scholarly journals Optimization of Spatial Resolution and Image Reconstruction Parameters for the Small-Animal MetisTM PET/CT System

2022 ◽  
Author(s):  
Jie Zhao ◽  
Qiong Liu ◽  
Chaofan Li ◽  
Yunfeng Song ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Spatial Resolution ◽  
Small Animal ◽  
Reconstruction Algorithm ◽  
Visual Assessment ◽  
Pet Ct ◽  
Noise Ratio ◽  
Ct System ◽  
Post Filter

Abstract The aim of this study was to investigate the optimization of spatial resolution and image reconstruction parameters related to image quality in an iterative reconstruction algorithm for the small-animal MetisTM PET/CT system. We used a homemade Derenzo phantom to evaluate the image quality by visual assessment, signal-to-noise ratio, contrast, coefficient of variation, and contrast-to-noise ratio of the 0.8 mm hot rods of 8 slices in the centre of the phantom PET images. A healthy mouse study was performed to analyze the influence of optimal reconstruction parameters and Gaussian post-filter FWHM. In the phantom study, the best image quality was obtained by placing the phantom at one end, keeping the central axis parallel to X-axis of the system, selecting iterations between 30 and 40, with a reconstruction voxel of 0.314 mm and a Gaussian post-filter FWHM of 1.57 mm. The optimization of spatial resolution can reach 0.6-mm. In the animal study, it was suitable to choose a voxel size of 0.472-mm, iterations between 30 and 40, and 2.36-mm Gaussian post-filter FWHM. Our results indicate that optimal imaging conditions and reconstruction parameters are necessary to obtain high-resolution images and quantitative accuracy, especially for the high-precision identification of tiny lesions.

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis

2021 ◽  
pp. 028418512110358
Author(s):  
Aurélien Delabie ◽  
Roger Bouzerar ◽  
Raphaël Pichois ◽  
Xavier Desdoit ◽  
Jérémie Vial ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
Intraclass Correlation ◽  
Reconstruction Algorithm ◽  
Urinary Stones ◽  
Abdominopelvic Ct ◽  
Noise Ratio

Background Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction. Purpose To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT. Material and Methods A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers. Results Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (<3 mm), results were different (ICC = 0.77 vs. 0.86). Conclusion For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

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.

Optimization of PET/CT image quality using the GE ‘Sharp IR’ point-spread function reconstruction algorithm

2017 ◽  
Vol 38 (6) ◽  
pp. 471-479 ◽  
Author(s):  
Nicholas J. Vennart ◽  
Nicholas Bird ◽  
John Buscombe ◽  
Heok K. Cheow ◽  
Ewa Nowosinska ◽  
...  
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Reconstruction Algorithm ◽  
Ct Image ◽  
Point Spread ◽  
Pet Ct ◽  
Spread Function ◽  
Ct Image Quality ◽  
Function Reconstruction

An Automatic System for Analyzing Phantom Images to Determine the Reliability of PET/SPECT Cameras

2015 ◽  
Author(s):  
Miri Weiss Cohen ◽  
John A. Kennedy ◽  
Archil Pirmisashvili ◽  
Gleb Orlikov
Keyword(s):  
Quality Control ◽  
Image Quality ◽  
Spatial Resolution ◽  
Automatic System ◽  
Quality Parameters ◽  
Tomographic Image ◽  
Performance Parameters ◽  
Pet Ct ◽  
Ct Scanners

This paper describes an automatic system for analyzing phantom images from two types of PET/CT scanners. The system was developed for the purpose of obtaining tomographic image quality parameters, which determine a number of different performance parameters, primarily scanner sensitivity, tomographic uniformity, contrast and spatial resolution. The system provides a method for generating and altering image masks used for the analysis of PET images, which are then automatically aligned with the PET data. The system automatically generates Quality Control (QC) reports and is currently being used at clinical PET/CT center.

scholarly journals Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Michael Messerli ◽  
Paul Stolzmann ◽  
Michèle Egger-Sigg ◽  
Josephine Trinckauf ◽  
Stefano D’Aguanno ◽  
...  
Keyword(s):  
Image Quality ◽  
Penalized Likelihood ◽  
Reconstruction Algorithm ◽  
Lung Tumors ◽  
Clinical Implications ◽  
Pet Ct ◽  
Digital Pet

Image quality phantom and parameters for high spatial resolution small-animal SPECT

2011 ◽  
Vol 654 (1) ◽  
pp. 539-545 ◽  
Author(s):  
Eric P. Visser ◽  
Anita A. Harteveld ◽  
Antoi P.W. Meeuwis ◽  
Jonathan A. Disselhorst ◽  
Freek J. Beekman ◽  
...  
Keyword(s):  
Image Quality ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Small Animal

scholarly journals Assessment of Image Quality and Lesion Detectability With Digital PET/CT System

2021 ◽  
Vol 8 ◽  
Author(s):  
Olivier Delcroix ◽  
David Bourhis ◽  
Nathalie Keromnes ◽  
Philippe Robin ◽  
Pierre-Yves Le Roux ◽  
...  
Keyword(s):  
Image Quality ◽  
Lesion Detection ◽  
Reference Dataset ◽  
Detection Capability ◽  
Lesion Detectability ◽  
Siemens Biograph ◽  
Pet Ct ◽  
Digital Pet ◽  
Ct System ◽  
Bed Position

Purpose: The aim of this study was to assess image quality and lesion detectability acquired with a digital Positron Emission Tomography/Computed Tomography (PET/CT) Siemens Biograph Vision 600 system.Material and Methods: Consecutive patients who underwent a FDG PET/CT during the first week of use of a digital PET/CT (Siemens Biograph Vision 600) at the nuclear medicine department of the university hospital of Brest were analyzed. PET were realized using list mode acquisition. For all patients, 4 datasets were reconstructed. We determined, according to phantom measurements, an equivalent time acquisition/reconstruction parameters pair of the digital PET/CT corresponding to an analog PET/CT image quality (“analog-like”) as reference dataset. We compared the reference dataset with 3 others digital PET/CT reconstruction parameters, allowing a decrease of emission duration: 60, 90, and 120 s per bed position. Three nuclear medicine physicians evaluated independently, for each dataset, overall image quality [Maximal Intensity Projection (MIP), noise, sharpness] using a 4-point scale. Physicians assessed also lesion detection capability by reporting new visible lesions on each digital datasets with their confidence level in comparison with analog-like dataset.Results: Ninety-eight patients were analyzed. Image quality of MIP (IQMIP), sharpness (IQSHARPNESS), and noise (IQNOISE) of all digital datasets (60, 90, and 120 s) were better than those evaluated with analog-like reconstruction. Moreover, digital PET/CT system improved IQMIP, IQNOISE, and IQSHARPNESS whatever the BMI. Lesion detection capability and confidence level were higher for 60, 90, 120 s per bed position, respectively, than for analog-like images.Conclusion: Our study demonstrated an improvement of image quality and lesion detectability with a digital PET/CT system.

scholarly journals Evaluation of 68Ga-PSMA PET/CT Images Acquired with a Reduced Scan Time Duration in Prostate Cancer Patients using the Digital Biograph Vision

2020 ◽  
Author(s):  
Manuel Weber ◽  
Regina Hofferber ◽  
Ken Herrmann ◽  
Wolfgang Peter Fendler ◽  
Maurizio Conti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Reconstruction ◽  
Diagnostic Performance ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Time Duration ◽  
Acquisition Protocol ◽  
Scan Time ◽  
Psma Pet ◽  
Pet Ct

Abstract Aim 68Ga-PSMA PET/CT allows for a superior detection of prostate cancer (PC) tissue, especially in context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration / administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand 68Ga-PSMA PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance. Methods Twenty PC patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent 68Ga-PSMA PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a scan time duration of approximately 17 min (reference acquisition protocol) and 5 min (reduced acquisition protocol). 4 iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak) and (c) image noise using the liver’s activity distribution. Results Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤ 4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13–21% (4 iterations) and from 10–15% (2 iterations) for PSF + TOF and TOF images. Conclusions High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98% of regions) and image quantification (mean deviation ≤ 10%) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5% of patients. Our results suggest that a reduction of scan time duration or administered 68Ga-PSMA activities can be considered in metastatic patients, where missing small lesions would not impact patient management.

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