Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

PURPOSE: A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7 years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5 min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. RESULTS: 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. CONCLUSION: Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

Subtle Radiographic Progression at Six Months Can Be Detected Using Automated Quantitative Software in Rheumatoid Arthritis While Receiving Tocilizumab.

OBJECTIVES: We investigated whether our in-house software equipped with partial image phase-only correlation (PIPOC) can detect subtle radiographic joint space narrowing (JSN) progression at six months and predict JSN progression in rheumatoid arthritis (RA) patients receiving Tocilizumab. METHODS: The study included 39 RA patients who were treated with Tocilizumab. Radiological progression of the metacarpophalangeal and the proximal interphalangeal joints was evaluated according to the Genant-modified Sharp score (GSS) at 0, 6, and 12 months. Automatic measurements were performed with the software. We validated the software in terms of accuracy in detecting the JSN progression. RESULTS: The success rate of the software for joint space width (JSW) measurement was 96.8% (449/464). The 0-12-month JSW change by the software was significantly greater in joints with the 0-6-month PIPOC (+) group than the 0-6-month PIPOC (-) group (p < 0.001). The 0-12-month JSW change by the software was 0-12-month GSS (+) than with 0-12-month GSS (-) (p = 0.02). Here, "(+)" indicates the JSN progression during the follow-up period. Meanwhile, "(-)" indicates no JSN progression during the follow-up period. Linear regression tests showed significant correlations between the 0-6-month and the 0-12-month PIPOC in the left 2nd and 3rd MCP joints (R2 = 0.554 and 0.420, respectively). CONCLUSIONS: Our in-house software equipped with PIPOC could predict subsequent JSN progression with only short-term observations.

Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients. PURPOSE: To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels. STUDY TYPE: Retrospective. SUBJECTS: Twelve RA patients underwent DCE-MRI with 27 phases for creating the AI model and tested on images with a variable number of phases from 35 RA patients. FIELD STRENGTH/SEQUENCE: 3.0 T/DCE T1-weighted gradient echo sequence (mDixon, water image). ASSESSMENT: The model was trained with various DCE-MRI time-intensity number of phases. Evaluations were conducted for similarity between AI segmentation and manual outlining in 51 ROIs with synovitis. The relationship between synovial volume via AI segmentation with rheumatoid arthritis magnetic resonance imaging scoring (RAMRIS) across whole hands was then evaluated. The reference standard was determined by an experienced musculoskeletal radiologist. STATISTICAL TEST: Area under the curve (AUC) of receiver operating characteristic (ROC), Dice and Spearman's rank correlation coefficients, and interclass correlation coefficient (ICC). A P-value <0.05 was considered statistically significant. RESULTS: A minimum of 15 phases (acquisition time at least 2.5 minutes) was found to be necessary. AUC ranged from 0.941 ± 0.009 to 0.965 ± 0.009. The Dice score was 0.557-0.615. Spearman's correlation coefficients between the AI model and ground truth were 0.884-0.927 and 0.736-0.831, for joint ROIs and whole hands, respectively. The Spearman's correlation coefficient for the additional test set between the model trained with 15 phases and RAMRIS was 0.768. CONCLUSION: The AI-based classification model effectively identified synovitis pixels while excluding arteries. The optimal performance was achieved with at least 15 phases, providing a quantitative assessment of inflammatory activity in RA while minimizing acquisition time. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

A new predictive parameter for dose-volume metrics in intensity-modulated radiation therapy planning for prostate cancer: Initial phantom study.

