Towards standardization of absolute SPECT/CT quantification: a multi-center and multi-vendor phantom study

Abstract Absolute quantification of radiotracer distribution using SPECT/CT imaging is of great importance for dosimetry aimed at personalized radionuclide precision treatment. However, its accuracy depends on many factors. Using phantom measurements, this multi-vendor and multi-center study evaluates the quantitative accuracy and inter-system variability of various SPECT/CT systems as well as the effect of patient size, processing software and reconstruction algorithms on recovery coefficients (RC). Methods Five SPECT/CT systems were included: Discovery™ NM/CT 670 Pro (GE Healthcare), Precedence™ 6 (Philips Healthcare), Symbia Intevo™, and Symbia™ T16 (twice) (Siemens Healthineers). Three phantoms were used based on the NEMA IEC body phantom without lung insert simulating body mass indexes (BMI) of 25, 28, and 47 kg/m2. Six spheres (0.5–26.5 mL) and background were filled with 0.1 and 0.01 MBq/mL 99mTc-pertechnetate, respectively. Volumes of interest (VOI) of spheres were obtained by a region growing technique using a 50% threshold of the maximum voxel value corrected for background activity. RC, defined as imaged activity concentration divided by actual activity concentration, were determined for maximum (RCmax) and mean voxel value (RCmean) in the VOI for each sphere diameter. Inter-system variability was expressed as median absolute deviation (MAD) of RC. Acquisition settings were standardized. Images were reconstructed using vendor-specific 3D iterative reconstruction algorithms with institute-specific settings used in clinical practice and processed using a standardized, in-house developed processing tool based on the SimpleITK framework. Additionally, all data were reconstructed with a vendor-neutral reconstruction algorithm (Hybrid Recon™; Hermes Medical Solutions). Results RC decreased with decreasing sphere diameter for each system. Inter-system variability (MAD) was 16 and 17% for RCmean and RCmax, respectively. Standardized reconstruction decreased this variability to 4 and 5%. High BMI hampers quantification of small lesions (< 10 ml). Conclusion Absolute SPECT quantification in a multi-center and multi-vendor setting is feasible, especially when reconstruction protocols are standardized, paving the way for a standard for absolute quantitative SPECT.

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Comparison between silicon photomultiplier-based and conventional PET/CT in patients with suspected lung cancer—a pilot study

EJNMMI Research ◽

10.1186/s13550-019-0504-y ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Johan Economou Lundeberg ◽

Jenny Oddstig ◽

Ulrika Bitzén ◽

Elin Trägårdh

Keyword(s):

Lung Cancer ◽

Expectation Maximization ◽

Image Interpretation ◽

Imaging Study ◽

Reconstruction Algorithm ◽

Tnm Staging ◽

Reconstruction Algorithms ◽

Silicon Photomultiplier ◽

Standardized Uptake Values ◽

Pet Ct

Abstract Background Lung cancer is one of the most common cancers in the world. Early detection and correct staging are fundamental for treatment and prognosis. Positron emission tomography with computed tomography (PET/CT) is recommended clinically. Silicon (Si) photomultiplier (PM)-based PET technology and new reconstruction algorithms are hoped to increase the detection of small lesions and enable earlier detection of pathologies including metastatic spread. The aim of this study was to compare the diagnostic performance of a SiPM-based PET/CT (including a new block-sequential regularization expectation maximization (BSREM) reconstruction algorithm) with a conventional PM-based PET/CT including a conventional ordered subset expectation maximization (OSEM) reconstruction algorithm. The focus was patients admitted for 18F-fluorodeoxyglucose (FDG) PET/CT for initial diagnosis and staging of suspected lung cancer. Patients were scanned on both a SiPM-based PET/CT (Discovery MI; GE Healthcare, Milwaukee, MI, USA) and a PM-based PET/CT (Discovery 690; GE Healthcare, Milwaukee, MI, USA). Standardized uptake values (SUV) and image interpretation were compared between the two systems. Image interpretations were further compared with histopathology when available. Results Seventeen patients referred for suspected lung cancer were included in our single injection, dual imaging study. No statically significant differences in SUVmax of suspected malignant primary tumours were found between the two PET/CT systems. SUVmax in suspected malignant intrathoracic lymph nodes was 10% higher on the SiPM-based system (p = 0.026). Good consistency (14/17 cases) between the PET/CT systems were found when comparing simplified TNM staging. The available histology results did not find any obvious differences between the systems. Conclusion In a clinical setting, the new SiPM-based PET/CT system with a new BSREM reconstruction algorithm provided a higher SUVmax for suspected lymph node metastases compared to the PM-based system. However, no improvement in lung cancer detection was seen.

