Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.

BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored. PURPOSE: To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria. STUDY TYPE: Retrospective. SUBJECTS: A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria. FIELD STRENGTH/SEQUENCE: Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery. ASSESSMENT: Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis). STATISTICAL TESTS: We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05. RESULTS: In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis. DATA CONCLUSION: ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM.

BACKGROUND: Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine disease status, prognosis assessment, guidance and management, but is not well studied in the framework of machine learning. METHODS: We proposed empirical kernel mapping-based kernel extreme learning machine plus (EKM-KELM+) classifier to discriminate different blood pressure grades in adults from structural brain MR images. ELM+ is the extended version of ELM, which integrates the additional privileged information about training samples in ELM to help train a more effective classifier. In this work, we extracted gray matter volume (GMV), white matter volume, cerebrospinal fluid volume, cortical surface area, cortical thickness from structural brain MR images, and constructed brain network features based on thickness. After feature selection and EKM, the enhanced features are obtained. Then, we select one feature type as the main feature to feed into KELM+, and the rest of the feature types are PI to assist the main feature to train 5 KELM+ classifiers. Finally, the 5 KELM+ classifiers are ensemble to predict classification result in the test stage, while PI is not used during testing. RESULTS: We evaluated the performance of the proposed EKM-KELM+ method using four grades of hypertension data (73 samples for each grade). The experimental results show that the GMV performs observably better than any other feature types with a comparatively higher classification accuracy of 77.37% (Grade 1 vs. Grade 2), 93.19% (Grade 1 vs. Grade 3), and 95.15% (Grade 1 vs. Grade 4). The most discriminative brain regions found using our method are olfactory, orbitofrontal cortex (inferior), supplementary motor area, etc. CONCLUSIONS: Using region of interest features and brain network features, EKM-KELM+ is proposed to study the most discriminative regions that have obvious structural changes in different blood pressure grades. The discriminative features that are selected using our method are consistent with the existing neuroimaging studies. Moreover, our study provides a potential approach to take effective interventions in the early period, when the blood pressure makes minor impacts on the brain structure and function.

Cortical bone water concentration: dependence of MR imaging measures on age and pore volume fraction.

PURPOSE: To quantify bulk bone water to test the hypothesis that bone water concentration (BWC) is negatively correlated with bone mineral density (BMD) and is positively correlated with age, and to propose the suppression ratio (SR) (the ratio of signal amplitude without to that with long-T2 suppression) as a potentially stronger surrogate measure of porosity, which is evaluated ex vivo and in vivo. MATERIALS AND METHODS: Human subject studies were conducted in compliance with institutional review board and HIPAA regulations. Healthy men and women (n = 72; age range, 20-80 years) were examined with a hybrid radial ultrashort echo time magnetic resonance (MR) imaging sequence at 3.0 T, and BWC was determined in the tibial midshaft. In a subset of 40 female subjects, the SR was measured with a similar sequence. Cortical volumetric BMD (vBMD) was measured by means of peripheral quantitative computed tomography (CT). The method was validated against micro-CT-derived porosity in 13 donor human cortical bone specimens. Associations among parameters were evaluated by using standard statistical tools. RESULTS: BWC was positively correlated with age (r = 0.52; 95% confidence interval [CI]: 0.22, 0.73; P = .002) and negatively correlated with vBMD at the same location (r = -0.57; 95% CI: -0.76, -0.29; P < .001). Data were suggestive of stronger associations with SR (r = 0.64, 95% CI: 0.39, 0.81, P < .001 for age; r = -0.67, 95% CI: -0.82, -0.43, P < .001 for vBMD; P < .001 for both), indicating that SR may be a more direct measure of porosity. This interpretation was supported by ex vivo measurements showing SR to be strongly positively correlated with micro-CT porosity (r = 0.88; 95% CI: 0.64, 0.96; P < .001) and with age (r = 0.87; 95% CI: 0.62, 0.96; P < .001). CONCLUSION: The MR imaging-derived SR may serve as a biomarker for cortical bone porosity that is potentially superior to BWC, but corroboration in larger cohorts is indicated.

Investigating the performance of susceptibility-weighted images in Fricke gel dosimeter reading and optimizing the related imaging parameters.

