Quantitative imaging of bone remodeling in patients with a unicompartmental joint unloading knee implant (ATLAS Knee System)—effect of metal artifacts on a SPECT-CT-based quantification

Abstract Background SPECT-CT using radiolabeled phosphonates is considered a standard for assessing bone metabolism (e.g., in patients with osteoarthritis of knee joints). However, SPECT can be influenced by metal artifacts in CT caused by endoprostheses affecting attenuation correction. The current study examined the effects of metal artifacts in CT of a specific endoprosthesis design on quantitative hybrid SPECT-CT imaging. The implant was positioned inside a phantom homogenously filled with activity (955 MBq 99mTc). CT imaging was performed for different X-ray tube currents (I = 10, 40, 125 mA) and table pitches (p = 0.562 and 1.375). X-ray tube voltage (U = 120 kVp) and primary collimation (16 × 0.625 mm) were kept constant for all scans. The CT reconstruction was performed with five different reconstruction kernels (slice thickness, 1.25 mm and 3.75 mm, each 512 × 512 matrix). Effects from metal artifacts were analyzed for different CT scans and reconstruction protocols. ROI analysis of CT and SPECT data was performed for two slice positions/volumes representing the typical locations for target structures relative to the prosthesis (e.g., femur and tibia). A reference region (homogenous activity concentration without influence from metal artifacts) was analyzed for comparison. Results Significant effects caused by CT metal artifacts on attenuation-corrected SPECT were observed for the different slice positions, reconstructed slice thicknesses of CT data, and pitch and CT-reconstruction kernels used (all, p < 0.0001). Based on the optimization, a set of three protocols was identified minimizing the effect of CT metal artifacts on SPECT data. Regarding the reference region, the activity concentration in the anatomically correlated volume was underestimated by 8.9–10.1%. A slight inhomogeneity of the reconstructed activity concentration was detected inside the regions with a median up to 0.81% (p < 0.0001). Using an X-ray tube current of 40 mA showed the best result, balancing quantification and CT exposure. Conclusion The results of this study demonstrate the need for the evaluation of SPECT-CT protocols in prosthesis imaging. Phantom experiments demonstrated the possibility for quantitative SPECT-CT of bone turnover in a specific prosthesis design. Meanwhile, a systematic bias caused by metal implants on quantitative SPECT data has to be considered.

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Non-destructive inspection of electro-mechanical systems using micro X-ray computed tomography (proposal of CT reconstruction methods for eliminating metal artifacts)

2006 8th Electronics Packaging Technology Conference ◽

10.1109/eptc.2006.342805 ◽

2006 ◽

Cited By ~ 1

Author(s):

Michihiko Koseki ◽

Shuhei Hashimoto ◽

Shinpei Sato ◽

Hitoshi Kimura ◽

Norio Inou

Keyword(s):

Computed Tomography ◽

Mechanical Systems ◽

Ct Reconstruction ◽

Metal Artifacts ◽

X Ray ◽

Reconstruction Methods ◽

X Ray Computed ◽

Non Destructive

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What validation tests can be done by the clinical medical physicist while waiting for the standardization of quantitative SPECT/CT imaging?

10.21203/rs.3.rs-118913/v1 ◽

2020 ◽

Author(s):

Hanna Piwowarska-Bilska ◽

Aleksandra Supińska ◽

Bożena Birkenfeld

Keyword(s):

Nuclear Medicine ◽

Activity Concentration ◽

Total Activity ◽

Ct Imaging ◽

Calibration Factor ◽

Recovery Coefficient ◽

Quantitative Spect ◽

Gamma Cameras ◽

Reconstruction Methods ◽

Accuracy Of Measurements

Abstract Objective The aim of the study was to assess the accuracy of quantitative SPECT/CT imaging in a clinical setting and to compare test results from two nuclear medicine departments.Methods Phantom studies were carried out with two gamma cameras manufactured by GE Healthcare: Discovery NM/CT 670 and NM/CT 850, used in two nuclear medicine departments.Results The convergence of activity concentration recovery was validated for the two gamma cameras operating in two medical centres using a homogeneous 3D phantom. The comparison of results revealed a 5% difference in the calibration factor Bg. cal; 6% difference in COV, and a 0.6% difference in total activity deviation ∆Atot.Recovery coefficients (RCmax) for activity concentration in spheres of the anthropomorphic phantom was measured for different image reconstruction techniques. RCmax was in the range of 0.2-0.4 for the smallest sphere (ϕ10 mm), and 1.3-1.4 for the largest sphere (ϕ37 mm). Conversion factors for SUVmax and SUVmean for the gamma camera systems used were 0.99 and 1.13, respectively.Conclusions 1) Measurements taken in our study confirmed the clinical suitability of 5 parameters of image quality (Bg. cal- background calibration factor, ∆Atot- total activity deviation, COV- noise level estimation, QH- hot contrast, AM-accuracy of measurements or RC- recovery coefficient) for the validation of SPECT/CT system performance in terms of correct quantitative acquisitions of images. 2) This work shows that absolute SPECT/CT quantification is achievable in clinical nuclear medicine centers. Results variation of quantitative analyzes between centers is mainly related to the use of different reconstruction methods. 3) It is necessary to standardize the technique of measuring the SUV conversion factor obtained with different SPECT/CT scanners.

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Rates and clinical impact of discordant X-ray and CT imaging in transfers to a pediatric emergency department

The American Journal of Emergency Medicine ◽

10.1016/j.ajem.2021.05.063 ◽

2021 ◽

Author(s):

Jason P. Miller ◽

Ciara Ivanics ◽

Kristina Zalewski ◽

Swati S. Mody ◽

Nirupama Kannikeswaran

Keyword(s):

Emergency Department ◽

Pediatric Emergency ◽

Ct Imaging ◽

Clinical Impact ◽

Pediatric Emergency Department ◽

X Ray

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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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Scan Angle Selection and Volume Fusion for Reducing Metal Artifacts by Multiple X-ray CT Scanning

Precision Engineering ◽

10.1016/j.precisioneng.2021.07.020 ◽

2021 ◽

Author(s):

Yingqi Tan ◽

Ohtake Yutaka ◽

Hiromasa Suzuki

Keyword(s):

Ct Scanning ◽

Metal Artifacts ◽

X Ray

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Improved spatial resolution and lower-dose pediatric CT imaging: a feasibility study to evaluate narrowing the X-ray photon energy spectrum

Pediatric Radiology ◽

10.1007/s00247-014-2893-4 ◽

2014 ◽

Vol 44 (8) ◽

pp. 1026-1030

Author(s):

Mark G. Benz ◽

Matthew W. Benz ◽

Steven B. Birnbaum ◽

Eric Chason ◽

Brian W. Sheldon ◽

...

Keyword(s):

Energy Spectrum ◽

Feasibility Study ◽

Spatial Resolution ◽

Photon Energy ◽

Ct Imaging ◽

X Ray ◽

Pediatric Ct ◽

Photon Energy Spectrum

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Motion correction for routine X-ray lung CT imaging

Scientific Reports ◽

10.1038/s41598-021-83403-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Doil Kim ◽

Jiyoung Choi ◽

Duhgoon Lee ◽

Hyesun Kim ◽

Jiyoung Jung ◽

...

Keyword(s):

Motion Estimation ◽

Motion Correction ◽

Ct Imaging ◽

Motion Artifacts ◽

Ct Scans ◽

Breath Hold ◽

X Ray ◽

Wide Range ◽

Thorax Ct ◽

Lung Ct

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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