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

2021 ◽  
Author(s):  
Duo Zhang ◽  
P. Hendrik Pretorius ◽  
Kaixian Lin ◽  
Weibing Miao ◽  
Jingsong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Spect ◽  
Spect Images

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

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.

scholarly journals Quality and utility of [123I]I-metaiodobenzylguanidine cardiac SPECT imaging in nondiabetic postinfarction heart failure patients qualified for implantable cardioverter defibrillator

2021 ◽  
Author(s):  
Anna Teresińska ◽  
Olgierd Woźniak ◽  
Aleksander Maciąg ◽  
Jacek Wnuk ◽  
Jarosław Jezierski ◽  
...  
Keyword(s):  
Heart Failure ◽  
Implantable Cardioverter Defibrillator ◽  
Left Lung ◽  
Spect Imaging ◽  
Cardiac Innervation ◽  
Cardiac Spect ◽  
Cardioverter Defibrillator ◽  
Planar Images ◽  
Spect Images

Abstract Objective Impaired cardiac adrenergic activity has been demonstrated in heart failure (HF) and in diabetes mellitus (DM). [123I]I-metaiodobenzylguanidine (MIBG) enables assessment of the cardiac adrenergic nervous system. Tomographic imaging of the heart is expected to be superior to planar imaging. This study aimed to determine the quality and utility of MIBG SPECT in the assessment of cardiac innervation in postinfarction HF patients without DM, qualified for implantable cardioverter defibrillator (ICD) in primary prevention of sudden cardiac death. Methods Consecutive patients receiving an ICD on the basis of contemporary guidelines were prospectively included. Planar MIBG studies were followed by SPECT. The essential analysis was based on visual assessment of the quality of SPECT images (“high”, “low” or “unacceptable”). The variables used in the further analysis were late summed defect score for SPECT images and heart-to-mediastinum rate for planar images. MIBG images were assessed independently by two experienced readers. Results Fifty postinfarction nondiabetic HF subjects were enrolled. In 13 patients (26%), the assessment of SPECT studies was impossible. In addition, in 13 of 37 patients who underwent semiquantitative SPECT evaluation, the assessment was equivocal. Altogether, in 26/50 patients (52%, 95% confidence interval 38–65%), the quality of SPECT images was unacceptable or low and was limited by low MIBG cardiac uptake and by comparatively high, interfering MIBG uptake in the neighboring structures (primarily, in the lungs). Conclusions The utility of MIBG SPECT imaging, at least with conventional imaging protocols, in the qualification of postinfarction HF patients for ICD, is limited. In approximately half of the postinfarction HF patients, SPECT assessment of cardiac innervation can be impossible or equivocal, even without additional damage from diabetic cardiac neuropathy. The criteria predisposing the patient to good-quality MIBG SPECT are: high values of LVEF from the range characterizing the patients qualified to ICD (i.e., close to 35%) and left lung uptake intensity in planar images comparable to or lower than heart uptake.

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

PLoS ONE ◽  
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.

scholarly journals Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

2021 ◽  
pp. jnumed.120.256396
Author(s):  
Jaewon Yang ◽  
Luyao Shi ◽  
Rui Wang ◽  
Edward J. Miller ◽  
Albert J. Sinusas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attenuation Correction ◽  
Feasibility Study ◽  
Cardiac Spect

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

2020 ◽  
pp. jnumed.120.245548
Author(s):  
Tobias Ryden ◽  
Martijn van Essen ◽  
Ida Marin ◽  
Johanna Svensson ◽  
Peter Bernhardt
Keyword(s):  
Deep Learning ◽  
Synthetic Intermediate ◽  
Spect Images

Hepatic cysts detected on Tc-99m sestamibi gated cardiac SPECT images

2008 ◽  
Vol 15 (4) ◽  
pp. e23-e23
Author(s):  
R VENKATARAMAN ◽  
J HEO ◽  
A ISKANDRIAN
Keyword(s):  
Cardiac Spect ◽  
Hepatic Cysts ◽  
Spect Images

scholarly journals Filters in 2D and 3D Cardiac SPECT Image Processing

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Maria Lyra ◽  
Agapi Ploussi ◽  
Maritina Rouchota ◽  
Stella Synefia
Keyword(s):  
Single Photon ◽  
Photon Emission ◽  
Image Resolution ◽  
Cardiac Spect ◽  
Analytical Technique ◽  
Spect Image ◽  
Reconstruction Methods ◽  
Nuclear Cardiac Imaging ◽  
And Function ◽  
Spect Images

Nuclear cardiac imaging is a noninvasive, sensitive method providing information on cardiac structure and physiology. Single photon emission tomography (SPECT) evaluates myocardial perfusion, viability, and function and is widely used in clinical routine. The quality of the tomographic image is a key for accurate diagnosis. Image filtering, a mathematical processing, compensates for loss of detail in an image while reducing image noise, and it can improve the image resolution and limit the degradation of the image. SPECT images are then reconstructed, either by filter back projection (FBP) analytical technique or iteratively, by algebraic methods. The aim of this study is to review filters in cardiac 2D, 3D, and 4D SPECT applications and how these affect the image quality mirroring the diagnostic accuracy of SPECT images. Several filters, including the Hanning, Butterworth, and Parzen filters, were evaluated in combination with the two reconstruction methods as well as with a specified MatLab program. Results showed that for both 3D and 4D cardiac SPECT the Butterworth filter, for different critical frequencies and orders, produced the best results. Between the two reconstruction methods, the iterative one might be more appropriate for cardiac SPECT, since it improves lesion detectability due to the significant improvement of image contrast.

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