Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs.

Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system's adaptability shows promise for applications in medical rehabilitation and sports.

An observation of short-wave near-infrared hyperspectral imaging in tracking of invisible post-traumatic subcutaneous lesions.

Post-traumatic soft tissue damage could persist for an extended period, and the non-traumatic side could be affected by indirect consequences. Hyperspectral imaging soft abundance scorer can identify these concealed and asymptomatic lesions.

Cortical thickness and diffusion tensor imaging in patients with neuropsychiatric systemic lupus erythematosus, with normal structure on brain MRI.

OBJECTIVES: Neuropsychiatric systemic lupus erythematosus (NPSLE) is associated with adverse outcomes; however, imaging abnormalities are only detectable by conventional brain magnetic resonance imaging (MRI) in up to 50% of patients. This study investigated the variability in cortical thickness and diffusion tensor imaging (DTI) parameters among patients with NPSLE whose brain morphology appeared normal on conventional MRI. METHODS: This retrospective study enrolled 27 female patients with NPSLE (median age: 41.0 years, range: 22-63 years) and 34 female healthy controls (median age: 37.0 years, range: 24-55 years). None exhibited evident abnormalities on conventional MRI. Regional volumes, cortical thickness, and DTI parameters, including fractional anisotropy (FA) and mean diffusivity (MD), were compared. Age-adjusted multivariable logistic regression analysis was conducted to detect significant NPSLE-associated differences. RESULTS: No significant differences in grey or white matter volume fractions were observed between the groups. However, the NPSLE group demonstrated significant cortical thinning in the right pars opercularis (2.45 vs 2.52 mm, p = 0.007), reduced FA values in the fornix (0.35 vs 0.40, p = 0.001) and left anterior limb of internal capsule (0.50 vs 0.52, p = 0.012), and increased MD in the fornix (1.71 vs 1.48, p = 0.009) and left posterior corona radiata (0.80 vs 0.76, p = 0.005) compared with those of healthy controls. CONCLUSIONS: Cortical thickness measurements and DTI analyses can be used to detect differential variations in patients with NPSLE who exhibit an otherwise normal brain structure on conventional MRI, indicating the existence of subtle changes despite the absence of obvious macrostructural central nervous system involvement of lupus.

Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments.

Diabetic foot ulcers (DFUs) are considered the most challenging forms of chronic ulcerations to handle their multifactorial nature. It is necessary to establish a comprehensive treatment plan, accurate, and systematic evaluation of a patient with a DFU. This paper proposed an image recognition of diabetic foot wounds to support the effective execution of the treatment plan. In the severity of a diabetic foot ulcer, we refer to the current qualitative evaluation method commonly used in clinical practice, developed by the International Working Group on the Diabetic Foot: PEDIS index, and the evaluation made by physicians. The deep neural network, convolutional neural network, object recognition, and other technologies are applied to analyze the classification, location, and size of wounds by image analysis technology. The image features are labeled with the help of the physician. The Object Detection Fast R-CNN method is applied to these wound images to build and train machine learning modules and evaluate their effectiveness. In the assessment accuracy, it can be indicated that the wound image detection data can be as high as 90%.

Intravoxel Incoherent Motion-Diffusion-Weighted MRI for Investigation of Delayed Graft Function Immediately after Kidney Transplantation.

Purpose: A non-invasive way of assessing post-transplant renal graft function has been needed. This study aimed to assess the micro-structural and micro-functional status of graft kidneys by using intravoxel incoherent motion- (IVIM-) diffusion-weighted imaging (DWI) to investigate delayed graft function (DGF) immediately after transplantation. Method: A prospective study was conducted on 37 patients, 14 with early graft function (EGF) and 23 with DGF (9 with complication, 14 without) who underwent IVIM-DWI, most often within 1-7 days after kidney transplantation. A total of 37 cases were collected and all the participants have been well-informed and signed their consents. In addition, the study conducted in this paper was approved by the Ethics Committee of Clinical Research, Taichung Veterans General Hospital (IRB number: CE14065). Using biexponential analysis of slow diffusion coefficient (D slow), fast diffusion coefficient (D fast), and perfusion fraction was performed. The apparent diffusion coefficient (ADC) was calculated by use of a monoexponential model. All parameters were measured from three different regions-of-interest (ROI), covering the entire renal parenchyma, cortex, and medulla. Results: D slow, perfusion fraction, and ADC were significantly higher in patients with EGF than DGF (all p values values <0.001). Especially, ADC measured from ROI covering the entire kidney parenchyma had the best cut-off value (1.93µm2/msec) with the highest area under the receiver operating characteristic curve (AUC 0.943) in differentiating EGF from DGF. For analysis of pair-wise differences, only the perfusion fraction values, measured from the ROI covering the renal cortex, were significantly higher in 14 DGF patients with no complications than in the 9 DGF patients with complications, with the best cut-off value of 12.3% and the AUC of 0.844. Conclusion: Noninvasive IVIM-DWI reliably differentiates DGF from EGF after kidney transplantation, and it may aid in identifying posttransplant complications and indications for renal biopsy.

