Terms Of Quantitative Accuracy Research Articles

Double-Layer Search and Adaptive Pooling Fusion for Reference-Based Image Super-Resolution

Reference-based image super-resolution (RefSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) images by introducing HR reference images. The key step of RefSR is to transfer reference features to LR features. However, existing methods still lack an efficient transfer mechanism, resulting in blurry details in the generated image. In this article, we propose a double-layer search module and an adaptive pooling fusion module group for reference-based image super-resolution, called DLASR. Based on the re-search strategy, the double-layer search module can produce an accurate index map and score map. These two maps are used to filter out accurate reference features, which greatly increases the efficiency of feature transfer in the later stage. Through two continuous feature-enhancement steps, the adaptive pooling fusion module group can transfer more valuable reference features to the corresponding LR features. In addition, a structure reconstruction module is proposed to recover the geometric information of the images, which further improves the visual quality of the generated image. We conduct comparative experiments on a variety of datasets, and the results prove that DLASR achieves significant improvements over other state-of-the-art methods, in terms of quantitative accuracy and qualitative visual effect. The code is available at https://github.com/clttyou/DLASR.

Two‐stage learning framework for single image deraining

AbstractSingle image deraining methods have been extensively studied for its ability to remarkably improve the performance of computer vision tasks in rainy environments. However, most existing rain removal methods still have two major drawbacks which are hindering the technology development. First, the rain streaks are seriously coupled with the background information in a single rainy image, which leads to incorrect identification of rain streaks by many methods and further makes the loss of texture details in the rain removal results. Second, they spend excessive computational cost, which is not conducive to practical applications. To address these issues, a progressive separation network (PSN) is proposed by decomposing the rain removal task into two stages, the bilateral grid learning stage and the joint feature refinement stage, from a novel perspective. The bilateral grid learning stage is designed to expand the distance between the rain streaks and the background information while preserving the image edge details to guide the subsequent refinement. For the joint feature refinement stage, a dual‐path interaction module is constructed to dynamically and gradually decouple the rain streak content and the intermediate features of the clear image details. In addition, an activation‐free feature refinement block is designed to further improve the computational efficiency by removing or replacing the activation function without loss of accuracy. Extensive experiments on synthetic and real datasets show that PSN outperforms state‐of‐the‐art rain removal methods in terms of quantitative accuracy and subjective visual quality. Furthermore, competitive results are derived by extending PSN to the defogging task.

Ultrahigh-resolution PM2.5 estimation from top-of-atmosphere reflectance with machine learning: Theories, methods, and applications

Intra-urban pollution monitoring requires fine particulate (PM2.5) concentration mapping at ultrahigh-resolution (dozens to hundreds of meters). However, current PM2.5 concentration estimation, which is mainly based on aerosol optical depth (AOD) and meteorological data, usually had a low spatial resolution (kilometers) and severe spatial missing problem, cannot be applied to intra-urban pollution monitoring. To solve these problems, top-of-atmosphere reflectance (TOAR), which contains both the information about land and atmosphere and has high resolution and large spatial coverage, may be efficiently used for PM2.5 estimation. This study aims to systematically evaluate the feasibility of retrieving ultrahigh-resolution PM2.5 concentration at a large scale (national level) from TOAR. Firstly, we make a detailed discussion about several important but unsolved theoretic problems on TOAR-based PM2.5 retrieval, including the band selection, scale effect, cloud impact, and mapping quality evaluation. Secondly, four types and eight retrieval models are compared in terms of quantitative accuracy, mapping quality, model generalization, and model efficiency, with the pros and cons of each type summarized. Deep neural network (DNN) model shows the highest retrieval accuracy, and linear models were the best in efficiency and generalization. As a compromise, ensemble learning shows the best overall performance. Thirdly, using the highly accurate DNN model (cross-validated R2 equals 0.93) and through combining Landsat 8 and Sentinel 2 images, a 90 m and ∼4-day resolution PM2.5 product was generated. The retrieved maps were used for analyzing the fine-scale interannual pollution change inner the city and the pollution variations during novel coronavirus disease 2019 (COVID-19). Results of this study proves that ultrahigh resolution can bring new findings of intra-urban pollution change, which cannot be observed at previous coarse resolution. Lastly, some suggestions for future ultrahigh-resolution PM2.5 mapping research were given.

