Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Review of Cervix Cancer Classification Using Radiomics on Diffusion-Weighted Imaging

Magnetic Resonance Imaging (MRI) is one of the most used imaging modalities for the identification and quantification of various types of cancers. MRI image analysis is mostly conducted by experts relying on the visual interpretation of the images and some basic semiquantitative parameters. However, it is well known that additional clinical information is available in these images and can be harvested using the field of radiomics. This consists of the extraction of complex unexplored features from these images that can provide underlying functions in disease process. In this paper, we provide a review of the application of radiomics to extract relevant information from MRI Diffusion Weighted Imaging (DWI) for the classification of cervix cancer. The main research findings are the presentation of the state of the art of this application with the description of its main steps and related challenges

Semiparametric Bayesian local functional models for diffusion tensor tract statistics

We propose a semiparametric Bayesian local functional model (BFM) for the analysis of multiple diffusion properties (e.g., fractional anisotropy) along white matter fiber bundles with a set of covariates of interest, such as age and gender. BFM accounts for heterogeneity in the shape of the fiber bundle diffusion properties among subjects, while allowing the impact of the covariates to vary across subjects. A nonparametric Bayesian LPP2 prior facilitates global and local borrowings of information among subjects, while an infinite factor model flexibly represents low-dimensional structure. Local hypothesis testing and credible bands are developed to identify fiber segments, along which multiple diffusion properties are significantly associated with covariates of interest, while controlling for multiple comparisons. Moreover, BFM naturally group subjects into more homogeneous clusters. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFM. We apply BFM to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment in new born infants

NONINVASIVE MULTIMODAL DIFFUSE OPTICAL IMAGING OF VULNERABLE TISSUE HEMODYNAMICS

Measurement of tissue hemodynamics provides vital information for the assessment of tissue viability. This thesis reports three noninvasive near-infrared diffuse optical systems for spectroscopic measurements and tomographic imaging of tissue hemodynamics in vulnerable tissues with the goal of disease diagnosis and treatment monitoring. A hybrid near-infrared spectroscopy/diffuse correlation spectroscopy (NIRS/DCS) instrument with a contact fiber-optic probe was developed and utilized for simultaneous and continuous monitoring of blood flow (BF), blood oxygenation, and oxidative metabolism in exercising gastrocnemius. Results measured by the hybrid NIRS/DCS instrument in 37 subjects (mean age: 67 ± 6) indicated that vitamin D supplement plus aerobic training improved muscle metabolic function in older population. To reduce the interference and potential infection risk on vulnerable tissues caused by the contact measurement, a noncontact diffuse correlation spectroscopy/tomography (ncDCS/ncDCT) system was then developed. The ncDCS/ncDCT system employed optical lenses to project limited numbers of sources and detectors on the tissue surface. A motor-driven noncontact probe scanned over a region of interest to collect boundary data for three dimensional (3D) tomographic imaging of blood flow distribution. The ncDCS was tested for BF measurements in mastectomy skin flaps. Nineteen (19) patients underwent mastectomy and implant-based breast reconstruction were measured before and immediately after mastectomy. The BF index after mastectomy in each patient was normalized to its baseline value before surgery to get relative BF (rBF). Since rBF values in the patients with necrosis (n = 4) were significantly lower than those without necrosis (n = 15), rBF levels can be used to predict mastectomy skin flap necrosis. The ncDCT was tested for 3D imaging of BF distributions in chronic wounds of 5 patients. Spatial variations in BF contrasts over the wounded tissues were observed, indicating the capability of ncDCT in detecting tissue hemodynamic heterogeneities. To improve temporal/spatial resolution and avoid motion artifacts due to a long mechanical scanning of ncDCT, an electron-multiplying charge-coupled device based noncontact speckle contrast diffuse correlation tomography (scDCT) was developed. Validation of scDCT was done by imaging both high and low BF contrasts in tissue-like phantoms and human forearms. In a wound imaging study using scDCT, significant lower BF values were observed in the burned areas/volumes compared to surrounding normal tissues in two patients with burn. One limitation in this study was the potential influence of other unknown tissue optical properties such as tissue absorption coefficient (µa) on BF measurements. A new algorithm was then developed to extract both µa and BF using light intensities and speckle contrasts measured by scDCT at multiple source-detector distances. The new algorithm was validated using tissue-like liquid phantoms with varied values of µa and BF index. In-vivo validation and application of the innovative scDCT technique with the new algorithm is the subject of future work

DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI

Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner. To be specific, the proposed DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and adversarial learning to efficiently generate high-fidelity SC through a few steps from fMRI. By designing the dual-channel multi-head spatial attention (DMSA) and graph convolutional modules, the symmetric graph generator first captures global relations among direct and indirect connected brain regions, then models the local brain region interactions. It can uncover the complex mapping relations between fMRI and structural connectivity. Furthermore, the spatially connected consistency loss is devised to constrain the generator to preserve global-local topological information for accurate intrinsic SC prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed model can effectively generate empirical SC-preserved connectivity from four-dimensional imaging data and shows superior performance in SC prediction compared with other related models. Furthermore, the proposed model can identify the vast majority of important brain regions and connections derived from the empirical method, providing an alternative way to fuse multimodal brain networks and analyze clinical disease.Comment: 12 page

DEEP MULTI-MODAL SCHIZOPHRENIA DISORDER DIAGNOSIS VIA A GRU-CNN ARCHITECTURE

The file on this institutional repository is embargoed indefinitely due to licensing and copyright restrictions. Individuals may download a copy of this article from the publisher's website for personal, non-commercial use at: https://doi.org/10.14311/NNW.2022.32.009 .Schizophrenia is a complex mental disorder associated with a change in the functional and structural of the brain. Accurate automatic diagnosis of schizophrenia is crucial and still a challenge. In this paper, we propose an automatic diagnosis of schizophrenia disorder method based on the fusion of different neuroimaging features and a deep learning architecture. We propose a deep-multimodal fusion (DMMF) architecture based on gated recurrent unit (GRU) network and 2D-3D convolutional neural networks (CNN). The DMMF model combines functional connectivity (FC) measures extracted from functional magnetic resonance imaging (fMRI) data and low-level features obtained from fMRI, magnetic resonance imaging (MRI), or diffusion tensor imaging (DTI) data and creates latent and discriminative feature maps for classification. The fusion of ROI-based FC with fractional anisotropy (FA) derived from DTI images achieved state-of-theart diagnosis-accuracy of 99.50% and an area under the curve (AUC) of 99.7% on COBRE dataset. The results are promising for the combination of features. The high accuracy and AUC in our experiments show that the proposed deep learning architecture can extract latent patterns from neuroimaging data and can help to achieve accurate classification of schizophrenia and healthy groups

Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study

IntroductionDiabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.MethodsFor this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort (n1 = 49) and the testing cohort (n2 = 21). According to the estimated glomerular filtration rate (eGFR), patients were assigned into the normal renal function (normal-RF) group, the non-severe renal function impairment (non-sRI) group, and the severe renal function impairment (sRI) group. Based on the largest coronal image of T2WI, the speeded up robust features (SURF) algorithm was used for texture feature extraction. Analysis of variance (ANOVA) and relief and recursive feature elimination (RFE) were applied to select the important features and then support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms were used for the model construction. The values of area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis were used to assess their performance. The robust T2WI model was selected to construct a multimodal MRI model by combining the measured BOLD (blood oxygenation level-dependent) and diffusion-weighted imaging (DWI) values.ResultsThe mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort.DiscussionThe model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function

DIFFUSE OPTICAL MEASUREMENTS OF HEAD AND NECK TUMOR HEMODYNAMICS FOR EARLY PREDICTION OF CHEMO-RADIATION THERAPY OUTCOMES

Chemo-radiation therapy is a principal modality for the treatment of head and neck cancers, and its efficacy depends on the interaction of tumor oxygen with free radicals. In this study, we adopted a novel hybrid diffuse optical instrument combining a commercial frequency-domain tissue oximeter (Imagent) and a custom-made diffuse correlation spectroscopy (DCS) flowmeter, which allowed for simultaneous measurements of tumor blood flow and blood oxygenation. Using this hybrid instrument we continually measured tumor hemodynamic responses to chemo-radiation therapy over the treatment period of 7 weeks. We also explored monitoring dynamic tumor hemodynamic changes during radiation delivery. Blood flow data analysis was improved by simultaneously extracting multiple parameters from one single autocorrelation function curve measured by DCS. Patients were classified into two groups based on clinical outcomes: a complete response (CR) group and an incomplete response (IR) group with remote metastasis and/or local recurrence within one year. Interestingly, we found human papilloma virus (HPV-16) status largely affected tumor homodynamic responses to therapy. Significant differences in tumor blood flow index (BFI) and reduced scattering coefficient (μs’) between the IR and CR groups were observed in HPV-16 negative patients at Week 3. Significant differences in oxygenated hemoglobin concentration ([HbO2]) and blood oxygen saturation (StO2) between the two groups were found in HPV-16 positive patients at Week 1 and Week 3, respectively. Receiver operating characteristic curves were constructed and results indicated high sensitivities and specificities of these hemodynamic parameters for early (within the first three weeks of the treatment) prediction of one-year treatment outcomes. Measurement of tumor hemodynamics may serve as a predictive tool allowing treatment selection based on biologic tumor characteristics. Ultimately, reduction of side effects in patients not benefiting from radiation treatment may be feasible