BACKGROUND: Organ-at-risk (OAR) sparing is often assessed using an overlap volume-based parameter, defined as the ratio of the volume of OAR that overlaps the planning target volume (PTV) to the whole OAR volume. However, this conventional overlap-based predictive parameter (COPP) does not consider the volume relationship between the PTV and OAR. PURPOSE: We propose a new overlap-based predictive parameter that consider the PTV volume. The effectiveness of proposed overlap-based predictive parameter (POPP) is evaluated compared with COPP. METHODS: We defined as POPP = (overlap volume between OAR and PTV/OAR volume) × (PTV volume/OAR volume). We generated intensity modulated radiation therapy (IMRT) based on step and shoot technique, and volumetric modulated arc therapy (VMAT) plans with the Auto-Planning module of Pinnacle3 treatment planning system (v14.0, Philips Medical Systems, Fitchburg, WI) using the American Association of Physicists in Medicine Task Group (TG119) prostate phantom. The relationship between the position and size of the prostate phantom was systematically modified to simulate various geometric arrangements. The correlation between overlap-based predictive parameters (COPP and POPP) and dose-volume metrics (mean dose, V70Gy, V60Gy, and V37.5 Gy for rectum and bladder) was investigated using linear regression analysis. RESULTS: Our results indicated POPP was better than COPP in predicting intermediate-dose metrics. The bladder results showed a trend similar to that of the rectum. The correlation coefficient of POPP was significantly greater than that of COPP in < 62 Gy (82% of the prescribed dose) region for IMRT and in < 55 Gy (73% of the prescribed dose) region for VMAT regarding the rectum (p < 0.05). CONCLUSIONS: POPP is superior to COPP for creating predictive models at an intermediate-dose level. Because rectal bleeding and bladder toxicity can be associated with intermediate-doses as well as high-doses, it is important to predict dose-volume metrics for various dose levels. POPP is a useful parameter for predicting dose-volume metrics and assisting the generation of treatment plans.

A Deep Learning-Based Model for Classifying Osteoporotic Lumbar Vertebral Fractures on Radiographs: A Retrospective Model Development and Validation Study.

Early diagnosis and initiation of treatment for fresh osteoporotic lumbar vertebral fractures (OLVF) are crucial. Magnetic resonance imaging (MRI) is generally performed to differentiate between fresh and old OLVF. However, MRIs can be intolerable for patients with severe back pain. Furthermore, it is difficult to perform in an emergency. MRI should therefore only be performed in appropriately selected patients with a high suspicion of fresh fractures. As radiography is the first-choice imaging examination for the diagnosis of OLVF, improving screening accuracy with radiographs will optimize the decision of whether an MRI is necessary. This study aimed to develop a method to automatically classify lumbar vertebrae (LV) conditions such as normal, old, or fresh OLVF using deep learning methods with radiography. A total of 3481 LV images for training, validation, and testing and 662 LV images for external validation were collected. Visual evaluation by two radiologists determined the ground truth of LV diagnoses. Three convolutional neural networks were ensembled. The accuracy, sensitivity, and specificity were 0.89, 0.83, and 0.92 in the test and 0.84, 0.76, and 0.89 in the external validation, respectively. The results suggest that the proposed method can contribute to the accurate automatic classification of LV conditions on radiography.

A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.

Analysis of the Anticipatory Behavior Formation Mechanism Induced by Methamphetamine Using a Single Hair.

While the suprachiasmatic nucleus (SCN) coordinates many daily rhythms, some circadian patterns of expression are controlled by SCN-independent systems. These include responses to daily methamphetamine (MAP) injections. Scheduled daily injections of MAP resulted in anticipatory activity, with an increase in locomotor activity immediately prior to the time of injection. The MAP-induced anticipatory behavior is associated with the induction and a phase advance in the expression rhythm of the clock gene Period1 (Per1). However, this unique formation mechanism of MAP-induced anticipatory behavior is not well understood. We recently developed a micro-photomultiplier tube (micro-PMT) system to detect a small amount of Per1 expression. In the present study, we used this system to measure the formation kinetics of MAP-induced anticipatory activity in a single whisker hair to reveal the underlying mechanism. Our results suggest that whisker hairs respond to daily MAP administration, and that Per1 expression is affected. We also found that elevated Per1 expression in a single whisker hair is associated with the occurrence of anticipatory behavior rhythm. The present results suggest that elevated Per1 expression in hairs might be a marker of anticipatory behavior formation.

Fully automatic software for detecting radiographic joint space narrowing progression in rheumatoid arthritis: phantom study and comparison with visual assessment.

PURPOSE: We have developed an in-house software equipped with partial image phase-only correlation (PIPOC) which can automatically quantify radiographic joint space narrowing (JSN) progression. The purpose of this study was to evaluate the software in phantom and clinical assessments. MATERIALS AND METHODS: In the phantom assessment, the software's performance on radiographic images was compared to the joint space width (JSW) difference using a micrometer as ground truth. A phantom simulating a finger joint was scanned underwater. In the clinical assessment, 15 RA patients were included. The software measured the radiological progression of the finger joints between baseline and the 52nd week. The cases were also evaluated with the Genant-modified Sharp score (GSS), a conventional visual scoring method. We also quantitatively assessed these joints' synovial vascularity (SV) on power Doppler ultrasonography (0, 8, 20 and 52 weeks). RESULTS: In the phantom assessment, the PIPOC software could detect changes in JSN with a smallest detectable difference of 0.044 mm at 0.1 mm intervals. In the clinical assessment, the JSW change of the joints with GSS progression detected by the software was significantly greater than those without GSS progression (p = 0.004). The JSW change of joints with positive SV at baseline was significantly higher than those with negative SV (p = 0.024). CONCLUSION: Our in-house software equipped with PIPOC can automatically and quantitatively detect slight radiographic changes of JSW in clinically inactive RA patients.

Visceral Obesity and Lipid Profiles in Chinese Adults with Normal and High Body Mass Index.

BACKGROUND: This study examined the prevalence of visceral obesity in Chinese adults across different body mass index (BMI) groups and their associated lipid profiles and demographic risk factors. METHODS: A total of 1653 Chinese adults were recruited for the study. Abdominal quantitative computed tomography (CT) imaging was performed to derive the visceral adipose tissue (VAT) at the lumbar vertebrae (L2-L3) levels. Visceral obesity was defined using established cutoff values. Fasting serum total cholesterol, total glucose, high-density lipoprotein, and low-density lipoprotein were measured. RESULTS: Visceral obesity was prevalent in 35% of men and 22% of women with normal BMI (18.5-24 kg/m2) and 86% of men and 78% of women with high BMI (≥24 kg/m2). In both sexes, participants with normal BMI and visceral obesity had higher levels of TC, TG and LDL and lower HDL compared to those with normal VAT. The risk factors for visceral obesity in women with normal BMI were an age ≥50 years and BMI ≥22.3 kg/m2 and in men included a BMI ≥22.5 kg/m2. CONCLUSION: Visceral obesity was observed in the participants with normal BMI and was associated with an adverse lipid profile. The BMI cutoff points were lower than the normally accepted values.

A real-time measurement system for gene expression rhythms from deep tissues of freely moving mice under light-dark conditions.

Clock gene expression in most organs of the living body exhibits a diurnal rhythm synchronized with the external 24 h light-dark (LD) cycle via circadian pacemaker suprachiasmatic nucleus (SCN). Disturbances in clock gene expression due to desynchronization of clock gene expression of the external LD cycle are risk factors for developing various diseases. Measuring the in vivo clock genes expression rhythm for a long duration under LD conditions can greatly contribute to understand the pathogenic mechanism of the disease caused by the disturbance of the biological rhythm. However, it is presently difficult to continuously measure gene expression for a long duration under LD conditions. In present study, we succeeded in measuring Period1 (Per1) gene expression under LD conditions using ultraviolet (UV) light with filter cut the visible light range. In addition, we succeeded in measuring the kinetic change of liver Per1 gene expression during the process of desynchronization of behavioral rhythm from the LD cycle by chronic administration of methamphetamine (MAP). In the future, by using this system to measure clock gene expression rhythms of brain tissues such as SCN and peripheral tissues under LD conditions, it could contribute to understand the onset mechanism of diseases induced by the desynchronization mechanism of biological rhythm to the LD cycle.