Detection of DLBCL by pixel purity index and iterative linearly constrained minimum variance into hyperspectral imaging analysis.

It is unclear whether a hyperspectral imaging-based approach can facilitate the diagnosis of diffuse large B-cell lymphoma (DLBCL), and further investigation is required. In this study, the pixel purity index (PPI) coupled with iterative linearly constrained minimum variance (ILCMV) was used to bridge this gap. We retrospectively reviewed 22 pathological DLBCL specimens. Ten normal lymph node specimens were used as controls. PPI endmember extraction was performed to identify seed-training samples. ILCMV was then used to classify cell regions. The 3D receiver operating characteristic (ROC) showed that the spectral information divergence possessed superior ability to distinguish between normal and abnormal lymphoid cells owing to its stronger background suppression compared with the spectral angle mapper and mean square error methods. An automated cell hyperspectral image classification approach that combined the PPI and ILCMV was used to improve DLBCL diagnosis. This strategy intelligently resolved critical problems arising in unsupervised classification.

Development of an Electronic Stethoscope and a Classification Algorithm for Cardiopulmonary Sounds.

With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5-45% and the best voting for lung sounds falls at 5-65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap.

Factors Associated with the Risk of Major Adverse Cardiovascular Events in Patients with Ankylosing Spondylitis: A Nationwide, Population-Based Case-Control Study.

Background: Potential risk factors for major adverse cardiovascular events (MACE) in patients with ankylosing spondylitis (AS) requiring medical therapy should be investigated. Methods: We identified newly diagnosed AS patients without previous MACE from 2004 to 2012 using the National Health Insurance Research Database, matched MACE cases with non-MACE controls at a 1:4 ratio for age, gender, AS duration, and index date, and included 947 AS patients with MACE and 3896 matched controls for final analyses. By using conditional logistic regression analyses, we examined the associations of MACE with low income, urbanisation, comorbidities, common extra-articular manifestations (EAM), and medications, including nonsteroidal anti-inflammatory drugs (NSAID) of three categories (traditional NSAIDs, selective cyclooxygenase-2 inhibitors (COX-2i), and preferential COX-2is) with their annual cumulative defined daily dose (cDDD) within a year before MACE development. Results: MACE development was associated with the use of selective COX-2is (especially with annual cDDD > 132) and corticosteroids, residence in rural regions, and well-known associated comorbidities, but not with the use of traditional NSAIDs, preferential COX-2i, biologics, methotrexate, sulfasalazine, and common EAMs. Conclusions: The risk factors of MACE in newly diagnosed AS patients include residence in rural regions, well-known associated comorbidities, and the use of corticosteroids and selective COX-2is. A major limitation was the lack of information on individual lifestyle patterns and disease activity.

Assessment of diabetic small-fiber neuropathy by using short-wave infrared hyperspectral imaging.

Among patients with type 2 diabetes mellitus (T2DM), the association between hyperspectral imaging (HSI) examination and diabetic neuropathy (DN) is ascertained using HSI of the feet using four types of spectral difference measurements. DN was evaluated by traditional Michigan Neuropathy Screening Instrument (MNSI), evaluation of painful neuropathy (ID-Pain, DN4) and sudomotor function by measuring electrochemical skin conductance (ESC). Of the 120 T2DM patients and 20 healthy adults enrolled, T2DM patients are categorized into normal sudomotor (ESC >60 µS) and sudomotor dysfunction (ESC ≤ 60 µS) groups. Spectral difference analyses reveal significant intergroup differences, whereas traditional examinations cannot distinguish between the two groups. HSI waveform reflectance gradually increases with disease severity, at 1400 to 1600 nm. The area under the curve (AUC) of receiver operating characteristic (ROC) analysis for abnormal ESC is ≥0.8 for all four HSI methods. HSI could be an objective, sensitive, rapid, noninvasive and remote approach to identify early small-fiber DN.

