Incorporation of Computed Tomography and Magnetic Resonance Imaging Function into NaYF4:Yb/Tm Upconversion Nanoparticles for in Vivo Trimodal Bioimaging

2013 ◽  
Vol 85 (24) ◽  
pp. 12166-12172 ◽  
Author(s):  
Ji-Wei Shen ◽  
Cheng-Xiong Yang ◽  
Lu-Xi Dong ◽  
Hao-Ran Sun ◽  
Kai Gao ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Upconversion Nanoparticles ◽  
Resonance Imaging

Neuroimaging for the Clinician

2017 ◽  
Author(s):  
Joshua P Klein
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Active Role ◽  
Cellular Level ◽  
Vascular Pathology ◽  
Resonance Spectroscopy ◽  
Resonance Imaging ◽  
Spinal Imaging

Modern neuroimaging has revolutionized the practice of neurology by allowing visualization and monitoring of evolving pathophysiologic processes. High-resolution magnetic resonance imaging (MRI) can now resolve structural abnormalities on a near-cellular level. Advances in functional imaging can assess the in vivo metabolic, vascular, and functional states of neuronal and glial populations in real time. Given the high density of data obtained from neuroimaging studies, it is essential for the clinician to take an active role in understanding the nature and significance of imaging abnormalities. This chapter reviews computed tomography and MRI techniques (including angiography and advanced sequences), specialized protocols for investigating specific diagnoses, risks associated with imaging, disease-specific imaging findings with general strategies for interpretation, and incidental findings and artifacts. Figures include computed tomography, T1- and T2-weighted signal intensity, diffusion-weighted magnetic resonance imaging, magnetic resonance spectroscopy, imaging in epilepsy and dementia, extra-axial versus intra-axial lesions, typical lesions of multiple sclerosis, spinal imaging, spinal pathology, vascular pathology, intracranial hemorrhage, and common imaging artifacts. Tables list Hounsfield units, patterns of enhancement from imaging, advanced techniques in imaging, magnetic resonance imaging sequences, and the evolution of cerebral infarction and intraparenchymal hemorrhage on magnetic resonance imaging. This review contains 12 figures, 6 tables, and 213 references.

Neuroimaging for the Clinician

2018 ◽  
Author(s):  
Joshua P Klein
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Active Role ◽  
Cellular Level ◽  
Vascular Pathology ◽  
Resonance Spectroscopy ◽  
Resonance Imaging ◽  
Spinal Imaging

Modern neuroimaging has revolutionized the practice of neurology by allowing visualization and monitoring of evolving pathophysiologic processes. High-resolution magnetic resonance imaging (MRI) can now resolve structural abnormalities on a near-cellular level. Advances in functional imaging can assess the in vivo metabolic, vascular, and functional states of neuronal and glial populations in real time. Given the high density of data obtained from neuroimaging studies, it is essential for the clinician to take an active role in understanding the nature and significance of imaging abnormalities. This chapter reviews computed tomography and MRI techniques (including angiography and advanced sequences), specialized protocols for investigating specific diagnoses, risks associated with imaging, disease-specific imaging findings with general strategies for interpretation, and incidental findings and artifacts. Figures include computed tomography, T1- and T2-weighted signal intensity, diffusion-weighted magnetic resonance imaging, magnetic resonance spectroscopy, imaging in epilepsy and dementia, extra-axial versus intra-axial lesions, typical lesions of multiple sclerosis, spinal imaging, spinal pathology, vascular pathology, intracranial hemorrhage, and common imaging artifacts. Tables list Hounsfield units, patterns of enhancement from imaging, advanced techniques in imaging, magnetic resonance imaging sequences, and the evolution of cerebral infarction and intraparenchymal hemorrhage on magnetic resonance imaging. This chapter contains 213 references.

Targeting Peritumoral Lesions Identified by Computed Tomography and Magnetic Resonance Imaging in Feline Injection-Site Sarcomas for Microscopic Examination

2021 ◽  
pp. 030098582110129
Author(s):  
Christiane V. Löhr ◽  
Susanne M. Stieger-Vanegas ◽  
Jesse L. Terry ◽  
Milan Milovancev ◽  
Jan Medlock
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Injection Site ◽  
Microscopic Examination ◽  
In Vivo Imaging ◽  
Surgical Margins ◽  
Surrounding Tissue ◽  
Resonance Imaging

Peritumoral lesions identified during in vivo imaging of feline injection-site sarcoma (FISS) are frequently interpreted as neoplastic. We recently showed that most peritumoral imaging-identified lesions (PTIILs) in FISS are non-neoplastic. In this article, we describe a protocol to target PTIIL for microscopic examination and report on the protocol’s performance. Ten client-owned cats with FISS were prospectively enrolled. A fiducial marker sutured onto the skin, centered on the palpable mass, served as reference point throughout the study. Each FISS and surrounding tissue was imaged in vivo by dual phase computed tomography angiography and multiple magnetic resonance imaging pulse sequences and each PTIIL documented. Subgross measurements obtained during trimming aided localization and identification of PTIIL during microscopy. Histologic findings were categorized by descending clinical relevance: neoplastic, equivocal, non-neoplastic, within normal limits (WNL). Based on in vivo imaging resolution limits, histologic findings were ≥3 mm in at least one dimension and ≥3 mm apart. Surgical margins served as control tissue for PTIILs. Eighty-one of 87 PTIIL were examined histologically; 13 were neoplastic, 16 equivocal, and 28 non-neoplastic; 24 had no identified histologic correlate. Two neoplastic and 10 equivocal findings were located outside of PTIILs but none of them were located in sections of surgical margins. Computation of a simple confusion matrix yielded fair sensitivity (70.4%) and low specificity (59.7%) for prediction of PTIIL by histologic findings. After combining instances of normal microanatomy with non-neoplastic histologic findings, specificity increased (85.1%) and sensitivity decreased (35.8%). The protocol is a blueprint for targeting PTIIL for microscopic examination but may benefit from further refinement.

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