Fricke gel dosimeters are especially useful in small-field dosimetry and validation of treatment delivery in three-dimensional space with features such as tissue equivalence, non-toxicity, high spatial resolution, non-dependence on energy, and dose rate. The use of basic Magnetic Resonance Imaging (MRI) protocols (T1- and T2-Weighted) for reading Fricke gel dosimeters has always been considered the dominant method in many studies. However, the development and application of advanced MRI techniques for more accurate readings of Fricke gel dosimeters can be useful. Considering that in the main structure of Fricke gel, there are conversions of iron ions to each other, this study aimed to investigate the performance of Susceptibility-Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM) based on magnetic susceptibility in the reading of Fricke gel dosimeters and to optimize the related imaging parameters. For this purpose, a Fricke-Xylenol orange-gelatin was made at five concentrations of iron ammonium sulfate. To obtain gel dosimeter calibration curves, vials containing gel were subjected to irradiation at three different doses by a linear accelerator. The reading of gel dosimeters was performed using MRI imaging in three protocols, T1W, T2W, and SWI, and analyzed with a method unique to each one. Finally, the results obtained from the three protocols were compared with each other. The comparison of calibration curves in three imaging protocols shows that the sensitivity of calibration curves in SWI was about three times its value in T2W, and on the other hand, the reported sensitivity in T1W was very small compared to the other two protocols. The linearity factor was similar between SWI and T1W protocols and higher in T2W. Therefore, it is concluded that in addition to the relaxometry techniques that have been used as a conventional method for reading Fricke gel dosimeter, SWI imaging has high sensitivity and specificity for reading dosimeter gel based on iron.

Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study.

(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.

Comparison of optimized soft-tissue suppression schemes for ultrashort echo time MRI.

Ultrashort echo time (UTE) imaging with soft-tissue suppression reveals short-T(2) components (typically hundreds of microseconds to milliseconds) ordinarily not captured or obscured by long-T(2) tissue signals on the order of tens of milliseconds or longer. Therefore, the technique enables visualization and quantification of short-T(2) proton signals such as those in highly collagenated connective tissues. This work compares the performance of the three most commonly used long-T(2) suppression UTE sequences, i.e., echo subtraction (dual-echo UTE), saturation via dual-band saturation pulses (dual-band UTE), and inversion by adiabatic inversion pulses (IR-UTE) at 3 T, via Bloch simulations and experimentally in vivo in the lower extremities of test subjects. For unbiased performance comparison, the acquisition parameters are optimized individually for each sequence to maximize short-T(2) signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) between short- and long-T(2) components. Results show excellent short-T(2) contrast which is achieved with these optimized sequences. A combination of dual-band UTE with dual-echo UTE provides good short-T(2) SNR and CNR with less sensitivity to B(1) homogeneity. IR-UTE has the lowest short-T(2) SNR efficiency but provides highly uniform short-T(2) contrast and is well suited for imaging short-T(2) species with relatively short T(1) such as bone water.

Quantifying cortical bone water in vivo by three-dimensional ultra-short echo-time MRI.

Bone contains a significant fraction of water that is not detectable with ordinary Cartesian imaging sequences. The advent of ultra-short echo-time (UTE) methods has allowed the recovery of this submillisecond T(2)* water. In this work, we have developed a new three-dimensional hybrid-radial ultra-short echo-time (3D HRUTE) imaging technique based on slab selection by means of half-sinc pulses, variable-TE slice encoding and algorithms for quantification. The protocol consists of collecting two datasets differing in TR, from which T(1) is extracted, which is needed for quantification. Unlike T(2)*, which has been found to vary within a narrow range and does not require individual correction, T(1) is critically subject dependent (range, 100-350 ms). No soft-tissue suppression was used to preserve the signal-to-noise ratio of the short-T(2) bone water protons or to minimize the loss of relatively mobile water in large pores. Critical for quantification is correction for spatial variations in reception field and selection of the endosteal boundary for inclusion of pixels in the bone water calculation, because of the ruffled boundary stemming from trabecularization of the endosteal surface. The reproducibility, evaluated in 10 subjects covering the age range 30-80 years, yielded an average coefficient of variation of 4.2% and an intraclass correlation coefficient of 0.95, suggesting that a treatment effect on the order of 5% could be detected in as few as 10 subjects. Lastly, experiments in specimens by means of graded deuterium exchange showed that approximately 90% of the detected signal arises from water protons, whose relaxation rates (1/T(1) and 1/T(2)*) scale linearly with the isotopic volume fraction of light water after stepwise exchange with heavy water. The data thus show conclusively that the method quantifies water even though, in vivo, no distinction can be made between various fractions, such as collagen-bound vs pore-resident water.

The Changes in 1H-MRS Metabolites in Cuprizone-Induced Model of Multiple Sclerosis: Effects of Prior Psychological Stress.