The analysis of Period1 gene expression in vivo and in vitro using a micro PMT system.

To detect a small amount of Period1 (Per1) expression, we developed a micro-photomultiplier tube (µPMT) system which can be used both in vivo and in vitro. Using this system, we succeeded in detecting Per1 gene expression in the skin of freely moving mice over 240 times higher compared with that of the tissue contact optical sensor (TCS) as previously reported. For in vitro studies, we succeeded in detecting elevated Per1 expression by streptozotocin (STZ) treatment in the scalp hairs at an early stage of diabetes, when glucose content in the blood was still normal. In addition, we could detect elevated Per1 expression in a single whisker hair at the time of diabetes onset. These results show that our µPMT system responds to minute changes in gene expression in freely moving mice in vivo and in mice hair follicles in vitro. Furthermore, Per1 in the hair can be used for a marker of diabetic aggravation.

Fundamental study on quality assurance (QA) procedures for a real-time tumor tracking radiotherapy (RTRT) system from the viewpoint of imaging devices.

PURPOSE: The real-time tumor tracking radiotherapy (RTRT) system requires periodic quality assurance (QA) and quality control. The goal of this study is to propose QA procedures from the viewpoint of imaging devices in the RTRT system. METHODS: Tracking by the RTRT system (equips two sets of colored image intensifiers (colored I.I.s) fluoroscopy units) for the moving gold-marker (diameter 2.0 mm) in a rotating phantom were performed under various X-ray conditions. To analyze the relationship between fluoroscopic image quality and precision of gold marker coordinate calculation, the standard deviation of the 3D coordinate (σ3D [mm]) of the gold marker, the mean of the pattern recognition score (PRS) and the standard deviation of the distance between rays (DBR) (σDBR [mm]) were evaluated. RESULTS: When tracking with speed of 10-60 mm/s, σDBR increased, though the mean PRS did not change significantly (p>0.05). On the contrary, the mean PRS increased depending on the integral noise equivalent quanta (∫NEQ) that is an indicator of image quality calculated from the modulation transfer function (MTF) as an indicator of spatial resolution and the noise power spectrum (NPS) as an indicator of noise characteristic. CONCLUSION: The indicators of NEQ, MTF, and NPS were useful for managing the tracking accuracy of the RTRT system. We propose observing the change of these indicators as additional QA procedures for each imaging device from the commissioning baseline.

Period1 gene expression in the olfactory bulb and liver of freely moving streptozotocin-treated diabetic mouse.

Clock genes express circadian rhythms in most organs. These rhythms are organized throughout the whole body, regulated by the suprachiasmatic nucleus (SCN) in the brain. Disturbance of these clock gene expression rhythms is a risk factor for diseases such as obesity. In the present study, to explore the role of clock genes in developing diabetes, we examined the effect of streptozotocin (STZ)-induced high glucose on Period1 (Per1) gene expression rhythm in the liver and the olfactory bub (OB) in the brain. We found a drastic increase of Per1 expression in both tissues after STZ injection while blood glucose content was low. After a rapid expression peak, Per1 expression showed no rhythm. Associated with an increase of glucose content, behavior became arrhythmic. Finally, we succeeded in detecting an increase of Per1 expression in mice hair follicles on day 1 after STZ administration, before the onset of symptoms. These results show that elevated Per1 expression by STZ plays an important role in the aggravation of diabetes.

Stability of d-luciferin for bioluminescence to detect gene expression in freely moving mice for long durations.

