Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

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Deep learning adapted to differential neural networks used as pattern classification of electrophysiological signals

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2021.3066996 ◽

2021 ◽

pp. 1-1

Author(s):

Dusthon Llorente ◽

Mariana Ballesteros ◽

Ivan DE JESUS Salgado Ramos ◽

Jorge Isaac Chairez Oria

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Pattern Classification ◽

Electrophysiological Signals

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Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS)

Scientific Reports ◽

10.1038/s41598-020-80855-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Virginia Liberini ◽

Fotis Kotasidis ◽

Valerie Treyer ◽

Michael Messerli ◽

Erika Orita ◽

...

Keyword(s):

Neuroendocrine Tumors ◽

Motion Correction ◽

Accessory Spleen ◽

Pancreatic Neuroendocrine Tumor ◽

Reconstruction Algorithm ◽

Data Driven ◽

Operating Characteristics ◽

Quantitative Pet ◽

Pet Ct ◽

Accessory Spleens

AbstractTo evaluate whether quantitative PET parameters of motion-corrected 68Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent 68Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM1000). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM1000 reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM1000 reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM1000/OSEM + DDG/BSREM1000 + DDG, respectively. BSREM1000 + DDG can accurately differentiate pNET from accessory spleen. Both BSREM1000 and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.

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Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05232-3 ◽

2021 ◽

Author(s):

Rui Guo ◽

Xiaobin Hu ◽

Haoming Song ◽

Pengpeng Xu ◽

Haoping Xu ◽

...

Keyword(s):

Deep Learning ◽

Fdg Pet ◽

Cell Lymphoma ◽

Training Set ◽

Test Set ◽

Natural Killer T Cell ◽

Pet Ct ◽

Weakly Supervised ◽

Fdg Pet Ct ◽

Killer T Cell

Abstract Purpose To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment 18F-FDG PET/CT results. Methods One hundred and sixty-seven patients with ENKTL who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features. Results PSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods. Conclusion The WSDL-based framework was more effective for extracting 18F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

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Evaluation of a Novel Elastic Respiratory Motion Correction Algorithm on Quantification and Image Quality in Abdominothoracic PET/CT

Journal of Nuclear Medicine ◽

10.2967/jnumed.118.213884 ◽

2018 ◽

Vol 60 (2) ◽

pp. 279-284 ◽

Cited By ~ 3

Author(s):

Joseph G. Meier ◽

Carol C. Wu ◽

Sonia L. Betancourt Cuellar ◽

Mylene T. Truong ◽

Jeremy J. Erasmus ◽

...

Keyword(s):

Image Quality ◽

Motion Correction ◽

Respiratory Motion ◽

Correction Algorithm ◽

Respiratory Motion Correction ◽

Pet Ct

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Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

Tomography ◽

10.3390/tomography8010011 ◽

2022 ◽

Vol 8 (1) ◽

pp. 131-141

Author(s):

Kanae Takahashi ◽

Tomoyuki Fujioka ◽

Jun Oyama ◽

Mio Mori ◽

Emi Yamaga ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Image Classification ◽

Test Data ◽

Characteristic Curve ◽

Maximum Intensity Projection ◽

Maximum Intensity ◽

Training Data ◽

Ct Image ◽

Pet Ct

Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.

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Stock Pattern Classification from Charts using Deep Learning Algorithms

Academic Perspective Procedia ◽

10.33793/acperpro.03.01.89 ◽

2020 ◽

Vol 3 (1) ◽

pp. 445-454

Author(s):

Celal Buğra Kaya ◽

Alperen Yılmaz ◽

Gizem Nur Uzun ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Pattern Classification ◽

Short Term Memory ◽

Stock Exchange ◽

Learning Techniques ◽

Proposed Model ◽

Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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Evaluation of a new motion correction algorithm in PET/CT

Nuclear Medicine Communications ◽

10.1097/mnm.0000000000000423 ◽

2016 ◽

Vol 37 (2) ◽

pp. 162-170 ◽

Cited By ~ 8

Author(s):

Ryogo Minamimoto ◽

Takuya Mitsumoto ◽

Yoko Miyata ◽

Fumio Sunaoka ◽

Miyako Morooka ◽

...