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Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence Envelope Detection

Micromachines ◽

10.3390/mi12020164 ◽

2021 ◽

Vol 12 (2) ◽

pp. 164

Author(s):

Dongxu Wu ◽

Fusheng Liang ◽

Chengwei Kang ◽

Fengzhou Fang

Keyword(s):

Surface Reconstruction ◽

Measurement Accuracy ◽

Experimental Studies ◽

Reconstruction Algorithm ◽

Low Noise ◽

Surface Measurement ◽

Reconstruction Algorithms ◽

Centroid Method ◽

Envelope Detection ◽

Vertical Scanning Interferometry

Optical interferometry plays an important role in the topographical surface measurement and characterization in precision/ultra-precision manufacturing. An appropriate surface reconstruction algorithm is essential in obtaining accurate topography information from the digitized interferograms. However, the performance of a surface reconstruction algorithm in interferometric measurements is influenced by environmental disturbances and system noise. This paper presents a comparative analysis of three algorithms commonly used for coherence envelope detection in vertical scanning interferometry, including the centroid method, fast Fourier transform (FFT), and Hilbert transform (HT). Numerical analysis and experimental studies were carried out to evaluate the performance of different envelope detection algorithms in terms of measurement accuracy, speed, and noise resistance. Step height standards were measured using a developed interferometer and the step profiles were reconstructed by different algorithms. The results show that the centroid method has a higher measurement speed than the FFT and HT methods, but it can only provide acceptable measurement accuracy at a low noise level. The FFT and HT methods outperform the centroid method in terms of noise immunity and measurement accuracy. Even if the FFT and HT methods provide similar measurement accuracy, the HT method has a superior measurement speed compared to the FFT method.

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Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT

EJNMMI Physics ◽

10.1186/s40658-021-00405-3 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Dennis Kupitz ◽

Heiko Wissel ◽

Jan Wuestemann ◽

Stephanie Bluemel ◽

Maciej Pech ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Quantitative Imaging ◽

Scatter Correction ◽

Reconstruction Algorithm ◽

Weighting Factor ◽

Quantitative Spect ◽

Pet Ct ◽

Spect Acquisition ◽

The Mean ◽

A Minor

Abstract Background The introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol. Results The reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by − 13.9 kBq/ml (− 16.3%) and − 7.1 kBq/ml (− 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = − 14.6 kBq/ml (− 17.1%), 240 iterations (24i/10s) = − 8.0 kBq/ml (− 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001). Conclusion Quantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

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Assessment of cardiac amyloidosis with 99mTc-pyrophosphate (PYP) quantitative SPECT

EJNMMI Physics ◽

10.1186/s40658-020-00342-7 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Chao Ren ◽

Jingyun Ren ◽

Zhuang Tian ◽

Yanrong Du ◽

Zhixin Hao ◽

...

Keyword(s):

