Advanced Magnetic Resonance Imaging in Glioblastoma: A Review

INTRODUCTION In 2017, it is estimated that 26,070 patients will be diagnosed with a malignant primary brain tumor in the United States, with more than half having the diagnosis of glioblas- toma (GBM).1 Magnetic resonance imaging (MRI) is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma; standard modalities available from any clinical MRI scanner, including T1, T2, T2-FLAIR, and T1-contrast-enhanced (T1CE) sequences, provide critical clinical information. In the last decade, advanced imaging modalities are increasingly utilized to further charac- terize glioblastomas. These include multi-parametric MRI sequences, such as dynamic contrast enhancement (DCE), dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI), functional imaging, and spectroscopy (MRS), to further characterize glioblastomas, and significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. A contemporary review of standard and advanced MR imaging in clinical neuro-oncologic practice is presented

Acute Stroke Multimodal Imaging: Present and Potential Applications toward Advancing Care.

In the past few decades, the field of acute ischemic stroke (AIS) has experienced significant advances in clinical practice. A core driver of this success has been the utilization of acute stroke imaging with an increasing focus on advanced methods including multimodal imaging. Such imaging techniques not only provide a richer understanding of AIS in vivo, but also, in doing so, provide better informed clinical assessments in management and treatment toward achieving best outcomes. As a result, advanced stroke imaging methods are now a mainstay of routine AIS practice that reflect best practice delivery of care. Furthermore, these imaging methods hold great potential to continue to advance the understanding of AIS and its care in the future. Copyright © 2017 by Thieme Medical Publishers, Inc

Full Issue: Volume 13, Issue 1 - Winter 2018

Full Issue: Volume 13, Issue 1 - Winter 201

Characterization and digital restauration of XIV-XV centuries written parchments by means of non-destructive techniques. Three case studies

Parchment is the primary writing medium of the majority of documents with cultural importance. Unfortunately, this material suffers of several mechanisms of degradation that affect its chemical-physical structure and the readability of text. Due to the unique and delicate character of these objects, the use of nondestructive techniques is mandatory. In this work, three partially degraded handwritten parchments dating back to the XIV-XV centuries were analyzed by means of X-ray fluorescence spectroscopy, µ-ATR Fourier transform infrared spectroscopy, and reflectance and UV-induced fluorescence spectroscopy. 'e elemental and molecular results provided the identification of the inks, pigments, and superficial treatments. In particular, all manuscripts have been written with iron gall inks, while the capital letters have been realized with cinnabar and azurite. Furthermore, multispectral UV fluorescence imaging and multispectral VIS-NIR imaging proved to be a good approach for the digital restoration of manuscripts that suffer from the loss of inked areas or from the presence of brown spotting. Indeed, using ultraviolet radiation and collecting the images at different spectral ranges is possible to enhance the readability of the text, while by illuminating with visible light and by collecting the images at longer wavelengths, the hiding effect of brown spots can be attenuated

Characterization and digital restauration of XIV-XV centuries written parchments by means of non-destructive techniques. Three case studies

Parchment is the primary writing medium of the majority of documents with cultural importance. Unfortunately, this material suffers of several mechanisms of degradation that affect its chemical-physical structure and the readability of text. Due to the unique and delicate character of these objects, the use of nondestructive techniques is mandatory. In this work, three partially degraded handwritten parchments dating back to the XIV-XV centuries were analyzed by means of X-ray fluorescence spectroscopy, µ-ATR Fourier transform infrared spectroscopy, and reflectance and UV-induced fluorescence spectroscopy. 'e elemental and molecular results provided the identification of the inks, pigments, and superficial treatments. In particular, all manuscripts have been written with iron gall inks, while the capital letters have been realized with cinnabar and azurite. Furthermore, multispectral UV fluorescence imaging and multispectral VIS-NIR imaging proved to be a good approach for the digital restoration of manuscripts that suffer from the loss of inked areas or from the presence of brown spotting. Indeed, using ultraviolet radiation and collecting the images at different spectral ranges is possible to enhance the readability of the text, while by illuminating with visible light and by collecting the images at longer wavelengths, the hiding effect of brown spots can be attenuated

Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai