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Radiosensitizing high-Z metal nanoparticles for enhanced radiotherapy of glioblastoma multiforme.
J Nanobiotechnology. 2020 Sep 3;18(1):122. Abstract Radiotherapy is an essential step during the treatment of glioblastoma multiforme (GBM), one of the most lethal malignancies. The survival in patie…
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Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.
Eur J Nucl Med Mol Imaging. 2021 Apr;48(4):1116–1123. Abstract Purpose: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the cli…
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Translating amyloid PET of different radiotracers by a deep generative model for interchangeability
Neuroimage. 2021,19:117890. Abstract It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeability of amylo…
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Refining diagnosis of Parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging
Neuroimage Clin. 2017 Sep 10;16:586–594. Abstract Dopaminergic degeneration is a pathologic hallmark of Parkinson’s disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT …
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Generation of structural MR images from amyloid PET: Application to MR-less quantification
J Nucl Med. 2018 Jul;59(7):1111–1117. Abstract Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the …
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Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging
Behav Brain Res. 2018 May 15;344:103–109. Abstract For effective treatment of Alzheimer’s disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. We…
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Predicting Aging of Brain Metabolic Topography Using Variational Autoencoder
Front Aging Neurosci. 2018 Jul 12;10:212. Abstract Predicting future brain topography can give insight into neural correlates of aging and neurodegeneration. Due to variability in the aging process, …
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Deep learning only by normal brain PET identify unheralded brain anomalies
EBioMedicine. 2019;43:447–453. Abstract Background: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical applicatio…
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Alzheimer’s Disease Neuroimaging Initiative. Amyloid PET Quantification Via End-to-End Training of a Deep Learning
Nucl Med Mol Imaging. 2019;53(5):340–348. Abstract Purpose: Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its r…
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