Cardiac Amyloidosis ◽

Activity Concentration ◽

Standardized Uptake Value ◽

Blood Pool ◽

Systematic Difference ◽

Repeated Measurements ◽

Quantitative Spect ◽

Phantom Studies ◽

Grade 3 ◽

Operator Reproducibility

Abstract Background 99mTc-PYP scintigraphy provides differential diagnosis of ATTR cardiomyopathy (ATTR-CM) from light chain cardiac amyloidosis and other myocardial disorders without biopsy. This study was aimed to assess the diagnostic feasibility and the operator reproducibility of 99mTc-PYP quantitative SPECT. Method Thirty-seven consecutive patients who underwent a 99mTc-PYP thorax planar scan followed by SPECT and CT scans to diagnose suspected ATTR-CM were enrolled. For the quantitative SPECT, phantom studies were initially performed to determine the image conversion factor (ICF) and partial volume correction (PVC) factor to recover 99mTc-PYP activity concentration in the myocardium for calculating the standardized uptake value (SUV) (unit: g/ml). SUVmax was compared among groups of ATTR-CM, AL cardiac amyloidosis, and other pathogens (others) and among categories of Perugini visual scores (grades 0–3). The intra- and inter-operator reproducibility of quantitative SPECT was verified, and the corresponded repeatability coefficient (RPC) was calculated. Results The ICF was 79,327 Bq/ml to convert count rate in pixel to 99mTc activity concentration. PVC factor as a function of the measured activity concentration ratio in the myocardium and blood-pool was [y = 1.424 × (1 − exp(− 0.759 × x)) + 0.104]. SUVmax of ATTR-CM (7.50 ± 2.68) was significantly higher than those of AL (1.96 ± 0.35) and others (2.00 ± 0.74) (all p < 0.05). SUVmax of grade 3 (8.95 ± 1.89) and grade 2 (4.71 ± 0.23) were also significantly higher than those of grade 1 (1.92 ± 0.31) and grade 0 (1.59 ± 0.39) (all p < 0.05). Correlation coefficient (R2) of SUVmax reached 0.966 to 0.978 with only small systematic difference (intra = − 0.14; inter = − 0.23) between two repeated measurements. Intra- and inter-operator RPCs were 0.688 and 0.877. Conclusions 99mTc-PYP quantitative SPECT integrated with adjustable PVC factors is feasible to quantitatively and objectively assess the burden of cardiac amyloidosis for diagnosis of ATTR-CM.

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Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

Scientific Reports ◽

10.1038/s41598-021-95962-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Robert Peter Reimer ◽

Konstantin Klein ◽

Miriam Rinneburger ◽

David Zopfs ◽

Simon Lennartz ◽

...

Keyword(s):

Computed Tomography ◽

Treatment Decision ◽

Reconstruction Algorithm ◽

Slice Thickness ◽

Reconstruction Algorithms ◽

Stone Size ◽

Bone Window ◽

3D Printed ◽

Measurement Strategies ◽

Digital Caliper

AbstractComputed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDIvol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p < 0.05), yet closely correlated (r = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements (p > 0.05), while image reformatations and window settings did (p < 0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05, r = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.

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Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

BioMed Research International ◽

10.1155/2017/6753831 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10

Author(s):

Hsuan-Ming Huang ◽

Ing-Tsung Hsiao

Keyword(s):

Computed Tomography ◽

Image Reconstruction ◽

Low Dose ◽

Computational Cost ◽

Reconstruction Algorithm ◽

Reconstruction Algorithms ◽

Acceleration Techniques ◽

Reconstruction Methods ◽

Speed Up ◽

Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

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Distortion reconstruction in S-ducts from wall static pressure measurements

International Journal of Numerical Methods for Heat &amp Fluid Flow ◽

10.1108/hff-06-2017-0232 ◽

2018 ◽

Vol 28 (5) ◽

pp. 1134-1155 ◽

Cited By ~ 1

Author(s):

Pierre Grenson ◽

Eric Garnier

Keyword(s):