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.

Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by algorithms. The progress in the field of automatic ECG analysis has up to now been hampered by a lack of appropriate datasets for training as well as a lack of well-defined evaluation procedures to ensure comparability of different algorithms. To alleviate these issues, we put forward first benchmarking results for the recently published, freely accessible clinical 12-lead ECG dataset PTB-XL, covering a variety of tasks from different ECG statement prediction tasks to age and sex prediction. Among the investigated deep-learning-based timeseries classification algorithms, we find that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks. We find consistent results on the ICBEB2018 challenge ECG dataset and discuss prospects of transfer learning using classifiers pretrained on PTB-XL. These benchmarking results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis, which provide connecting points for future research on the dataset. Our results emphasize the prospects of deep-learning-based algorithms in the field of ECG analysis, not only in terms of quantitative accuracy but also in terms of clinically equally important further quality metrics such as uncertainty quantification and interpretability. With this resource, we aim to establish the PTB-XL dataset as a resource for structured benchmarking of ECG analysis algorithms and encourage other researchers in the field to join these efforts.

Quantitative bone SPECT/CT reconstruction utilizing anatomical information

BackgroundBone SPECT/CT has been shown to offer superior sensitivity and specificity compared to conventional whole-body planar scanning. Furthermore, bone SPECT/CT allows quantitative imaging, which is challenging with planar methods. In order to gain better quantitative accuracy, Bayesian reconstruction algorithms, including both image derived and anatomically guided priors, have been utilized in reconstruction in PET/CT scanning, but they have not been widely used in SPECT/CT studies. Therefore, the aim of this work was to evaluate the performance of CT-guided reconstruction in quantitative bone SPECT.MethodsThree Bayesian reconstruction methods were evaluated against the conventional ordered subsets expectation maximization (OSEM) reconstruction method. One of the studied Bayesian methods was the relative difference prior (RDP), which has recently gained popularity in PET reconstruction. The other two methods, anatomically guided smoothing prior (AMAP-S) and anatomically guided relative difference prior (AMAP-R), utilized anatomical information from the CT scan. The reconstruction methods were evaluated in terms of quantitative accuracy with artificial lesions inserted in clinical patient studies and with 20 real clinical patients. Maximum and mean standardized uptake values (SUVs) of the lesions were defined.ResultsThe analyses showed that all studied Bayesian methods performed better than OSEM and the anatomical priors also outperformed RDP. The average relative error in mean SUV for the artificial lesion study for OSEM, RDP, AMAP-S, and AMAP-R was − 53%, − 35%, − 15%, and − 10%, when the CT study had matching lesions. In the patient study, the RDP method gave 16 ± 9% higher maximum SUV values than OSEM, while AMAP-S and AMAP-R offered increases of 36 ± 8% and 36 ± 9%, respectively. Mean SUV increased for RDP, AMAP-S, and AMAP-R by 18 ± 9%, 26 ± 5%, and 33 ± 5% when compared to OSEM.ConclusionsThe Bayesian methods with anatomical prior, especially the relative difference prior-based method (AMAP-R), outperformed OSEM and reconstruction without anatomical prior in terms of quantitative accuracy.

Respiratory-correlated 4D digital tomosynthesis with deep convolutional neural networks for image-guided radiation therapy

4D digital tomosynthesis (DTS) techniques for image-guided radiation therapy (IGRT) are able to reduce radiation dose, scan and reconstruction time compared to 4D cone-beam computed tomography (CBCT). In spite of these benefits, the 4D DTS techniques cause the degradation of image quality due to an intrinsic imaging strategy and consequently reduce treatment accuracy. In this study, a deep learning-based convolutional neural network (CNN) framework was proposed for 4D DTS imaging. The proposed CNN framework consisted of the data restoration network based on a U-Net and the denoising network combined with a 2D wavelet transform, and the network training was implemented with clinical images. The quality of the 4D DTS images obtained from the proposed model was evaluated in terms of quantitative accuracy, spatial resolution and noise property. The results showed that the proposed CNN framework improved the quantitative accuracy of 4D DTS images by 3–19%, and the spatial resolution and noise for the proposed CNN framework were reduced by 2.24–7.33% and 8.92–40.07%, respectively, in comparison to other imaging models. These results represented that the degradation of the 4D DTS image quality can be recovered using the proposed CNN framework, and the proposed model is suitable for maintaining spatial resolution as well as suppressing noise and artifacts. In conclusion, the proposed CNN framework can be potentially used to improve the quality of 4D DTS images for the IGRT.