Circadian disturbance of clock gene expression is a risk factor for diseases such as obesity, cancer, and sleep disorders. To study these diseases, it is necessary to monitor and analyze the expression rhythm of clock genes in the whole body for a long duration. The bioluminescent reporter enzyme firefly luciferase and its substrate d-luciferin have been used to generate optical signals from tissues in vivo with high sensitivity. However, little information is known about the stability of d-luciferin to detect gene expression in living animals for a long duration. In the present study, we examined the stability of a luciferin solution over 21 days. l-Luciferin, which is synthesized using racemization of d-luciferin, was at high concentrations after 21 days. In addition, we showed that bioluminescence of Period1 (Per1) expression in the liver was significantly decreased compared with the day 1 solution, although locomotor activity rhythm was not affected. These results showed that d-luciferin should be applied to the mouse within, at most, 7 days to detect bioluminescence of Per1 gene expression rhythm in vivo.

Quantification of Joint Space Width Difference on Radiography Via Phase-Only Correlation (POC) Analysis: a Phantom Study Comparing with Various Tomographical Modalities Using Conventional Margin-Contouring.

Several visual scoring methods are currently used to assess progression of rheumatoid arthritis (RA) on radiography. However, they are limited by its subjectivity and insufficient sensitivity. We have developed an original measurement system which uses a technique called phase-only correlation (POC). The purpose of this study is to validate the system by using a phantom simulating the joint of RA patients.A micrometer measurement apparatus that can adjust arbitrary joint space width (JSW) in a phantom joint was developed to define true JSW. The phantom was scanned with radiography, 320 multi detector CT (MDCT), high-resolution peripheral quantitative CT (HR-pQCT), cone beam CT (CBCT), and tomosynthesis. The width was adjusted to the average size of a women's metacarpophalangeal joint, from 1.2 to 2.2 mm with increments of 0.1 mm and 0.01 mm. Radiographical images were analyzed by the POC-based system and manual method, and images from various tomographical modalities were measured via the automatic margin detection method. Correlation coefficients between true JSW difference and measured JSW difference were all strong at 0.1 mm intervals with radiography (POC-based system and manual method), CBCT, 320MDCT, HR-pQCT, and tomosynthesis. At 0.01 mm intervals, radiography (POC-based system), 320MDCT, and HR-pQCT had strong correlations, while radiography (manual method) and CBCT had low correlations, and tomosynthesis had no statistically significant correlation. The smallest detectable changes for radiography (POC-based system), radiography (manual method), 320MDCT, HR-pQCT, CBCT, and tomosynthesis were 0.020 mm, 0.041 mm, 0.076 mm, 0.077 mm, 0.057 mm, and 0.087 mm, respectively. We conclude that radiography analyzed with the POC-based system might sensitively detect minute joint space changes of the finger joint.

Quick and accurate selection of hand images among radiographs from various body parts using deep learning.

BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection of relevant images (e.g. hand images) among radiographic images of various body parts is necessary for serial analysis on a large scale. OBJECTIVE: In this study we examined whether deep learning can select target images from a large number of stored images retrieved from a picture archiving and communication system (PACS) including miscellaneous body parts of patients. METHODS: We selected 1,047 X-ray images including various body parts and divided them into two groups: 841 images for training and 206 images for testing. The training images were augmented and used to train a convolutional neural network (CNN) consisting of 4 convolution layers, 2 pooling layers and 2 fully connected layers. After training, we created software to classify the test images and examined the accuracy. RESULTS: The image extraction accuracy was 0.952 and 0.979 for unilateral hand and both hands, respectively. In addition, all 206 test images were perfectly classified into unilateral hand, both hands, and the others. CONCLUSIONS: Deep learning showed promise to enable efficiently automatic selection of target X-ray images of RA patients.

Validation of Fully Automatic Quantitative Software for Finger Joint Space Narrowing Progression for Rheumatoid Arthritis Patients.

