Show-through removal with sparsity-based blind deconvolution.

When scanning a document printed on both sides by using an electronic scanner, the printed material on the back (front) side may be transmitted to the front (back) side. This phenomenon is called show-through. The problem to remove the show-through from scanned images is called the show-through removal in the literature. In this paper, we propose a new method of show-through removal based on the following principle. The proposed method uses two scanned images with the front side and with the back side as input images. The proposed method is based on Ahmed's Blind Image Deconvolution method discovered in 2013, which succeeded in formulating Blind Image Deconvolution as a nuclear norm minimization. Since the structure of show-through removal resembles that of Blind Image Deconvolution, we discovered that the show-through removal can be reformulated into a nuclear norm minimization in the space of outer product matrix constructed from an image vector and a point spread function vector of blurring. Using this key idea, we constructed the proposed method as follows. First, our cost function consists of the following three terms. The first term is the data term and the second term is the nuclear norm derived from the above reformulation. The third term is a regularization term to overcome the underdetermined nature of show-through removal problem and the existence of noise in the measured images. The regularization term consists of Total Variation imposed on the images. The resulting nuclear norm minimization problem is solved by using Accelerated Proximal Gradient method and Singular Value Projection with some problem-specific modifications, which converges fast and requires a simple implementation. We show results of simulation studies as well as results of real image experiments to demonstrate the performances of the proposed method.

Fundamental Study of a Wristwatch Sweat Lactic Acid Monitor.

A lactic acid (LA) monitoring system aimed at sweat monitoring was fabricated and tested. The sweat LA monitoring system uses a continuous flow of phosphate buffer saline, instead of chambers or cells, for collecting and storing sweat fluid excreted at the skin surface. To facilitate the use of the sweat LA monitoring system by subjects when exercising, the fluid control system, including the sweat sampling device, was designed to be unaffected by body movements or muscle deformation. An advantage of our system is that the skin surface condition is constantly refreshed by continuous flow. A real sample test was carried out during stationary bike exercise, which showed that LA secretion increased by approximately 10 µg/cm2/min compared to the baseline levels before exercise. The LA levels recovered to baseline levels after exercise due to the effect of continuous flow. This indicates that the wristwatch sweat LA monitor has the potential to enable a detailed understanding of the LA distribution at the skin surface.

Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.

This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.

Simple phase unwrapping method with continuous convex minimization.

Phase unwrapping is a problem to reconstruct true phase values from modulo 2π phase values measured using various phase imaging techniques. This procedure is essentially formulated as a discrete optimization problem. However, most energy minimization methods using continuous optimization techniques have ignored the discrete nature and solved it as a continuous minimization problem directly, leading to losing exactness of the algorithms. We propose a new minimum norm method that can yield the optimal solution of the discrete problem by minimizing a continuous energy function. In contrast to the graph-cuts method, which is state of the art in this field, the proposed method requires much less memory space and a very simple implementation. Therefore, it can be simply extended to 3D or 4D phase unwrapping problems.

Estimation of patient's angle from skull radiographs using deep learning.

BACKGROUND: Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient's rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient's angle for adequate assessment, which requires extensive experience. OBJECTIVE: To develop and test a new deep learning model or method to automatically estimate patient's angle from radiographs. METHODS: The patient's position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed tomography images and used as input data to estimate the patient's angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient's angle is estimated in the lateral and superior-inferior directions. RESULTS: Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. CONCLUSIONS: These findings suggest that a patient's angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography.

Sparsity-based method for ring artifact elimination in computed tomography.

Ring artifact elimination is one of the popular problems in computed tomography (CT). It appears in the reconstructed image in the form of bright or dark patterns of concentric circles. In this paper, based on the compressed sensing theory, we propose a method for eliminating the ring artifact during the image reconstruction. The proposed method is based on representing the projection data by a sum of two components. The first component contains ideal correct values, while the latter contains imperfect error values causing the ring artifact. We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. In particular, we investigate the effect of using different sparse models, i.e. different sparsity-induced norms, on the accuracy of the ring artifact correction. The proposed cost function is optimized using an iterative algorithm derived from the alternative direction method of multipliers. Moreover, we propose improved versions of the proposed algorithms by incorporating a smoothing penalty function into the cost function. We also introduce angular constrained forms of the proposed algorithms by considering a special case as follows. The imperfect error values are constant over all the projection angles, as in the case where the source of ring artifact is the non-uniform sensitivity of the detector. Real data and simulation studies were performed to evaluate the proposed algorithms. Results demonstrate that the proposed algorithms with incorporating smoothing penalty and their angular constrained forms are effective in ring artifact elimination.

[Dr. Eiichi Tanaka's Achievement in the Research of Image Reconstruction].

In memoriam of Dr. Eiichi Tanaka who passed away on August 21, 2021, I review his achievement in the research of tomographic image reconstruction. Tomographic image reconstruction is an important research area which has wide applications including X-ray CT, nuclear medicine imaging such as PET and SPECT, and electron microscopy. Since 1970's, Dr. Tanaka has worked on numerous important topics in tomographic reconstruction fields aiming at using them in image reconstruction for PET and SPECT. Among them, in this paper, I will introduce his research on Filtered BackProjection (FBP) method, analytical attenuation correction in SPECT, image reconstruction in Time-of-Flight PET, image reconstruction for 3-D PET imaging, and iterative image reconstruction method called Dynamic Row-Action Maximum Likelihood Algorithm (DRAMA).

Persistent Water Repellency of Syndiotactic Polymethylene with Perfluoroethyl Hexyloxycarbonyl Side Chains.

Polyacrylates bearing long fluoroalkyl (Rf) side chains are known to have ultralow surface energies that are appropriate for functional coating and fabric finishing. However, these long Rf chains cause health concerns because of the risk of toxic and bioaccumulative perfluoroalcanoic acid emission via oxidative degradation. This work demonstrates that incorporating a short Rf chain of perfluoroethylene at the end of the side chains of syndiotactic poly(substituted methylene) (PM) produces hydrophobicity. A contact angle of 105° of PM remains constant for more than 50 s, whereas that of the polyacrylate (PA) with the same side chain rapidly decreases from 85° to 44° over the same period. Such persistent water repellency of the PM is ascribed to a liquid crystal structure comprised the main chains arranged in a 2D hexagonal lattice and side chains that extend perpendicularly from these main chains.

Factors that affect the changes in the satisfaction of Japanese physical therapists with their profession over the years.

[Purpose] To analyze changes in the level of satisfaction of new physical therapists with their profession over time and in the factors that affect their satisfaction, focusing on the impact of previous experiences and working conditions. [Participants and Methods] This longitudinal survey study administered a questionnaire to 585 physical therapists who participated in the newcomer orientation at Hiroshima Prefectural Physical Therapy Association from 2017 to 2019 as follows: 210 in 2017, 188 in 2018, and 187 in 2019. The responses to the questions regarding professional satisfaction and the factors that affect it were investigated. The factors identified consisted of the motivation to become a physical therapist, learning in school, clinical internship, working environment, and workplace comfort. A 5-point Likert scale (strongly agree, agree, undecided, disagree, and strongly disagree) was used. [Results] A significant difference was observed in the satisfaction with profession over the 3 year period, and the results of the multiple comparison tests showed a difference between 2018 and 2019. Among the factors identified, positive professional perceptions in clinical internship strongly influenced professional satisfaction over time. [Conclusion] The 1 year longitudinal study revealed a decline in professional satisfaction among the new physical therapists. The characteristic changes in the factors of professional satisfaction were determined from the motivation to become a physical therapist to the clinical internship experience.

Localization of the Lumbar Plexus in the Psoas Muscle: Considerations for Avoiding Lumbar Plexus Injury during the Transpsoas Approach.

INTRODUCTION: Transpsoas lumbar spine surgery is minimally invasive and has very good corrective effects. However, approach-side nerve complications delay post-operative rehabilitation. We anatomically investigated the localization of the lumbar plexus running in the psoas muscle. METHODS: We examined 27 formalin-fixed cadavers. The left-sided psoas muscle was extracted and cut parallel to the intervertebral disc at the L2/3, L3/4, and L4/5 disc levels. Using digitized photographs, we calculated the ratio of the distance from the front edge of the psoas muscle to the center of the lumbar plexus in the anteroposterior diameter of the psoas muscle (%). Then, we calculated the ratio of the distance from the lateral edge of the psoas muscle to the center of the lumbar plexus in the lateral diameter of the psoas muscle (%). RESULTS: The anterior-posterior lumbar plexus localization was 74.5 at L2/3, 74.7 at L3/4, and 81.2 at L4/5. There was a significant difference between L2/3 and L4/5 and between L3/4 and L4/5, but not between L2/3 and L3/4 (P=0.02, 0.01, and 0.94, respectively). The lateral and medial lumbar plexus localization was 85.4 at L2/3, 83.9 at L3/4, and 77.7 at L4/5. There was a significant difference between L2/3 and L4/5 and between L3/4 and L4/5, but not between L2/3 and L3/4 (P=0.01, 0.04, and 0.41, respectively). CONCLUSIONS: The lumbar plexus was localized in the posterior one-third and medial one-third of the psoas muscle and moved to a posterolateral location at L4/5. To avoid neuropathy, consider the psoas muscle's position relative to that of the intervertebral disc. It is essential to understand lumbar plexus localization in the psoas muscle when looking directly at this muscle to enter the pricking point or route with a lower risk of nerve damage.

Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT Image Reconstruction.

We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity changes for textures and low-contrast objects, which is a major limitation in improving image quality. The proposed nonlocal TV combines the first and second order derivatives to preserve smooth intensity changes well. Furthermore, to accelerate the iterative algorithm to minimize the cost function using the proposed nonlocal TV, we propose a proximal splitting based on Passty's framework. We demonstrate that the proposed nonlocal TV method achieves adequate image quality both in sparse-view CT and low-dose CT, through simulation studies using a brain CT image with a very narrow contrast range for which it is rather difficult to preserve smooth intensity changes.

Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection.

PURPOSE: Dysphagia has a large impact on the society because it is a risk factor of malnutrition and aspiration pneumonia, and therefore, it is necessary to elucidate the entire mechanism of dysphagia. In this study, we propose a segmentation method of cervical intervertebral disks (CIDs) in videofluorography (VF) by use of patch-based convolutional neural network (CNN), our multi-channelization (MC) method and image feature selection. METHODS: Twenty image filters are individually applied to a VF frame image to generate feature images. One color image, called a multi-channelized image, is generated by setting three selected feature images to its red, green and blue channels. Patch-based CNN is applied to the MC image, and the segmentation accuracy of CIDs is evaluated by the pixel-based F-measure. The combination of the three feature images is optimized by the simulated annealing method. RESULTS: The proposed method was applied to actual VF dataset consisting of 19 patients and 39 healthy participants. The segmentation accuracy was 59.3% in the F-measure when Sobel and morphological top-hat filters were selected in MC, whereas it was 56.2% when original frame images were used. CONCLUSION: The experimental results demonstrated that the proposed method was able to segment CIDs from actual VF and also that the MC method was able to increase the segmentation accuracy by approximately 3%. In this study, LeNet was used as CNN. One of our future tasks is to use other CNNs.

A fast regularized iterative algorithm for fan-beam CT reconstruction.

We propose a fast iterative image reconstruction algorithm for normal, short-scan, and super-short-scan fan-beam computed tomography (CT), which aims at iterative reconstruction for low-dose and few-view CT by minimizing a data-fidelity term regularized with a total variation (TV) penalty. The derivation of the algorithm can be outlined as follows. First, the original minimization problem is formulated into a saddle-point (primal-dual) problem by using the Lagrangian duality, to which we apply the alternating projection proximal (APP) algorithm, which belongs to a class of first-order primal-dual methods. Second, we precondition the iterative formula using the modified ramp filter of the filtered back-projection (FBP) reconstruction algorithm in such a way that the solution to this preconditioned iteration perfectly coincides with the solution to the original problem. The resulting algorithm converges quickly to the minimizer of the cost function. To demonstrate the advantages of our method, we perform reconstruction experiments using projection data from both numerical phantoms and real CT data. Both qualitative and quantitative results are presented.

Factors affecting physical therapists' job satisfaction: questionnaire survey targeting first-year physical therapists.

[Purpose] The survey aimed to clarify the factors that affect physiotherapists' job satisfaction. [Subjects and Methods] To examine factors affecting physical therapists' job satisfaction using a cross-sectional study with a questionnaire survey. Subjects were 193 first-year physical therapists who participated in a newcomer orientation at Hiroshima Prefectural Physical Therapy Association. The questionnaire comprised items concerning physical therapists' satisfaction with their work, motives for becoming physical therapists, education in school, internships, the workplace, and comfort in the workplace. [Results] Subjects were divided into two groups according to their satisfaction with their occupation. The "high satisfaction" group included 157 subjects, and the group "low satisfaction" group included 36 subjects. Using logistic regression analysis, items concerning comfort in the workplace, motives for becoming physical therapists, and learning in school were analysed. [Conclusion] Factors affecting physical therapists' job satisfaction were primarily influenced by previous experience and working conditions.

Relationships between motives to become a physical therapist, delayed graduation, and perceptions of school and internship learning among physical therapy students.

[Purpose] The purpose of this study is to reveal the relationships between physical therapy students' motives to become physical therapists and their academic performance. This was investigated by their experience of delayed or non-delayed graduation, and their perceptions of learning in school and internship. [Subjects and Methods] Subjects were 245 physical therapists participating in a newcomer orientation by the Hiroshima Prefectural Physical Therapy Association in May, 2017. Subjects' basic attributes and their responses to specially created questionnaires were investigated. [Results] Seventeen of 193 physical therapists in their first year experienced delayed graduation. There were differences between the delayed graduation group and the non-delayed graduation group about motives for becoming a physical therapist. Moreover, there were significant relationships between motives for becoming a physical therapist and perceptions of learning in school as opposed to internship. [Conclusion] We conclude that motives for becoming a physical therapist are related to academic performance.

Interactive Segmentation of Pancreases in Abdominal Computed Tomography Images and Its Evaluation Based on Segmentation Accuracy and Interaction Costs.

The present paper proposed an interactive segmentation method of pancreases in abdominal computed tomography (CT) images based on the anatomical knowledge of medical doctors and the statistical information of pancreas shapes. This segmentation method consisted of two phases: training and testing. In the training phase, pancreas regions were manually extracted from sample CT images for training, and then a probabilistic atlas (PA) was constructed from the extracted regions. In the testing phase, a medical doctor selected seed voxels for a pancreas and background in a CT image for testing by use of our graphical user interface system. The homography transformation was used to fit the PA to the seeds. The graph cut technique whose data term was weighted by the transformed PA was applied to the test image. The seed selection, the atlas transformation, and the graph cut were executed iteratively. This doctor-in-the-loop segmentation method was applied to actual abdominal CT images of fifteen cases. The experimental results demonstrated that the proposed method was more accurate and effective than the conventional graph cut.

Lumbar artery branches coursing vertically over the intervertebral discs of the lower lumbar spine: an anatomic study.

PURPOSE: Bleeding from the lumbar artery is a potential complication during the transpsoas approach to the lower lumbar intervertebral discs. In this anatomic study, the morphological relationships between the branches of the lumbar artery and the lower intervertebral disc were investigated to assess the risk of injury to the branches of the lumbar segmental arteries. METHODS: We studied 88 sites (86 lumbar arteries) at the third and fourth lumbar vertebrae bilaterally in 22 formalin-fixed cadavers. The branches of the lumbar artery coursing along the lateral sides of the lower intervertebral disc [muscular branch, anastomotic branch, and branch supplying the spinal nerve and plexus (BSNP)] and the iliolumbar artery running upward over the L4-5 disc were identified. Branches crossing the intervertebral discs vertically were evaluated. RESULTS: Muscular branches with a lumen structure longer than 2 cm coursed vertically over the middle third of the intervertebral disc in 3 of 88 sites (3.4 %). Anastomotic branches ran downward in 13 of 88 (14.8 %), and iliolumbar arteries ran upward on the posterior third of the lateral sides of the disc in 2 of 88 (2.3 %). BSNPs ran downward through the posterior third of the disc at 18 of 88 sites (20.5 %). Overall, the arterial branches coursed vertically over the posterior third of the lateral sides of the intervertebral discs in approximately 30 % of subjects. CONCLUSIONS: Lumbar artery branches coursed vertically over the middle third and the posterior third of the lateral sides of the intervertebral discs in approximately 3 and 30 % of subjects, respectively.

Probabilistic atlas prior for CT image reconstruction.

BACKGROUND AND OBJECTIVES: In computed tomography (CT), statistical iterative reconstruction (SIR) approaches can produce images of higher quality compared to the conventional analytical methods such as filtered backprojection (FBP) algorithm. Effective noise modeling and possibilities to incorporate priors in the image reconstruction problem are the main advantages that lead to continuous development of SIR methods. Oriented by low-dose CT requirements, several methods are recently developed to obtain a high-quality image reconstruction from down-sampled or noisy projection data. In this paper, a new prior information obtained from probabilistic atlas is proposed for low-dose CT image reconstruction. METHODS: The proposed approach consists of two main phases. In learning phase, a dataset of images obtained from different patients is used to construct a 3D atlas with Laplacian mixture model. The expectation maximization (EM) algorithm is used to estimate the mixture parameters. In reconstruction phase, prior information obtained from the probabilistic atlas is used to construct the cost function for image reconstruction. RESULTS: We investigate the low-dose imaging by considering the reduction of X-ray beam intensity and by acquiring the projection data through a small number of views or limited view angles. Experimental studies using simulated data and chest screening CT data demonstrate that the probabilistic atlas prior is a practically promising approach for the low-dose CT imaging. CONCLUSIONS: The prior information obtained from probabilistic atlas constructed from earlier scans of different patients is useful in low-dose CT imaging.

2D Non-Separable Block-Lifting Structure and Its Application to M-Channel Perfect Reconstruction Filter Banks for Lossy-to-Lossless Image Coding.

We propose a 2D non-separable block-lifting structure (2D-NSBL) that is easily formulated from the 1D separable block-lifting structure (1D-SBL) and 2D non-separable lifting structure (2D-NSL). The 2D-NSBL can be regarded as an extension of the 2D-NSL, because a two-channel 2D-NSBL is completely equivalent to a 2D-NSL. We apply the 2D-NSBL to M-channel ( M=2(n), n ∈ N) perfect reconstruction filter banks (PRFBs). The 2D-NSBL-based PRFBs outperform 1D-SBL-based PRFBs at lossy-to-lossless coding, whose image quality is scalable from lossless data to high compressed lossy data, because their rounding errors are reduced by merging many rounding operations.