Stress is considered as an important risk factor in the progression and the onset of many disorders such as multiple sclerosis. However, metabolite changes as a result of demyelination under the detrimental effects of stress are not well understood. Thus, 36 female Wistar rats (i.e., groups (1) no-cuprizone (Cont), (2) no-stress + cuprizone-treated (Cup), (3) physical stress + cuprizone-treated (P-Cup), (4) psychological stress + cuprizone-treated (Psy-Cup), (5) physical stress + no-cuprizone-treated (P), (6) psychological stress + no-cuprizone-treated (Psy)) were used in this study. Following induction of repetitive stress, cuprizone treatment was carried out for 6 weeks to instigate demyelination in all groups except the control animal. Relative metabolite concentrations of the brain were investigated by single-voxel proton magnetic resonance spectroscopy (reporting N-acetyl-aspartate (NAA), glycerophosphocholine with phosphocholine (tCho) relative to total creatine (tCr)). According to 1H-MRS, rats in the Cup group indicated a reduction in NAA/ tCr (p < 0.001) as well as tCho/ tCr (p < 0.05) compared with that in the Cont group. In contrast, in both stress + cuprizone-treated groups, NAA/tCr and tCho/tCr ratios remarkably increased versus the Cup group (p < 0.001) and the Cont group (p < 0.001 for the Psy-Cup group and p < 0.05 for the P-Cup group). Both P and Psy groups revealed normal metabolite concentrations similar to the Cont group 6 weeks post stress. Seemingly, in the case of cuprizone alone, decreased level of metabolites is mainly relevant to neuronal cell impairments. Meanwhile, as a result of oxidative stress enhancement due to stress exposure, oligodendrocyte becomes the main victim indicating the increased level of metabolite ratios.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review.

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients.

PURPOSE: The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients.Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. RESULTS: Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group (P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1-99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9-92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group (P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2-100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3-95.8%). CONCLUSIONS: Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.

Design and Validation of Synchronous QCT Calibration Phantom: Practical Methodology.

INTRODUCTION: Quantitative computed tomography (QCT) can supplement dual x-ray absorptiometry by enabling geometric and compartmental bone assessments. Whole-body spiral CT scanners are widely available and require a short scanning time of seconds, in contrast to peripheral QCT scanners, which require several minutes of scanning time. This study designed and evaluated the accuracy and precision of a homemade QCT calibration phantom using a whole-body spiral CT scanner. MATERIALS AND METHODS: The QCT calibration phantom consisted of K2HPO4 solutions as reference. The reference material with various concentrations of 0, 50, 100, 200, 400, 1000, and 1200 mg/cc of K2HPO4 in water were used. For designing the phantom, we used the ABAQUS software. RESULTS: The phantoms were used for performance assessment of QCT method through measurement of accuracy and precision errors, which were generally less than 5.1% for different concentrations. The correlation between CT numbers and concentration were close to one (R2 = 0.99). DISCUSSION: Because whole-body spiral CT scanners allow central bone densitometry, evaluating the accuracy and precision for the easy to use calibration phantom may improve the QCT bone densitometry test. CONCLUSION: This study provides practical directions for applying a homemade calibration phantom for bone mineral density quantification in QCT technique.

T1 correlates age: A short-TE MR relaxometry study in vivo on human cortical bone free water at 1.5T.

Large pores of human cortical bone (>30µm) are filled with fluids, essentially consisting of water, suggesting that cortical bone free water can be considered as a reliable surrogate measure of cortical bone porosity and hence quality. Signal from such pores can be reliably captured using Short Echo Time (STE) pulse sequence with echo-time in the range of 1-1.5msec (which should be judiciously selected correspond to T2(⁎) value of free water molecules). Furthermore, it is well-known that cortical bone T1-relaxivity is a function of its geometry, suggesting that cortical bone free water increases with age. In this work, we quantified cortical bone free water longitudinal relaxation time (T1) by a Dual-TR technique using STE pulse sequence. In the sequel, we investigated relationship between STE-derived cortical bone free water T1-values and age in a group of healthy volunteers (thirty subjects covering the age range of 20-70years) at 1.5T. Preliminary results showed that cortical bone free water T1 highly correlates with age (r(2)=0.73, p<0.0001), representing cortical bone free water T1 as a reliable indicator of cortical bone porosity and age-related deterioration. It can be concluded that STE-MRI can be utilized as proper alternative in quantifying cortical bone porosity parameters in-vivo, with the advantages of widespread clinical availability and being cost-effective.

1H-MRS metabolite's ratios show temporal alternation in temporal lobe seizure: Comparison between interictal and postictal phases.

PURPOSES: To determine 1H-MRSI metabolites changes in interictal and postictal phases of patients suffering from mesial temporal lobe epilepsy with hippocampal sclerosis and lateralization of seizure foci. MATERIALS AND METHODS: MR spectroscopic imaging was performed in 5 adult patients with refractory temporal lobe epilepsy interictally and immediately after the seizure and in 4 adult control subjects. All patients underwent MR imaging and VideoEEG Monitoring. RESULTS: The results showed statistically significant decreases in N-acetylaspartate/Creatine, N-acetylaspartate/Choline and N-acetylaspartate/(creatine+choline) immediately after ictus in ipsilateral hippocampus as compared with control data and contralateral hippocampus of patients while no statistically significant difference was presented in interictal phase. CONCLUSION: The present study clearly indicates 1H-MRS abnormalities following an ictus of temporal lobe epilepsy with metabolite recovery in interictal phase. This finding suggests postictal 1H-MRS as a possible useful tool to assist in lateralizing and localizing of seizure foci in epileptic patients with structural lesions.