Estimation of the lower limits for feasible Ra-223 SPECT imaging: a Monte Carlo simulation study.

Ra-223 is a promising radionuclide for the treatment of skeletal metastases in castration-resistant prostate cancer patients. This study aims to estimate the lower limits for feasible Ra-223 single-photon emission computerized tomography (SPECT) imaging using a Monte Carlo simulation study. The SPECT images were produced on a homemade code: the Monte Carlo simulation of electrons and photons for SPECT (MCEP-SPECT). The National Electrical Manufacturers Association (NEMA) phantom with six hot spheres of diameters of 37, 28, 22, 17, 13, and 10 mm installed inside, was used. The background activity concentration was 0.6 kBq/mL, and the ratios of hot concentrations to the background (R HB) were 25, 20, 15, 10, and 5. When R HB was 15, the background concentrations of 1.5, 0.9, 0.3, and 0.15 kBq/mL were also tested. The energy window was 84 keV±10%. The number of projections was 60/360°, and the acquisition time was 60 s per projection. Two kinds of collimators: middle-energy general-purpose (MEGP) and high-energy general-purpose (HEGP), were examined. The SPECT images were evaluated based on two quantitative indexes: contrast-to-noise ratio (CNR) for detectability and contrast recovery coefficient (CRC) for quantitative accuracy. The CRC for the HEGP collimator was 35-40%, while the CRC for the MEGP collimator was 25-30%. The CNRs for the MEGP collimator were larger than those for the HEGP collimator. The CNRs of the hot spheres with diameters less than 22 mm were lower than 5.0 for both collimators, when R HB and the background concentration were 15 and 0.6 kBq/mL, respectively. Based on the obtained results, it was estimated that the lower limit of R HB for the detection of the hot sphere with a diameter of 37 mm would be approximately 20 if the background concentration is 0.05 kBq/mL. The MEGP collimator is superior in terms of detectability, while the HEGP collimator is superior in terms of quantitative accuracy. When the lesion size is small, the MEGP collimator may be favorable. Based on these results, the estimated lower limit of the activity concentration would be approximately 1 kBq/mL if the background concentration is 0.05 kBq/mL for a large lesion.

Development of a truncation artifact reduction method in stationary inverse-geometry X-ray laminography for non-destructive testing

In an industrial field, non-destructive testing (NDT) is commonly used to inspect industrial products. Among NDT methods using radiation sources, X-ray laminography has several advantages, such as high depth resolution and low computational costs. Moreover, an X-ray laminography system with stationary source array and compact detector is able to reduce mechanical motion artifacts and improve inspection efficiency. However, this system, called stationary inverse-geometry X-ray laminography (s-IGXL), causes truncation artifacts in reconstructed images due to limited fields-of-view (FOVs). In this study, we proposed a projection data correction (PDC) method to reduce the truncation artifacts arisen in s-IGXL images, and the performance of the proposed method was evaluated with the different number of focal spots in terms of quantitative accuracy. Comparing with conventional techniques, the PDC method showed superior performance in reducing truncation artifacts and improved the quantitative accuracy of s-IGXL images for all the number of focal spots. In conclusion, the PDC method can improve the accuracy of s-IGXL images and allow precise NDT measurements.

Geometric dependence of image quality in digital tomosynthesis: Simulations of X-ray source trajectories and scan angles

Digital tomosynthesis (DTS) is able to provide the 3-dimensional (3D) images of an object, while reducing radiation dose compared to computed tomography (CT). Despite these benefits, DTS has a geometric sensitivity because only a few dozen projections obtained with limited scan angles are used for DTS imaging. In this study, we evaluated the effects of X-ray source scan trajectories and angles on DTS images for investigating the geometric dependence in DTS. The DTS systems were simulated with linear, rotational and circular scan trajectories, and 54 projections were obtained from each of the four scan angle intervals of ±17, ±27, ±37 and ±47 degrees. The obtained projections were reconstructed by using a model-based filtered back-projection (FBP) technique. The characteristics of reconstructed DTS images were analyzed in terms of quantitative accuracy, noise property, contrast-to-noise ratio (CNR) and spatial resolution. The results showed that the quality of DTS images was highly dependent on the geometric variations, and the characteristics of DTS images differed according to the X-ray source scan trajectories and angles. In conclusion, the DTS geometries should be determined by considering image quality and characteristic in order to optimize their systems and applications.

Quantification of the impact of TOF and PSF on PET images using the noise-matching concept: clinical and phantom study

This study was to assess quantitatively the accuracy of 18F-FDG PET/CT images reconstructed by TOF + PSF and TOF only, considering the noise-matching concept to minimize probable bias in evaluating algorithm performance caused by noise. PET images of similar noise level were considered. Measurements were made on an in-house phantom with hot inserts of Φ10–37 mm, and oncological images of 14 patients were analyzed. The PET images were reconstructed using the OSEM, OSEM + TOF and OSEM + TOF + PSF algorithms. Optimal reconstruction parameters including iteration, subset, and FWHM of post-smoothing filter were chosen for both the phantom and patient data. In terms of quantitative accuracy, the recovery coefficient (RC) was calculated for the phantom PET images. The signal-to-noise ratio (SNR), lesion-to-background ratio (LBR), and SUVmax were evaluated from the phantom and clinical data. The smallest hot insert (Φ10 mm) with 2:1 activity concentration ratio could be detected in the PET image reconstructed using the TOF and TOF + PSF algorithms, but not the OSEM algorithm. The relative difference for SNR between the TOF + PSF and OSEM showed significantly higher values for smaller sizes, while SNR change was smaller for Φ22–37 mm inserts both 2:1 and 4:1 activity concentration ratio. In the clinical study, SNR gains were 1.6 ± 0.53 and 2.7 ± 0.74 for the TOF and TOF + PSF, while the relative difference of contrast was 17 ± 1.05 and 41.5 ± 1.85% for the TOF only and TOF + PSF, respectively. The impact of TOF + PSF is more significant than that of TOF reconstruction, in smaller inserts with low activity concentration ratio. In the clinical PET/CT images, the use of the TOF + PSF algorithm resulted in better SNR and contrast for lesions, and the highest SUVmax was also seen for images reconstructed with the TOF + PSF algorithm.

Computationally efficient error estimate for evaluation of regularization in photoacoustic tomography.

The model-based image reconstruction techniques for photoacoustic (PA) tomography require an explicit regularization. An error estimate (?2) minimization-based approach was proposed and developed for the determination of a regularization parameter for PA imaging. The regularization was used within Lanczos bidiagonalization framework, which provides the advantage of dimensionality reduction for a large system of equations. It was shown that the proposed method is computationally faster than the state-of-the-art techniques and provides similar performance in terms of quantitative accuracy in reconstructed images. It was also shown that the error estimate (?2) can also be utilized in determining a suitable regularization parameter for other popular techniques such as Tikhonov, exponential, and nonsmooth (?1 and total variation norm based) regularization methods.

BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge.

The binding energy distribution analysis method (BEDAM) protocol has been employed as part of the SAMPL4 blind challenge to predict the binding free energies of a set of octa-acid host-guest complexes. The resulting predictions were consistently judged as some of the most accurate predictions in this category of the SAMPL4 challenge in terms of quantitative accuracy and statistical correlation relative to the experimental values, which were not known at the time the predictions were made. The work has been conducted as part of a hands-on graduate class laboratory session. Collectively the students, aided by automated setup and analysis tools, performed the bulk of the calculations and the numerical and structural analysis. The success of the experiment confirms the reliability of the BEDAM methodology and it shows that physics-based atomistic binding free energy estimation models, when properly streamlined and automated, can be successfully employed by non-specialists.

PET/MR attenuation correction: where have we come from and where are we going?

