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1.
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Kocher, M, Ruge, MI, Galldiks, N, Lohmann, P
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]. 2020;(10):856-867
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Abstract
BACKGROUND Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. METHODS This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. RESULTS Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. CONCLUSION Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
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Pitfalls in liver MRI: Technical approach to avoiding misdiagnosis and improving image quality.
Yacoub, JH, Elsayes, KM, Fowler, KJ, Hecht, EM, Mitchell, DG, Santillan, C, Szklaruk, J
Journal of magnetic resonance imaging : JMRI. 2019;(1):41-58
Abstract
The following is an illustrative review of common pitfalls in liver MRI that may challenge interpretation. This article reviews common technical and diagnostic challenges encountered when interpreting dynamic multiphasic T1 -weighted imaging, hepatobiliary phase imaging, and diffusion-weighted imaging of the liver. Additionally, each section includes suggestions for avoiding diagnostic and technical errors. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:41-58.
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Harnessing the new emerging imaging technologies to uncover the role of Ca2+ signalling in plant nutrient homeostasis.
Vigani, G, Costa, A
Plant, cell & environment. 2019;(10):2885-2901
Abstract
Increasing crop yields by using ecofriendly practices is of high priority to tackle problems regarding food security and malnutrition worldwide. A sustainable crop production requires a limited use of fertilizer and the employment of plant varieties with improved ability to acquire nutrients from soil. To reach these goals, the scientific community aims to understand plant nutrients homeostasis by deciphering the nutrient sensing and signalling mechanisms of plants. Several lines of evidence about the involvement of Ca2+ as the signal of an impaired nutrient availability have been reported. Ca2+ signalling is a tightly regulated process that requires specific protein toolkits to perceive external stimuli and to induce the specific responses in the plant needed to survive. Here, we summarize both older and recent findings concerning the involvement of Ca2+ signalling in the homeostasis of nutrients. In this review, we present new emerging technologies, based on the use of genetically encoded Ca2+ sensors and advanced microscopy, which offer the chance to perform in planta analyses of Ca2+ dynamics at cellular resolution. The harnessing of these technologies with different genetic backgrounds and subjected to different nutritional stresses will provide important insights to the still little-known mechanisms of nutrient sensing in plants.
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Amide proton transfer imaging of tumors: theory, clinical applications, pitfalls, and future directions.
Kamimura, K, Nakajo, M, Yoneyama, T, Takumi, K, Kumagae, Y, Fukukura, Y, Yoshiura, T
Japanese journal of radiology. 2019;(2):109-116
Abstract
Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging technique based on chemical exchange saturation transfer (CEST). APT imaging has shown promise in oncologic imaging, especially in the imaging of brain tumors. This review article illustrates the theory of CEST/APT imaging and describes the clinical utility, pitfalls, and potential for future development of APT imaging.
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Radiomics derived from amino-acid PET and conventional MRI in patients with high-grade gliomas.
Lohmann, P, Kocher, M, Steger, J, Galldiks, N
The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of.... 2018;(3):272-280
Abstract
Radiomics is a technique that uses high-throughput computing to extract quantitative features from tomographic medical images such as MRI and PET that usually are beyond visual perception. Importantly, the radiomics approach can be performed using neuroimages that have already been acquired during the routine follow-up of the patients allowing an additional data evaluation at low cost. In Neuro-Oncology, these features can potentially be used for differential diagnosis of newly diagnosed cerebral lesions suggestive for brain tumors or for the prediction of response to a neurooncological treatment option. Furthermore, especially in the light of the recent update of the World Health Organization classification of brain tumors, radiomics also has the potential to non-invasively assess important prognostic and predictive molecular markers such as a mutation in the isocitrate dehydrogenase gene or a 1p/19q codeletion which are not accessible by conventional visual interpretation of MRI or PET findings. This review summarizes the current status of the rapidly evolving field of radiomics with a special focus on patients with high-grade gliomas.
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Automated segmentation of midbrain structures with high iron content.
Garzón, B, Sitnikov, R, Bäckman, L, Kalpouzos, G
NeuroImage. 2018;:199-209
Abstract
The substantia nigra (SN), the subthalamic nucleus (STN), and the red nucleus (RN) are midbrain structures of ample interest in many neuroimaging studies, which may benefit from the availability of automated segmentation methods. The high iron content of these structures awards them high contrast in quantitative susceptibility mapping (QSM) images. We present a novel segmentation method that leverages the information of these images to produce automated segmentations of the SN, STN, and RN. The algorithm builds a map of spatial priors for the structures by non-linearly registering a set of manually-traced training labels to the midbrain. The priors are used to inform a Gaussian mixture model of the image intensities, with smoothness constraints imposed to ensure anatomical plausibility. The method was validated on manual segmentations from a sample of 40 healthy younger and older subjects. Average Dice scores were 0.81 (0.05) for the SN, 0.66 (0.14) for the STN and 0.88 (0.04) for the RN in the left hemisphere, and similar values were obtained for the right hemisphere. In all structures, volumes of manual and automatically obtained segmentations were significantly correlated. The algorithm showed lower accuracy on R2* and T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images, which are also sensitive to iron content. To illustrate an application of the method, we show that the automated segmentations were comparable to the manual ones regarding detection of age-related differences to putative iron content.