In rheumatoid arthritis (RA), the radiographic progression of joint space narrowing (JSN) is evaluated using visual assessments. However, those methods are complicated and time-consuming. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. The purpose of this study was to establish the validity of the software that automatically evaluates the temporal change of JSN. This study consisted of 39 patients with RA. All patients were treated with tocilizumab and underwent hand radiography (left and right hand separately) at 0, 6, and 12 months. The JSN was evaluated using mTSS (modified Total Sharp Score) by one musculoskeletal radiologist as well as our automatic system. Software measurement showed that JSDI between 0 and 12 months was significantly higher than that between 0 and 6 months (p < 0.01). While, there was no significant difference in mTSS between 0, 6, and 12 months. The group with higher disease activity at 0 months had significantly higher JSDI between 0 and 6 months than that with lower disease activity (p = 0.02). The automatic software can evaluate JSN progression of RA patients in the finger joint on X-ray.

Double recording system of Period1 gene expression rhythm in the olfactory bulb and liver in freely moving mouse.

Clock genes express circadian rhythms in most organs. These rhythms are organized throughout the whole body, regulated by the suprachiasmatic nucleus (SCN) in the brain. Disturbance of these clock gene expression rhythms is a risk factor for diseases such as obesity and cancer. To understand the mechanism of regulating clock gene expression rhythms in vivo, multiple real time recording systems are required. In the present study, we developed a double recording system of Period1 expression rhythm in peripheral tissue (liver) and the brain. In peripheral tissue, quantification of gene expression in a steadily moving target was achieved by using a photomultiplier tube (PMT) attached to a tissue contact optical sensor (TCS). Using this technique, we were able to analyze circadian rhythms of clock gene expression over a prolonged period in the liver and olfactory bub (OB) of the brain. The present double recording system has no effect on behavioral activity or rhythm. Our novel system thus successfully quantifies clock gene expression in deep areas of the body in freely moving mice for a period sufficient to analyze circadian dynamics. In addition, our double recording system can be widely applied to many areas of biomedical research, as well as applications beyond medicine.

Fully automatic quantitative software for assessment of minute finger joint space narrowing progression on radiographs: evaluation in rheumatoid arthritis patients with long-term sustained clinical low disease activity.

PURPOSE: Rheumatoid arthritis (RA) causes joint space narrowing (JSN) as a form of joint destruction. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. This study aims to evaluate the application of "machine vision" for radiographic image of the finger joints. MATERIALS AND METHODS: Fifteen RA patients with long-term sustained clinical low disease activity were recruited. All patients underwent hand radiography and power Doppler ultrasonography (PDUS). The JSN was evaluated using the Genant-modified Sharp scoring (GSS) method and the automatic system. Synovial vascularity (SV) was assessed quantitatively using ultrasonography. RESULTS: There were no significant differences in the JSDI between the joints with JSN and those without JSN on GSS (p = 0.052). The JSDI of the joints with SV was significantly higher than those without SV (p = 0.043). The JSDI of the no therapeutic response group was significantly higher than those of the response group (p < 0.001). CONCLUSION: Our software can automatically evaluate temporal changes of JSN, which might free rheumatologists / radiologists from the burden of scoring hand radiography.

Mouse period1 gene expression recording from olfactory bulb under free moving conditions with a portable optic fibre device.

Because the disruption of circadian clock gene is a risk factor in many diseases such as obesity and cancer, it is important to monitor and analyzed the expression of the rhythm of the clock gene throughout the body over a long period of time. Although we previously reported on a new gene expression analysis system tracking a target position on the body surface of freely moving mice, the experimental apparatus required a large space. We have therefore developed an in vivo recording system using a portable photomultiplier tube (PMT) system attached to an optical fibre. Directly connecting the target area with the device, we could easily measure the photon counts in a very small space. However, little information is known about the characteristics of optical fibres when exposed to twisting/looping in association with a moving mouse and the effect of the surface of optical fibre. In the present study, we report on the characteristics of optical fibres to detect gene expression rhythm in freely moving mice. Using this portable optical device directly connected with a target area, we were able to measure the circadian rhythm of clock gene expression over a prolonged period in freely moving mice in a small space.