Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS

Background: Magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of the million molars. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient NSR comes at the cost of a long acquisition time. Purpose: We propose to use deep-learning approaches to denoise MRS data without increasing the NSA. Methods: The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192) which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improved SNRs. Results: With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 22.8% and the MSE decreased by 47.3%. For low NSA images of the human parietal and temporal lobes, the SNR increased by 43.8% and the MSE decreased by 68.8%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra, suggesting no distortion to the spectra from denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. Conclusions: The reported SAE denoising method is a model-free approach to enhance the SNR of low NSA MRS data. With the denoising capability, it is possible to acquire MRS data with a few NSA, resulting in shorter scan times while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest

Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visualization

The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique

Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS

International audienceProstate cancer is considered to be the third and sixth leading cause of death from cancer in men in developed and developing countries, respectively. As Multiparametric Magnetic Resonance Imaging (mp-MRI) and Magnetic Resonance Spectroscopic Imaging (MRSI) play an important role in the detection and the localization of cancerous tissues, in this paper, we propose a SVM and Random Forest based supervised classification schema, based on three classes (Healthy, Benign, Malignant) and on an MRI/MRSI data base of 34 patients. A total of 7711 spectroscopy voxels were exploited. The first contribution of this paper is to present improvements of the automatic classification results compared with those of Parfait, thanks to an improvement in the quality of spectra. We also improved the global detection by introducing mp-MRI based features in the classification process. We selected the most discriminative features by evaluating several combinations of MRI modalities. Moreover, we have extended the analysis to the entire prostate gland (peripheral zone (PZ) and central gland (CG)). We evaluated the SVM classifier's ability to discriminate healthy and malignant voxels and the proposed method produces a global error rate of 1%, sensitivity of 99.1% and specificity of 98.4%. The three classes, including benign voxels data were then evaluated. An error rate of 18.2%, a sensitivity of 72% and a specificity of 88% were obtained when associating Random Forest classifier, MRSI, Dynamic Contrast-Enhanced MRI and Diffusion-Weighted MRI. We finally present classification results in the form of color-coded maps, which are a computer aided diagnosis tool which could help in the evaluation of the results and could also provide an estimation of tumor shape and volume. (C) 2016 Elsevier Ltd. All rights reserved