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Monoenergetic Dual-energy Computed Tomographic Imaging: Cardiothoracic Applications.
Lenga, L, Albrecht, MH, Othman, AE, Martin, SS, Leithner, D, D'Angelo, T, Arendt, C, Scholtz, JE, De Cecco, CN, Schoepf, UJ, et al
Journal of thoracic imaging. 2017;(3):151-158
Abstract
Monoenergetic imaging is an increasingly used reconstruction technique in postprocessing of dual-energy computed tomography (DECT). The main advantage of this technique is the ability to substantially increase image contrast of structures with uptake of iodinated contrast material. Although monoenergetic imaging was mainly used in oncological DECT applications, recent research has further demonstrated its role in vascular imaging. Using this dedicated postprocessing algorithm, image contrast of vascular structures in the thorax can be increased, a drastic reduction of contrast material is feasible, and even beam-hardening artifacts can be reduced. The aim of this review article is to explain the technical background of this technique, showcase its relevance in cardiothoracic DECT, and provide an outlook on the clinical impact of this technique beyond solely improvements in image quality.
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Phenomic Approaches and Tools for Phytopathologists.
Simko, I, Jimenez-Berni, JA, Sirault, XR
Phytopathology. 2017;(1):6-17
Abstract
Plant phenomics approaches aim to measure traits such as growth, performance, and composition of plants using a suite of noninvasive technologies. The goal is to link phenotypic traits to the genetic information for particular genotypes, thus creating the bridge between the phenome and genome. Application of sensing technologies for detecting specific phenotypic reactions occurring during plant-pathogen interaction offers new opportunities for elucidating the physiological mechanisms that link pathogen infection and disease symptoms in the host, and also provides a faster approach in the selection of genetic material that is resistant to specific pathogens or strains. Appropriate phenomics methods and tools may also allow presymptomatic detection of disease-related changes in plants or to identify changes that are not visually apparent. This review focuses on the use of sensor-based phenomics tools in plant pathology such as those related to digital imaging, chlorophyll fluorescence imaging, spectral imaging, and thermal imaging. A brief introduction is provided for less used approaches like magnetic resonance, soft x-ray imaging, ultrasound, and detection of volatile compounds. We hope that this concise review will stimulate further development and use of tools for automatic, nondestructive, and high-throughput phenotyping of plant-pathogen interaction.
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Clinical quantitative susceptibility mapping (QSM): Biometal imaging and its emerging roles in patient care.
Wang, Y, Spincemaille, P, Liu, Z, Dimov, A, Deh, K, Li, J, Zhang, Y, Yao, Y, Gillen, KM, Wilman, AH, et al
Journal of magnetic resonance imaging : JMRI. 2017;(4):951-971
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Abstract
UNLABELLED Quantitative susceptibility mapping (QSM) has enabled magnetic resonance imaging (MRI) of tissue magnetic susceptibility to advance from simple qualitative detection of hypointense blooming artifacts to precise quantitative measurement of spatial biodistributions. QSM technology may be regarded to be sufficiently developed and validated to warrant wide dissemination for clinical applications of imaging isotropic susceptibility, which is dominated by metals in tissue, including iron and calcium. These biometals are highly regulated as vital participants in normal cellular biochemistry, and their dysregulations are manifested in a variety of pathologic processes. Therefore, QSM can be used to assess important tissue functions and disease. To facilitate QSM clinical translation, this review aims to organize pertinent information for implementing a robust automated QSM technique in routine MRI practice and to summarize available knowledge on diseases for which QSM can be used to improve patient care. In brief, QSM can be generated with postprocessing whenever gradient echo MRI is performed. QSM can be useful for diseases that involve neurodegeneration, inflammation, hemorrhage, abnormal oxygen consumption, substantial alterations in highly paramagnetic cellular iron, bone mineralization, or pathologic calcification; and for all disorders in which MRI diagnosis or surveillance requires contrast agent injection. Clinicians may consider integrating QSM into their routine imaging practices by including gradient echo sequences in all relevant MRI protocols. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:951-971.
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Algae through the looking glass.
Coltelli, P, Barsanti, L, Evangelista, V, Gualtieri, P
Microscopy research and technique. 2017;(5):486-494
Abstract
Microalgae are one of the most suitable subjects for testing the potentiality of light microscopy and image analysis, because of the size of single cells, their endogenous chromaticity, and their metabolic and physiological characteristics. Microscope observations and image analysis can use microalgal cells from lab cultures or collected from water bodies as model to investigate metabolic processes, behavior/reaction of cells under chemical or photic stimuli, and dynamics of population in the natural environment in response to changing conditions. In this paper we will describe the original microscope we set up together with the image processing techniques we improved to deal with these topics. Our system detects and recognizes in-focus cells, extracts their features, measures cell concentration in multi-algal samples, reconstructs swimming cell tracks, monitors metabolic processes, and measure absorption and fluorescent spectra of subcellular compartments. It can be used as digital microscopy station for algal cell biology and behavioral studies, and field analysis applications.