Head sinuses, melon, and jaws of bottlenose dolphins, Tursiops truncatus, observed with computed tomography structural and single photon emission computed tomography functional imaging

2003 ◽  
Vol 113 (4) ◽  
pp. 2308-2308
Author(s):  
Sam Ridgway ◽  
Dorian Houser ◽  
James J. Finneran ◽  
Don Carder ◽  
William Van Bonn ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Functional Imaging ◽  
Tursiops Truncatus ◽  
Single Photon ◽  
Bottlenose Dolphins ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Single Photon Emission ◽  
Single Photon Emission Computed ◽  
Photon Emission Computed Tomography

scholarly journals Low-Parameter Small Convolutional Neural Network Applied to Functional Medical Imaging of Tc-99m Trodat-1 Brain Single-Photon Emission Computed Tomography for Parkinson’s Disease

2021 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Yu-Chieh Chang ◽  
Te-Chun Hsieh ◽  
Jui-Cheng Chen ◽  
Kuan-Pin Wang ◽  
Zong-Kai Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Functional Imaging ◽  
Dopaminergic Neuron ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Single Photon Emission ◽  
Single Photon Emission Computed ◽  
Photon Emission Computed Tomography

Parkinson’s disease (PD), a progressive disease that affects movement, is related to dopaminergic neuron degeneration. Tc-99m Trodat-1 brain (TRODAT) single-photon emission computed tomography (SPECT) aids the functional imaging of dopamine transporters and is used for dopaminergic neuron enumeration. Herein, we employed a convolutional neural network to facilitate PD diagnosis through TRODAT SPECT, which is simpler than models such as VGG16 and ResNet50. We retrospectively collected the data of 3188 patients (age range 20–107 years) who underwent TRODAT SPECT between June 2011 and December 2019. We developed a set of functional imaging multiclassification deep learning algorithms suitable for TRODAT SPECT on the basis of the annotations of medical experts. We then applied our self-proposed model and compared its results with those of four other models, including deep and machine learning models. TRODAT SPECT included three images collected from each patient: one presenting the maximum absorption of the metabolic function of the striatum and two adjacent images. An expert physician determined that our model’s accuracy, precision, recall, and F1-score were 0.98, 0.98, 0.98, and 0.98, respectively. Our TRODAT SPECT model provides an objective, more standardized classification correlating to the severity of PD-related diseases, thereby facilitating clinical diagnosis and preventing observer bias.

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