A high throughput and efficient visualization method for diffusion tensor imaging of human brain white matter employing diffusion-map space.

Diffusion tensor imaging (DTI) possesses high dimension and complex structure, so that detecting available pattern information and its analysis based on conventional linear statistics and classification methods become inefficient. In order to facilitate classification, segmentation, compression or visualization of the data, dimension reduction is far-reaching. There have been many approaches proposed for this purpose, which mostly rely on complex low dimensional manifold embedding of the high-dimensional space. Dimension reduction is commonly applicable through linear algorithms, such as principal component analysis and multi-dimensional scaling; however, they are not able to deal with complex and high dimensional data. In this light, nonlinear algorithms with the capability to preserve the distance of high dimensional data have been developed. The purpose of this paper is to propose a new method for meaningful visualization of brain white matter using diffusion tensor data to map the 6-dimensional tensor to a three dimensional space employing Markov random walk and diffusion distance algorithms, leading to a new distance-preserving map for the DTI data with lower dimension and higher throughput information.

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition.

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme.

PURPOSE: Compressed sensing (CS) provides a promising framework for MR image reconstruction from highly undersampled data, thus reducing data acquisition time. In this context, sparsity-promoting regularization techniques exploit the prior knowledge that MR images are sparse or compressible in a given transform domain. In this work, a new regularization technique was introduced by iterative linearization of the non-convex smoothly clipped absolute deviation (SCAD) norm with the aim of reducing the sampling rate even lower than it is required by the conventional l1 norm while approaching an l0 norm. MATERIALS AND METHODS: The CS-MR image reconstruction was formulated as an equality-constrained optimization problem using a variable splitting technique and solved using an augmented Lagrangian (AL) method developed to accelerate the optimization of constrained problems. The performance of the resulting SCAD-based algorithm was evaluated for discrete gradients and wavelet sparsifying transforms and compared with its l1-based counterpart using phantom and clinical studies. The k-spaces of the datasets were retrospectively undersampled using different sampling trajectories. In the AL framework, the CS-MRI problem was decomposed into two simpler sub-problems, wherein the linearization of the SCAD norm resulted in an adaptively weighted soft thresholding rule with a sparsity enhancing effect. RESULTS: It was demonstrated that the proposed regularization technique adaptively assigns lower weights on the thresholding of gradient fields and wavelet coefficients, and as such, is more efficient in reducing aliasing artifacts arising from k-space undersampling, when compared to its l1-based counterpart. CONCLUSION: The SCAD regularization improves the performance of l1-based regularization technique, especially at reduced sampling rates, and thus might be a good candidate for some applications in CS-MRI.

Effects of electric and magnetic loadings on bone surface remodeling: a model modification and simulation.

This paper presents a new modification to the previous model of bone surface remodeling under electric and magnetic loadings. For this study, the thermo-electro-magneto-elastic model of bone surface remodeling is used. This model is modified by considering an important phenomenon occurring in living bone through its adaptation to external loadings called desensitization. In fact, bone cells lose their responsiveness and sensitivity to long-term external loadings, i.e., they become desensitized. Therefore, bone cells need a recovery period, during which they become resensitized. In this work, this phenomenon is considered in the original model. The effects of various electric and magnetic loading conditions, including various frequencies, waveforms and pulse duty cycles, are explored on the modified model and compared to the original model. The modified model is also searched for the optimal frequency and duty cycle, to obtain the best bone growth response under electromagnetic fields. The results of this paper show that the modified model is consistent with experimental observations. In addition, it is indicated that this modified model in contrast to the original model, is sensitive to frequency. It is shown that the optimal frequency of loading for the modified model is 1 Hertz (Hz), and the pulse duty cycles up to 50% are sufficient for bone remodeling to reach its maximum value.

Segmentation of brain tumors in MRI images using multi-scale gradient vector flow.

The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI images. Using this idea, the active contour is evolved with respect to scaled edge maps in a multi scale manner. The edge detection performance of the modified GVF algorithm is further enhanced by applying a threshold-based edge detector to improve the edge map. The Bspline snake is selected for representation of the active contour, due to its ability to capture corners and its local control. The results showed an improvement of 30% in the accuracy of tumor segmentation against traditional GVF and 10 % as compared to Bspline GVF in the presence of noise, besides the repeatability of the algorithm in contrast to traditional GVF. The clinical evaluation also proved the accuracy and sensitivity of the proposed method as 92.8% and 95.4%, respectively.