Extra Reading. Flashcards
PET and MRI in Neurologic Applications.
(Catana et al., 2012) - This paper reviewed the integration of PET and MRI for enhanced spatial-temporal alignment of metabolic and structural brain data, aiding in diagnosing and monitoring neurological conditions like stroke, dementia and epilepsy.
The combination improved diagnostic accuracy and workflow efficiency by addressing motion artifacts and partial-volume effects.
OCT and OCTA in Parkinson’s Disease.
(Zou et al., 2020) - The study combined Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA) to detect retinal structural and vascular changes in Parkinson’s disease.
This integration improved diagnostic accuracy for early detection by correlating macular thickness with vessel density.
Deep Learning in Chest Imaging.
(Lee et al., 2019) - This paper explored the application of deep learning (DL) models, especially CNNs, to automate feature extraction in chest radiography and CT.
It demonstrated improved nodule detection, classification of interstitial lung diseases and survival prediction, significantly enhancing diagnostic efficiency.
Fluorescence Scanning Electron Microscopy (FL-SEM).
(Kanemaru et al., 2009) - The study developed the FL-SEM, integrating fluorescence and scanning electron microscopy into one tool for correlative imaging.
It demonstrated practical applications in labelling rat tissues, combining molecular and structural insights in one imaging session.
Light Microscopy and Immunohistochemistry in Mesothelioma.
(Roberts et al., 2001) - This comparative study analysed biopsy and post-mortem specimens using light microscopy and immunohistochemistry to identify mesothelioma.
It showed histological variability and marker expression differences, emphasising the importance of combining biopsy and post-mortem data for accurate diagnosis.
