Effect of Scan Time on Neuro 18F-Fluorodeoxyglucose Positron Emission Tomography Image Generated Using Deep Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 681-687
Author(s):  
Jaewon Kim ◽  
Sungsik Kang ◽  
Konsu Lee ◽  
Jin Ho Jung ◽  
Garam Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
Signal To Noise Ratio ◽  
Ground Truth ◽  
High Signal ◽  
Ct Scanner ◽  
Signal To Noise ◽  
Scan Time ◽  
Mean Square Errors

The purpose of this study was to generate the PET images with high signal-to-noise ratio (SNR) acquired for typical scan durations (H-PET) from short scan time PET images with low SNR (L-PET) using deep learning and to evaluate the effect of scan time on the quality of predicted PET image. A convolutional neural network (CNN) with a concatenated connection and residual learning framework was implemented. PET data from 27 patients were acquired for 900 s, starting 60 minutes after the intravenous administration of FDG using a commercial PET/CT scanner. To investigate the effect of scan time on the quality of the predicted H-PETs, 10 s, 30 s, 60 s, and 120 s PET data were generated by sorting the 900 s LMF data into the LMF data acquired for each scan time. Twenty-three of the 27 patient images were used for training of the proposed CNN and the remaining four patient images were used for test of the CNN. The predicted H-PETs generated by the CNN were compared to ground-truth H-PETs, L-PETs, and filtered L-PETs processed with four commonly used denoising algorithms. The peak signal-to-noise ratios (PSNRs), normalized root mean square errors (NRMSEs), and average regionof- interest (ROI) differences as a function of scan time were calculated. The quality of the predicted H-PETs generated by the CNN was superior to that of the L-PETs and filtered L-PETs. Lower NRMSEs and higher PSNRs were also obtained from predicted H-PETs compared to the L-PETs and filtered L-PETs. ROI differences in the predicted H-PETs were smaller than those of the L-PETs. The quality of the predicted H-PETs gradually improved with increasing scan times. The lowest NRMSEs, highest PSNRs, and smallest ROI differences were obtained using the predicted H-PETs for 120 s. Various performance test results for the proposed CNN indicate that it is possible to generate H-PETs from neuro FDG L-PETs using the proposed CNN method, which might allow reductions in both scan time and injection dose.

scholarly journals High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

2020 ◽  
Vol 25 (12) ◽  
Author(s):  
Qiangjiang Hao ◽  
Kang Zhou ◽  
Jianlong Yang ◽  
Yan Hu ◽  
Zhengjie Chai ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Optical Coherence ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Estimating the number of components in an OSL decay curve using the Bayesian Information Criterion

2014 ◽  
Vol 41 (4) ◽  
pp. 334-341 ◽  
Author(s):  
Jun Peng ◽  
Zhibao Dong ◽  
Fengqing Han ◽  
Yuanhong Han ◽  
Xueling Dai
Keyword(s):  
Bayesian Information Criterion ◽  
Decay Curve ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Optimal Number ◽  
High Signal ◽  
Signal To Noise ◽  
Number Of Components ◽  
Correct Estimation

Abstract The optically stimulated luminescence (OSL) decay curve is assumed to consist of a number of first-order exponential components. Improper estimation of the number of components leads to under-or over-fitting of the curve under consideration. Hence, correct estimation of the number of components is important to accurately analyze an OSL decay curve. In this study, we investigated the possibility of using the Bayesian Information Criterion to estimate the optimal number of components in an OSL decay curve. We tested the reliability of this method using several hundred measured decay curves and three simulation scenarios. Our results demonstrate that the quality of the identification can be influenced by several factors: the measurement time and the number of channels; the variability of the decay constants; and the signal-to-noise ratios of a decaying component. The results also suggest that the Bayesian Information Criterion has great potential to estimate the number of components in an OSL decay curve with a moderate to high signal-to-noise ratio.

scholarly journals On the objectivity, reliability, and validity of deep learning enabled bioimage analyses