The advent of clinical multimodality imaging with the development of PET/CT scanners [1] has presented us with ample opportunities to harvest the benefits of combining functional and anatomical imaging. These benefits concern improved PET quantitative accuracy and overall patient management (improved diagnostic accuracy and therapy response assessment), but also increased patient throughput. It is indeed the last of these points that has substantially contributed to the rapid acceptance of PET/CT imaging in clinical practice eclipsing PET-only systems. Indeed, one of the major reasons behind the improved patient throughput achieved with PET/ CT has been the use of CT images for attenuation correction (AC) of the acquired PET emission datasets. In this context, CT images possess two desirable properties. Firstly, CT acquisitions are very fast, removing the need for long radionuclide-based transmission imaging that was traditionally used in PET (about 50 % of the overall acquisition times). Secondly, CT intensity values represent the attenuation properties of the tissues in the imaging field of view, albeit at X-ray photon energies. The necessary transformation of CT images into attenuation maps at 511 keV can be achieved by bilinear transformation [2]. Although such a transformation represents a certain approximation, CT-based AC of PET datasets using such attenuation maps has been shown to lead to the same level of quantitative accuracy and superior contrast in the reconstructed PET images compared to radionuclide-based transmission scanning [3]. In the last couple of years clinical PET/MR devices have become a reality and the first results concerning the potential of this modality in terms of patient management are beginning to emerge. However, different issues persist with respect to the quantitative accuracy of this new modality, concerning in particular the question of PET AC based on the use of MRderived attenuation maps. A recent study published in this journal clearly reinforces these issues for neurological imaging [4]. In contrast to CT imaging, MRI does not provide direct information concerning tissue attenuation properties, and there is therefore no direct way to obtain the required information for PET AC purposes. Most of the approaches currently proposed in clinical PET/MR systems for PET AC are based on the combination of specific MR sequences and subsequent image segmentation. Amongst these, the approach implemented in the first generation of clinical systems is based on the two-point Dixon gradient echo sequence [5]. This sequence, which involves a few seconds of acquisition time, allows the separation of water and fat tissue by using the chemical shift of fat relative to that of water. This information facilitates in turn the segmentation of MR images into four to five different classes (lung, fat tissue, nonfat tissue, mixture of fat/nonfat tissue, air) [6]. It is important to highlight that once segmented, fixed 511 keV linear attenuation coefficients (LACs) are assigned to each of the considered tissue types, largely ignoring tissue heterogeneities. However, the biggest drawback of this approach is the lack for consideration of bone structures which are considered as soft tissue for the purpose of reconstructing PETAC maps. The use of this approach may therefore introduce severe quantitative errors depending clearly on the location of the region of interest. In terms of quantitative accuracy, it is generally accepted that the inclusion of bone in the AC of brain PET images is essential. On the other hand, the exclusion D. Visvikis : F. Monnier : J. Bert :M. Hatt :H. Fayad INSERM, UMR1101 LaTIM, CHRU Brest, Brest, France

Glyco-Analytical Multispecific Proteolysis (Glyco-AMP): A Simple Method for Detailed and Quantitative Glycoproteomic Characterization

Despite recent advances, site-specific profiling of protein glycosylation remains a significant analytical challenge for conventional proteomic methodology. To alleviate the issue, we propose glyco-analytical multispecific proteolysis (Glyco-AMP) as a strategy for glycoproteomic characterization. Glyco-AMP consists of rapid, in-solution digestion of an analyte glycoprotein (or glycoprotein mixture) by a multispecific protease (or protease cocktail). Resulting glycopeptides are chromatographically separated by isomer-specific porous graphitized carbon nano-LC, quantified by high-resolution MS, and structurally elucidated by MS/MS. To demonstrate the consistency and customizability of Glyco-AMP methodology, the glyco-analytical performances of multispecific proteases subtilisin, pronase, and proteinase K were characterized in terms of quantitative accuracy, sensitivity, and digestion kinetics. Glyco-AMP was shown be effective on glycoprotein mixtures as well as glycoproteins with multiple glycosylation sites, providing detailed, quantitative, site- and structure-specific information about protein glycosylation.

