High Performance Savu Software for Fast 3D Model-Based Iterative Reconstruction of Large Data at Diamond Light Source

2021 ◽  
Author(s):  
Daniil Kazantsev ◽  
Nicola Wadeson ◽  
Mark Basham
Keyword(s):  
Light Source ◽  
Iterative Reconstruction ◽  
High Performance ◽  
3D Model ◽  
Large Data ◽  
Model Based

scholarly journals Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

2021 ◽  
Author(s):  
Luuk J. Oostveen ◽  
Frederick J. A. Meijer ◽  
Frank de Lange ◽  
Ewoud J. Smit ◽  
Sjoert A. Pegge ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Quality Characteristics ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Contrast Ct ◽  
Lower Noise ◽  
Reconstruction Time

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

scholarly journals Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data

2019 ◽  
Vol 46 (12) ◽  
Author(s):  
Viktor Haase ◽  
Katharina Hahn ◽  
Harald Schöndube ◽  
Karl Stierstorfer ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Model Based ◽  
Ct Data

Model-Based Iterative Reconstruction Versus Adaptive Statistical Iterative Reconstruction in Low-Dose Abdominal CT for Urolithiasis

2014 ◽  
Vol 203 (2) ◽  
pp. 336-340 ◽  
Author(s):  
Diomidis Botsikas ◽  
Salvatore Stefanelli ◽  
Sana Boudabbous ◽  
Seema Toso ◽  
Christoph D. Becker ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Low Dose ◽  
Adaptive Statistical Iterative Reconstruction ◽  
Abdominal Ct ◽  
Model Based ◽  
Statistical Iterative Reconstruction

High performance parallel-DSP computing in model-based spectral estimation

1999 ◽  
Vol 23 (6) ◽  
pp. 337-344 ◽  
Author(s):  
J.S. González ◽  
D.F. Garcı́a Nocetti ◽  
M.G. Ruano
Keyword(s):  
High Performance ◽  
Spectral Estimation ◽  
Model Based

scholarly journals Core-Level Modeling and Frequency Prediction for DSP Applications on FPGAs

2015 ◽  
Vol 2015 ◽  
pp. 1-20
Author(s):  
Gongyu Wang ◽  
Greg Stitt ◽  
Herman Lam ◽  
Alan George
Keyword(s):  
High Performance ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Core Level ◽  
Prediction Methods ◽  
Clock Frequency ◽  
Worst Case ◽  
Model Based ◽  
Dsp Applications

Field-programmable gate arrays (FPGAs) provide a promising technology that can improve performance of many high-performance computing and embedded applications. However, unlike software design tools, the relatively immature state of FPGA tools significantly limits productivity and consequently prevents widespread adoption of the technology. For example, the lengthy design-translate-execute (DTE) process often must be iterated to meet the application requirements. Previous works have enabled model-based, design-space exploration to reduce DTE iterations but are limited by a lack of accurate model-based prediction of key design parameters, the most important of which is clock frequency. In this paper, we present a core-level modeling and design (CMD) methodology that enables modeling of FPGA applications at an abstract level and yet produces accurate predictions of parameters such as clock frequency, resource utilization (i.e., area), and latency. We evaluate CMD’s prediction methods using several high-performance DSP applications on various families of FPGAs and show an average clock-frequency prediction error of 3.6%, with a worst-case error of 20.4%, compared to the best of existing high-level prediction methods, 13.9% average error with 48.2% worst-case error. We also demonstrate how such prediction enables accurate design-space exploration without coding in a hardware-description language (HDL), significantly reducing the total design time.

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