2018 ◽  
Author(s):  
Dennis Segebarth ◽  
Matthias Griebel ◽  
Nikolai Stein ◽  
Cora R. von Collenberg ◽  
Corinna Martin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Life Sciences ◽  
Reliability And Validity ◽  
Ground Truth ◽  
Fluorescent Labeling ◽  
Signal To Noise ◽  
Data Annotation ◽  
Analysis Process ◽  
Model Training

AbstractFluorescent labeling of biomolecules is widely used for bioimage analyses throughout the life sciences. Recent advances in deep learning (DL) have opened new possibilities to scale the image analysis processes through automation. However, the annotation of fluorescent features with a low signal-to-noise ratio is frequently based on subjective criteria. Training on subjective annotations may ultimately lead to biased DL models yielding irreproducible results. An end-to-end analysis process that integrates data annotation, ground truth estimation, and model training can mitigate this risk. To highlight the importance of this integrated process, we compare different DL-based analysis approaches. Based on data from different laboratories, we show that ground truth estimation from multiple human annotators is indispensable to establish objectivity in fluorescent feature annotations. We demonstrate that ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible and transparent bioimage analyses using DL methods.

A compact guided-wave EMAT with pulsed electromagnet for ferromagnetic tube inspection

2020 ◽  
Vol 64 (1-4) ◽  
pp. 951-958
Author(s):  
Tianhao Liu ◽  
Yu Jin ◽  
Cuixiang Pei ◽  
Jie Han ◽  
Zhenmao Chen
Keyword(s):  
Power Plants ◽  
Signal To Noise Ratio ◽  
Small Diameter ◽  
Performance Testing ◽  
Guided Wave ◽  
High Signal ◽  
Receiver Coil ◽  
Signal To Noise ◽  
Corrosion Defects

Small-diameter tubes that are widely used in petroleum industries and power plants experience corrosion during long-term services. In this paper, a compact inserted guided-wave EMAT with a pulsed electromagnet is proposed for small-diameter tube inspection. The proposed transducer is noncontact, compact with high signal-to-noise ratio and unattractive to ferromagnetic tubes. The proposed EMAT is designed with coils-only configuration, which consists of a pulsed electromagnet and a meander pulser/receiver coil. Both the numerical simulation and experimental results validate its feasibility on generating and receiving L(0,2) mode guided wave. The parameters for driving the proposed EMAT are optimized by performance testing. Finally, feasibility on quantification evaluation for corrosion defects was verified by experiments.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Giganti ◽  
Alex Kirkham ◽  
Veeru Kasivisvanathan ◽  
Marianthi-Vasiliki Papoutsaki ◽  
Shonit Punwani ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Diagnostic Quality ◽  
Prostate Mri ◽  
Prostate Imaging ◽  
Dynamic Contrast Enhanced ◽  
Diffusion Weighted ◽  
Contrast Enhanced

AbstractProstate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.

Narrow-frequency sharp-angular filters using all-dielectric cascaded meta-gratings

Nanophotonics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 3443-3450 ◽  
Author(s):  
Wei-Nan Liu ◽  
Rui Chen ◽  
Wei-Yi Shi ◽  
Ke-Bo Zeng ◽  
Fu-Li Zhao ◽  
...  
Keyword(s):  
Thin Film ◽  
Signal To Noise Ratio ◽  
Incident Angle ◽  
Design Strategy ◽  
Transmission Peak ◽  
High Signal ◽  
Waveguide Array ◽  
Signal To Noise ◽  
Center Wavelength ◽  
Noise Ratio

AbstractSelective transmission or filtering always responds to either frequency or incident angle, so as hardly to maximize signal-to-noise ratio in communication, detection and sensing. Here, we propose compact meta-filters of narrow-frequency sharp-angular transmission peak along with broad omnidirectional reflection sidebands, in all-dielectric cascaded subwavelength meta-gratings. The inherent collective resonance of waveguide-array modes and thin film approximation of meta-grating are employed as the design strategy. A unity transmission peak, locating at the incident angle of 44.4° and the center wavelength of 1550 nm, is demonstrated in a silicon meta-filter consisting of two-layer silicon rectangular meta-grating. These findings provide possibilities in cascaded meta-gratings spectroscopic design and alternative utilities for high signal-to-noise ratio applications in focus-free spatial filtering and anti-noise systems in telecommunications.

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