Association Between a History of Dengue Fever and the Risk of Systemic Autoimmune Rheumatic Diseases: A Nationwide, Population-Based Case-Control Study.

Purpose: To determine the association between a history of clinically diagnosed dengue infection and the risk of systemic autoimmune rheumatic diseases (SARDs). Methods: Using claims data from the 1997-2013 Taiwanese National Health Insurance Research Database, we included 74,422 patients who were diagnosed with SARDs and 297,688 patients without SARDs who were matched (in a 1:4 ratio) for age, sex, year of SARDs index date, and city of residence. The associations between the development of SARDs and a history of dengue infection (International Classification of Diseases, Ninth Revision, Clinical Modification code 061) were investigated using conditional logistic regression analysis shown as odds ratios (ORs) with 95% confidence intervals (CIs) after adjusting for potential confounders. Results: We included 17,126 patients with systemic lupus erythematosus (SLE), 15,531 patients with Sjogren's syndrome (SS), 37,685 patients with rheumatoid arthritis (RA), 1,911 patients with systemic sclerosis (SSc), 1,277 patients with dermatomyositis (DM), and 892 patients with polymyositis (PM). SLE (OR, 4.55; 95% CI, 2.77-7.46; p <0.001) risk was significantly associated with a history of dengue infection. However, no statistically significant association was found between dengue infection and SS (OR, 1.41; 95% CI, 0.88-2.26; p = 0.155), RA (OR, 1.03; 95% CI, 0.70-1.50; p = 0.888), SSc (OR, 1.97; 95% CI, 0.38-10.29; p = 0.420), DM (OR, 0.54; 95% CI, 0.04-7.27; p = 0.641), or PM (OR, 2.08; 95% CI, 0.23-18.79; p = 0.513). Conclusion: This study revealed that a history of dengue infection was significantly associated with the risk of SLE, but not SS, RA, SSc, DM, or PM.

Impact of systemic lupus erythematosus on the 5-year survival of critically ill septic patients.

BACKGROUND: Infectious disease is an increasing threat to patients with systemic lupus erythematosus (SLE); however, the long-term outcome in critically ill septic patients with SLE remains unclear, and we aimed to address the impact of SLE on 5-year survival in critically ill septic patients. METHODS: We used the 2003-2017 nationwide data with 825,556 patients with sepsis in Taiwan. We identified lupus cases with sepsis that required admission to the intensive care unit and mechanical ventilation and selected controls matched (1:4) for age, sex, and index-year. Conditional logistic regression analysis was used to determine risk factors for mortality risk and shown as odds ratios (HRs) with 95% confidence intervals (CIs). RESULTS: A total of 513 SLE-sepsis patients and 2052 matched non-SLE septic individuals were enrolled. The mortality rate was higher in the SLE group (38.5 per 100,000 person-year) than that in the non-SLE group (13.7 per 100,000 person-year), with an IRR of 2.8 (95% CI, 2.5-3.2). We found that SLE was independently associated with a high mortality rate after adjusting relevant variables (HR 1.47, 95% CI 1.27-1.77). In addition to SLE, a higher age (HR 1.02, 95% CI 1.02-1.02), more comorbidities, and receiving prednisolone equivalent dose higher than 5 mg/day (HR 1.55, 95% CI 1.27-1.90), methotrexate (HR 2.19, 95% CI 1.61-2.99), and immunosuppressants (HR 1.45, 95% CI 1.22-1.74) were also independent risks for mortality. CONCLUSIONS: We identified that SLE affects the long-term mortality in critically ill septic patients, and more studies are warranted for the underlying mechanism.

Association of the Risk of Primary Sjögren's Syndrome With Fibrocystic Breast Disease: A Nationwide, Population-Based Study.