Keyword(s):

Motion Correction ◽

Correction Algorithm ◽

Pet Ct

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Robust MR-free Grey Matter Extraction in Amyloid PET/CT Studies with Deep Learning

2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) ◽

10.1109/nss/mic42677.2020.9507836 ◽

2020 ◽

Author(s):

Luca Presotto ◽

Carolina Bezzi ◽

Giovanna Vanoli ◽

Cristina Muscio ◽

Fabrizio Tagliavini ◽

...

Keyword(s):

Deep Learning ◽

Grey Matter ◽

Amyloid Pet ◽

Pet Ct ◽

Ct Studies

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P1218Deep learning survival analysis enhances the value of hybrid PET/CT for long-term cardiovascular event prediction

European Heart Journal ◽

10.1093/eurheartj/ehz748.0177 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Cited By ~ 1

Author(s):

L E Juarez-Orozco ◽

J W Benjamins ◽

T Maaniitty ◽

A Saraste ◽

P Van Der Harst ◽

...

Keyword(s):

Myocardial Infarction ◽

Survival Analysis ◽

Deep Learning ◽

Ct Angiography ◽

Time To Event ◽

Clinical Variables ◽

Pet Ct ◽

Early Revascularization

Abstract Background Deep Learning (DL) is revolutionizing cardiovascular medicine through complex data-pattern recognition. In spite of its success in the diagnosis of coronary artery disease (CAD), DL implementation for prognostic evaluation of cardiovascular events is still limited. Traditional survival models (e.g.Cox) notably incorporate the effect of time-to-event but are unable to exploit complex non-liner dependencies between large numbers of predictors. On the other hand, DL hasn't systematically incorporated time-to-event for prognostic evaluations. Long-term registries of hybrid PET/CT imaging represent a suitable substrate for DL-based survival analysis due the large amount of time-dependent structured variables that they convey. Therefore, we sought to evaluate the feasibility and performance of DL Survival Analysis in predicting the occurrence of myocardial infarction (MI) and death in a long-term registry of cardiac hybrid PET/CT. Methods Data from our PET/CT registry of symptomatic patients with intermediate CAD risk who underwent sequential CT angiography and 15O-water PET for suspected ischemia, was analyzed. The sample has been followed for a 6-year average for MI or death. Ten clinical variables were extracted from electronic records including cardiovascular risk factors, dyspnea and early revascularization. CT angiography images were evaluated segmentally for: presence of plaque, % of luminal stenosis and calcification (58 variables). Absolute stress PET myocardial perfusion data was evaluated globally and regionally across vascular territories (4 variables). Cox-Nnet (a deep survival neural network) was implemented in a 5-fold cross-validated 80:20 split for training and testing. Resulting DL-hazard ratios were operationalized and compared to the observed events developed during follow-up. The performance of Cox-Nnet evaluating structured CT, PET/CT, and PET/CT+clinical variables was compared to expert interpretation (operationalized as: normal coronaries, non-obstructive CAD, obstructive CAD) and to Calcium Score (CaSc), through the concordance (c)-index. Results There were 426 men and 525 women with a mean age of 61±9 years-old. Twenty-four MI and 49 deaths occurred during follow-up (1 month–9.6 years), while 11.5% patients underwent early revascularization. Cox-Nnet evaluation of PET/CT data (c-index=0.75) outperformed categorical expert interpretation (c-index=0.54) and CaSc (c-index=0.65), while hybrid PET/CT and PET/CT+clinical (c-index=0.75) variables demonstrated incremental performance overall independent from early revascularization. Conclusion Deep Learning Survival Analysis is feasible in the evaluation of cardiovascular prognostic data. It might enhance the value of cardiac hybrid PET/CT imaging data for predicting the long-term development of myocardial infarction and death. Further research into the implementation of Deep Learning for prognostic analyses in CAD is warranted.

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