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Thanseer, N.T.K.,Bhadada, Sanjay Kumar,Sood, Ashwani,Parihar, Ashwin Singh,Dahiya, Divya,Singh, Priyanka,Basher, Rajender Kumar,Das, Ashim,Mittal, Bhagwant R. 대한핵의학회 2018 핵의학 분자영상 Vol.52 No.2
$^{18}F$-Fluorocholine (FCH) PET/CT is evolving as a functional imaging modality for the preoperative imaging of abnormal parathyroid tissue(s) helping to localize eutopic and ectopic parathyroid tissue and limit the extent of surgery. FCH PET/CT may show incidental uptake in various thyroid lesions necessitating further evaluation, whereas the role of $^{18}F$-fluorodeoxyglucose (FDG) PET/CT in the detection of incidental thyroid nodules is well documented. The case of a middle-aged woman with dual pathology of parathyroid adenoma and papillary thyroid cancer detected on FCH and FDG PET/CT is presented.
Sensitive Detection of a Small Parathyroid Adenoma Using Fluorocholine PET/CT: A Case Report
Thanseer N. T. K. Padinhare-Keloth,Sanjay K. Bhadada,Ashwani Sood,Rajender Kumar,Arunanshu Behera,Bishan D. Radotra,Bhagwant R. Mittal 대한핵의학회 2017 핵의학 분자영상 Vol.51 No.2
Primary hyperparathyroidism is caused by parathyroidadenoma in the majority of cases and diagnosis is usuallymade biochemically. Pre-surgical localization of parathyroidadenoma is essential to limit the extent of surgery and avoidmissing them at ectopic sites. Anatomical and functional imagingare used for the localization, but may fail to identify thesmall and ectopic parathyroid adenoma. We present a case ofsmall sized ectopic parathyroid adenoma at unusual locationdetected by F-18 fluorocholine (FCH) PET/CT, where otherimaging modalities failed. The post-operative histopathologyconfirmed the diagnosis of ectopic parathyroid adenoma.
Supervised ECG wave segmentation using convolutional LSTM
Aman Malali,Srinidhi Hiriyannaiah,Siddesh G.M.,Srinivasa K.G.,Sanjay N.T. 한국통신학회 2020 ICT Express Vol.6 No.3
Electrocardiogram (ECG) is the graphical representation of electrical activity of the heart and is used to detect certain structural and functional heart conditions. Segmenting ECG waveforms and annotating constituent components is required for analysis of ECG and to arrive at a diagnosis. This paper proposes a Convolutional Long Short-Term Memory (ConvLSTM) neural network to segment the ECG waves. It consists of a convolutional layer followed by a Bidirectional LSTM architecture. The segmentation is achieved by adding additional features such as derivative of the ECG wave as well as the smoothened ECG wave and the model outperforms traditional Markov models.