Static Pressure ◽

Active Flow Control ◽

Hybrid Approach ◽

Reconstruction Algorithm ◽

Stochastic Estimation ◽

Pressure Measurements ◽

Reconstruction Algorithms ◽

Detached Eddy Simulation ◽

Flow Distortion ◽

Content Type

Purpose This paper aims to report the attempts for predicting “on-the-fly” flow distortion in the engine entrance plane of a highly curved S-duct from wall static pressure measurements. Such a technology would be indispensable to trigger active flow control devices to mitigate the intense flow separations which occur in specific flight conditions. Design/methodology/approach Evaluation of different reconstruction algorithms is performed on the basis of data extracted from a Zonal Detached Eddy Simulation (ZDES) of a well-documented S-Duct (Garnier et al., AIAA J., 2015). Contrary to RANS methods, such a hybrid approach makes unsteady distortions available, which are necessary information for reconstruction algorithm assessment. Findings The best reconstruction accuracy is obtained with the artificial neural network (ANN) but the improvement compared to the classical linear stochastic estimation (LSE) is minor. The different inlet distortion coefficients are not reconstructed with the same accuracy. KA2 coefficient is finally identified as the more suited for activation of the control device. Originality/value LSE and its second-order variant (quadratic stochastic estimation [QSE]) are applied for reconstructing instantaneous stagnation pressure in the flow field. The potential improvement of an algorithm based on an ANN is also evaluated. The statistical link between the wall sensors and 40-Kulite rake sensors are carefully discussed and the accuracy of the reconstruction of the most used distortion coefficients (DC60, RDI, CDI and KA2) is quantified for each estimation technique.

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Source reconstruction for bioluminescence tomography via L1∕2 regularization

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545817500146 ◽

2018 ◽

Vol 11 (02) ◽

pp. 1750014 ◽

Cited By ~ 1

Author(s):

Jingjing Yu ◽

Qiyue Li ◽

Haiyu Wang

Keyword(s):

Image Quality ◽

Matching Pursuit ◽

Imaging Modality ◽

Reconstruction Algorithm ◽

Reconstruction Method ◽

Reconstruction Algorithms ◽

Preclinical Research ◽

Bioluminescence Tomography ◽

Comparison Results ◽

Stable Reconstruction

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L[Formula: see text] regularization to enhance sparsity of BLT solution and solve the nonconvex L[Formula: see text] norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

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Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

Journal of X-Ray Science and Technology ◽

10.3233/xst-211069 ◽

2022 ◽

pp. 1-13

Author(s):

Lei Shi ◽

Gangrong Qu ◽

Yunsong Zhao

Keyword(s):

Image Reconstruction ◽

Condition Number ◽

Reconstruction Algorithm ◽

Projection Data ◽

Ct Image ◽

Reconstruction Algorithms ◽

Edge Preserving ◽

Image Reconstruction Algorithm ◽

Ct Image Reconstruction ◽

Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

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A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery

Algorithms ◽

10.3390/a12070126 ◽

2019 ◽

Vol 12 (7) ◽

pp. 126 ◽

Cited By ~ 1

Author(s):

Bin Wang ◽

Li Wang ◽

Hao Yu ◽

Fengming Xin

Keyword(s):

Compressed Sensing ◽

Reconstruction Algorithm ◽

Steepest Descent Method ◽

Signal Recovery ◽

Two Dimensional ◽

Reconstruction Algorithms ◽

Experimental Conditions ◽

One Dimensional ◽

Recovery Algorithm ◽

Reconstruction Performance

The compressed sensing theory has been widely used in solving undetermined equations in various fields and has made remarkable achievements. The regularized smooth L0 (ReSL0) reconstruction algorithm adds an error regularization term to the smooth L0(SL0) algorithm, achieving the reconstruction of the signal well in the presence of noise. However, the ReSL0 reconstruction algorithm still has some flaws. It still chooses the original optimization method of SL0 and the Gauss approximation function, but this method has the problem of a sawtooth effect in the later optimization stage, and the convergence effect is not ideal. Therefore, we make two adjustments to the basis of the ReSL0 reconstruction algorithm: firstly, we introduce another CIPF function which has a better approximation effect than Gauss function; secondly, we combine the steepest descent method and Newton method in terms of the algorithm optimization. Then, a novel regularized recovery algorithm named combined regularized smooth L0 (CReSL0) is proposed. Under the same experimental conditions, the CReSL0 algorithm is compared with other popular reconstruction algorithms. Overall, the CReSL0 algorithm achieves excellent reconstruction performance in terms of the peak signal-to-noise ratio (PSNR) and run-time for both a one-dimensional Gauss signal and two-dimensional image reconstruction tasks.

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