Objective: Primary Sjögren's syndrome (pSS) is characterized by exocrine glandular inflammation; however, the association between preceding mammary-gland-inflammation-related diseases and newly diagnosed pSS remains unexplored. Methods: We used the 2003-2013 data retrieved from Taiwan's National Health Insurance Research Database (NHIRD) to conduct the present population-based study. We identified newly diagnosed pSS female patients during the 2001-2013 period, as well as age-matched (1:20) and propensity-score-matched (1:2) non-SS individuals (as controls). We explored the associations between pSS and a history of mastitis and fibrocystic breast disease by determining adjusted odds ratios (aORs) with 95% confidence intervals (CIs) using a conditional logistical regression analysis after controlling for potential confounders. Results: We identified 9,665 patients with pSS and 193,300 age-matched non-SS controls, as well as 9,155 SS cases and 18,310 propensity-score-matched non-SS controls. We found that fibrocystic breast disease (aOR, 1.75; 95% CI, 1.63-1.88) were independently associated with incident SS, whereas mastitis and childbirth-associated breast infections were not associated with incident SS. We also found positive associations between SS and previously reported SS-associated diseases, including cardiovascular diseases, thyroid diseases, pancreatitis, bronchiectasis, infectious diseases, osteoporosis, and ankylosing spondylitis. In the propensity-score-matched populations, the associations between pSS and fibrocystic breast disease (aOR, 1.74; 95% CI, 1.58-1.91) remained consistent. Conclusion: The present population-based study revealed a previously unexplored association between pSS and history of fibrocystic breast disease, and the finding highlights the need to survey pSS in patients with mammary-gland-inflammation-associated diseases.

Comparison of Multispectral Image-Processing Methods for Brain Tissue Classification in BrainWeb Synthetic Data and Real MR Images.

Accurate quantification of brain tissue is a fundamental and challenging task in neuroimaging. Over the past two decades, statistical parametric mapping (SPM) and FMRIB's Automated Segmentation Tool (FAST) have been widely used to estimate gray matter (GM) and white matter (WM) volumes. However, they cannot reliably estimate cerebrospinal fluid (CSF) volumes. To address this problem, we developed the TRIO algorithm (TRIOA), a new magnetic resonance (MR) multispectral classification method. SPM8, SPM12, FAST, and the TRIOA were evaluated using the BrainWeb database and real magnetic resonance imaging (MRI) data. In this paper, the MR brain images of 140 healthy volunteers (51.5 ± 15.8 y/o) were obtained using a whole-body 1.5 T MRI system (Aera, Siemens, Erlangen, Germany). Before classification, several preprocessing steps were performed, including skull stripping and motion and inhomogeneity correction. After extensive experimentation, the TRIOA was shown to be more effective than SPM and FAST. For real data, all test methods revealed that the participants aged 20-83 years exhibited an age-associated decline in GM and WM volume fractions. However, for CSF volume estimation, SPM8-s and SPM12-m both produced different results, which were also different compared with those obtained by FAST and the TRIOA. Furthermore, the TRIOA performed consistently better than both SPM and FAST for GM, WM, and CSF volume estimation. Compared with SPM and FAST, the proposed TRIOA showed more advantages by providing more accurate MR brain tissue classification and volume measurements, specifically in CSF volume estimation.

Novel Automated Method for the Detection of White Matter Hyperintensities in Brain Multispectral MR Images.

BACKGROUND: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients' conditions can be contained. AIMS: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. METHODS: The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. RESULTS: The most notable findings are as follows: Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images. CONCLUSION: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances the detection of brain lesions.

Hyperspectral imaging for skin assessment in systemic sclerosis: a pilot study.

OBJECTIVE: Hyperspectral imaging (HSI) is a novel technology for obtaining quantitative measurements from transcutaneous spatial and spectral information. In patients with SSc, the severity of skin tightness is associated with internal organ involvement. However, clinical assessment using the modified Rodnan skin score is highly variable and there are currently no universal standardized protocols. This study aimed to compare the ability to differentiate between SSc patients and healthy controls using skin scores, ultrasound and HSI. METHODS: Short-wave infrared light was utilized to detect the spectral angle mapper (SAM) of HSI. In addition, skin severity was evaluated by skin scores, ultrasound to detect dermal thickness and strain elastography. Spearman's correlation was used for assessing skin scores, strain ratio, thickness and SAM. Comparisons of various assessment tools were performed by receiver operating characteristic curves. RESULTS: In total, 31 SSc patients were enrolled. SAM was positively correlated with skin scores and dermal thickness. In SSc patients with normal skin scores, SAM values were still significantly higher than in healthy controls. SAM exhibited the highest area under the curve (AUC: 0.812, P < 0.001) in detecting SSc compared with skin scores (AUC: 0.712, P < 0.001), thickness (AUC: 0.585, P = 0.009) and strain ratio by elastography (AUC: 0.522, P = 0.510). Moreover, the severity of skin tightness was reflected by the incremental changes of waveforms in the spectral diagrams. CONCLUSION: SAM was correlated with skin scores and sufficiently sensitive to detect subclinical disease. HSI can be used as a novel, non-invasive method for assessing skin changes in SSc.

Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques.

Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.

Noninvasive assessment of intracranial elastance and pressure in spontaneous intracranial hypotension by MRI.

BACKGROUND: Spontaneous intracranial hypotension (SIH) is often misdiagnosed, and can lead to severe complications. Conventional MR sequences show a limited ability to aid in this diagnosis. MR-based intracranial pressure (MR-ICP) may be able to detect changes of intracranial elastance and pressure. PURPOSE: To determine whether MR-ICP is able to differentiate SIH patients from normal subjects, improve diagnostic sensitivity, and provide an insight into the pathophysiology. STUDY TYPE: Prospective. SUBJECTS: Twenty-eight SIH cases with orthostatic headache and 20 healthy volunteers. FIELD STRENGTH/SEQUENCE: Cine phase-contrast MRI on a 1.5T scanner. ASSESSMENT: Intracranial elastance (IE) was derived from the ratio of the peak-to-peak cerebrospinal fluid (CSF) pressure gradient (PGcsf-pp ) and intracranial volume change, obtained by summing all flows before each sequential cardiac frame. STATISTICAL TESTS: Student's t-test was used to compare the MR-ICP indexes and flow parameters between SIH patients and healthy volunteers (P < 0.01). RESULTS: The SIH patients with cervical epidural venous dilatation (EVD) had an IE of 0.121 ± 0.027 mmHg/cm/ml, significantly higher than that of the normal volunteers (0.085 ± 0.027 mmHg/cm/ml; P = 0.002). In contradistinction, the EVD-negative SIH patients, including four with no sign of CSF leaks, had significantly lower IE (0.055 ± 0.012 mmHg/cm/ml) compared with the normal volunteers and the EVD-positive group (P = 0.001, P < 0.001). The EVD-negative patients had significantly lower PGcsf-pp (0.024 ± 0.007 mmHg/cm) compared with the normal volunteers and the EVD-positive group (0.035 ± 0.011 mmHg/cm, 0.040 ± 0.010 mmHg/cm; P = 0.003, P < 0.001). Additionally, the MRI flow study showed a significant decrease in transcranial inflow and outflow of SIH patients (P < 0.01). DATA CONCLUSION: We found that the MR-ICP method is potentially more sensitive than morphological MRI in the early diagnosis of SIH. Also, contrary to common belief, our results suggest that an abnormal craniospinal elastance might be the cause of SIH, instead of CSF leak. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1255-1263.

Robust volume assessment of brain tissues for 3-dimensional fourier transformation MRI via a novel multispectral technique.

A new TRIO algorithm method integrating three different algorithms is proposed to perform brain MRI segmentation in the native coordinate space, with no need of transformation to a standard coordinate space or the probability maps for segmentation. The method is a simple voxel-based algorithm, derived from multispectral remote sensing techniques, and only requires minimal operator input to depict GM, WM, and CSF tissue clusters to complete classification of a 3D high-resolution multislice-multispectral MRI data. Results showed very high accuracy and reproducibility in classification of GM, WM, and CSF in multislice-multispectral synthetic MRI data. The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity. The method particularly allows for classification of CSF with 0.994, 0.961 and 0.996 of accuracy, sensitivity and specificity in images data with 3% noise level and 0% non-uniformity intensity, which had seldom performed well in previous studies. As for clinical MRI data, the quantitative data of brain tissue volumes aligned closely with the brain morphometrics in three different study groups of young adults, elderly volunteers, and dementia patients. The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups. The mean coefficients of variation of GM, WM, and CSF volume measurements were in the range of 0.03% to 0.30% of intra-operator measurements and 0.06% to 0.45% of inter-operator measurements. In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

Improving image quality in medical images using a combined method of undecimated wavelet transform and wavelet coefficient mapping.

We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of denoised images obtained from the first component. This methodology can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. To confirm its superiority over existing state-of-the-art methods, the proposed method is experimentally evaluated via 30 mammograms and 20 chest radiographs. It is demonstrated that the proposed method can further improve the image quality of mammograms and chest radiographs, as compared to two other methods in the literature. These results reveal the effectiveness and superiority of the proposed method.

A modified undecimated discrete wavelet transform based approach to mammographic image denoising.

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.