing and Displaying].

We developed a text-data based learning tool that integrates image processing and displaying by Excel. Knowledge required for programing this tool is limited to using absolute, relative, and composite cell references and learning approximately 20 mathematical functions available in Excel. The new tool is capable of resolution translation, geometric transformation, spatial-filter processing, Radon transform, Fourier transform, convolutions, correlations, deconvolutions, wavelet transform, mutual information, and simulation of proton density-, T1-, and T2-weighted MR images.

[Development of the image registration program for portal and DRR images in radiation therapy].

In this article, the authors propose an image registration program of portal images and digitally reconstructed radiography (DRR) images used as simulation images for external beam radiation therapy planning. First, the center of the radiation field in a portal image taken using a computed radiograhy cassette is matched to the center of the portal image. Then scale points projected on a DRR image and the portal image are deleted, and the portal image with the radiation field is extracted.

[Development of computer assisted learning program using cone beam projection for head radiography].

We present a computer assisted learning (CAL) program to simulate head radiography. The program provides cone beam projections of a target volume, simulating three-dimensional computed tomography (CT) of a head phantom. The generated image is 512 x 512 x 512 pixels with each pixel 0.

Multimodal image registration using IECC as the similarity measure.

Purpose: The registration of images from positron emission tomography (PET) to those from magnetic resonance imaging (MRI) using mutual information is usually effective, but fails occasionally because of small region of overlap, low-activity defects in the PET image, difference in spatial resolution, etc. In this article, the authors propose the pixel-based individual entropy correlation coefficient (IECC) as a new, more accurate and more robust registration criterion.

Methods: The authors compare it to the current criteria: Mutual information (MI), normalized mutual information (NMI), and the entropy correlation coefficient (ECC).

Self-refocused slice selection by magic echo DANTE with 270 degrees flipping Gaussian RF modulation.

The method of slice selection proposed for solid-state MRI by combining DANTE selective excitation with magic echo (ME) line narrowing requires a rephasing period ca. 0.6 times the DANTE excitation period.

An iterative reconstruction using median root prior and anatomical prior from the segmented mu-map for count-limited transmission data in PET imaging.

Objective: Recently, whole-body positron emission tomography (PET) examination has greatly developed. To reduce the overall examination time, the transmission scan has been increasingly shortened. Many noise-reduction processes have been developed for count-limited transmission data.