Deep learning generation of synthetic intermediate projections improves 177Lu SPECT images reconstructed with sparsely acquired projections

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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A novel deep-learning–based approach for automatic reorientation of 3D cardiac SPECT images

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05319-x ◽

2021 ◽

Author(s):

Duo Zhang ◽

P. Hendrik Pretorius ◽

Kaixian Lin ◽

Weibing Miao ◽

Jingsong Li ◽

...

Keyword(s):

Deep Learning ◽

Cardiac Spect ◽

Spect Images

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Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images

PLoS ONE ◽

10.1371/journal.pone.0243253 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243253

Author(s):

Qiang Lin ◽

Mingyang Luo ◽

Ruiting Gao ◽

Tongtong Li ◽

Zhengxing Man ◽

...

Keyword(s):

Deep Learning ◽

Fine Tuning ◽

Disease Stage ◽

Whole Body ◽

Learning Technology ◽

Automatic Assessment ◽

Original Dataset ◽

Bone Spect ◽

Segmentation Models ◽

Spect Images

SPECT imaging has been identified as an effective medical modality for diagnosis, treatment, evaluation and prevention of a range of serious diseases and medical conditions. Bone SPECT scan has the potential to provide more accurate assessment of disease stage and severity. Segmenting hotspot in bone SPECT images plays a crucial role to calculate metrics like tumor uptake and metabolic tumor burden. Deep learning techniques especially the convolutional neural networks have been widely exploited for reliable segmentation of hotspots or lesions, organs and tissues in the traditional structural medical images (i.e., CT and MRI) due to their ability of automatically learning the features from images in an optimal way. In order to segment hotspots in bone SPECT images for automatic assessment of metastasis, in this work, we develop several deep learning based segmentation models. Specifically, each original whole-body bone SPECT image is processed to extract the thorax area, followed by image mirror, translation and rotation operations, which augments the original dataset. We then build segmentation models based on two commonly-used famous deep networks including U-Net and Mask R-CNN by fine-tuning their structures. Experimental evaluation conducted on a group of real-world bone SEPCT images reveals that the built segmentation models are workable on identifying and segmenting hotspots of metastasis in bone SEPCT images, achieving a value of 0.9920, 0.7721, 0.6788 and 0.6103 for PA (accuracy), CPA (precision), Rec (recall) and IoU, respectively. Finally, we conclude that the deep learning technology have the huge potential to identify and segment hotspots in bone SPECT images.

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Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.104312 ◽

2021 ◽

Vol 132 ◽

pp. 104312

Author(s):

Matthew P. Adams ◽

Arman Rahmim ◽

Jing Tang

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Outcome Prediction ◽

Motor Outcome ◽

Spect Images

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A myocardial extraction method using deep learning for 99mTc myocardial perfusion SPECT images: A basic study to reduce the effects of extra-myocardial activity

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.105164 ◽

2021 ◽

pp. 105164

Author(s):

Akihiro Kikuchi ◽

Naofumi Wada ◽

Takashi Kawakami ◽

Kenichi Nakajima ◽

Hiroto Yoneyama

Keyword(s):

Deep Learning ◽

Myocardial Perfusion ◽

Extraction Method ◽

Myocardial Perfusion Spect ◽

Basic Study ◽

Perfusion Spect ◽

Spect Images

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The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images

Diagnostics ◽

10.3390/diagnostics11112091 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2091

Author(s):

Yu-Ching Ni ◽

Fan-Pin Tseng ◽

Ming-Chyi Pai ◽

Ing-Tsung Hsiao ◽

Kun-Ju Lin ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Transfer Learning ◽

Lewy Body ◽

Lewy Body Dementia ◽

Model Complexity ◽

Patient Treatment ◽

Data Set ◽

Spect Images

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.

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