Practical fully three-dimensional reconstruction algorithms for diffuse optical tomography

We have developed an efficient fully three-dimensional (3D) reconstruction algorithm for diffuse optical tomography (DOT). The 3D DOT, a severely ill-posed problem, is tackled through a pseudodynamic (PD) approach wherein an ordinary differential equation representing the evolution of the solution on pseudotime is integrated that bypasses an explicit inversion of the associated, ill-conditioned system matrix. One of the most computationally expensive parts of the iterative DOT algorithm, the reevaluation of the Jacobian in each of the iterations, is avoided by using the adjoint-Broyden update formula to provide low rank updates to the Jacobian. In addition, wherever feasible, we have also made the algorithm efficient by integrating along the quadratic path provided by the perturbation equation containing the Hessian. These algorithms are then proven by reconstruction, using simulated and experimental data and verifying the PD results with those from the popular Gauss-Newton scheme. The major findings of this work are as follows: (i) the PD reconstructions are comparatively artifact free, providing superior absorption coefficient maps in terms of quantitative accuracy and contrast recovery; (ii) the scaling of computation time with the dimension of the measurement set is much less steep with the Jacobian update formula in place than without it; and (iii) an increase in the data dimension, even though it renders the reconstruction problem less ill conditioned and thus provides relatively artifact-free reconstructions, does not necessarily provide better contrast property recovery. For the latter, one should also take care to uniformly distribute the measurement points, avoiding regions close to the source so that the relative strength of the derivatives for measurements away from the source does not become insignificant.

Rapid determination of 40 pesticide residues in fruits using gas chromatography-mass spectrometry coupled with analyte protectants to compensate for matrix effects

A gas chromatography-mass spectrometry (GC-MS) method was developed for the determination of 40 pesticides in fruits. The effects of adding analyte protectants were evaluated for compensating matrix effects and the impacts on the quantitative results. A new combination of analyte protectants - Polyethylene Glycol 400 (PEG 400) and olive oil combination, which can be dissolved in acetone, was used for the quantitative analysis. The pesticides were extracted from fruit samples with acetonitrile and the extracts were cleaned up using micro-solid phase extraction. A GC-MS method in selective ion monitoring (SIM) mode coupled with large volume injection was finally developed. Using the newly developed analyte protectant combination of PEG 400 and olive oil, a good linearity was obtained in the range of 1 - 200 microg/L with coefficients better than 0.99, and the detection limits were between 0.1 - 3.0 microg/L. The mean recoveries of the pesticides were 75% - 119% with the relative standard deviation values less than 16.6% except for dimethoate. The performance of the analyte protectants was compared with matrix-matched standards calibration curves in terms of quantitative accuracy. The results showed that the method of adding analyte protectants can replace the matrix-matched standard calibration, and can also reduce the sample pretreatment. When the devel- oped method was used for the analysis of apple, peache, orange, banana, grape and other fruit samples, a good matrix compensation effect was achieved, and thus effectively reduced the bad effects of the water-soluble agents to the gas chromatographic column.

Three-dimensional SPECT reconstruction with transmission-dependent scatter correction

The quality of single-photon emission computed tomography (SPECT) imaging is hampered by attenuation, collimator blurring, and scatter. Correction for all of these three factors is required for accurate reconstruction, but unfortunately, reconstruction-based compensation often leads to clinically unacceptable long reconstruction times. Especially, efficient scatter correction has proved to be difficult to achieve. The objective of this article was to extend the well-known transmission-dependent convolution subtraction (TDCS) scatter-correction approach into a rapid reconstruction-based scatter-compensation method and to include it into a fast 3D reconstruction algorithm with attenuation and collimator-blurring corrections. Ordered subsets expectation maximization algorithm with attenuation, collimator blurring, and accelerated transmission-dependent scatter compensation were implemented. The new reconstruction method was compared with TDCS-based scatter correction and with one other transmission-dependent scatter-correction method using Monte Carlo simulated projection data of (99m)Tc-ECD and (123)I-FP-CIT brain studies. The new reconstruction-based scatter compensation outperformed the other two scatter-correction methods in terms of quantitative accuracy and contrast measured with normalized mean-squared error, gray-to-white matter and striatum-to-background ratios, and also in visual quality. Highest accuracy was achieved when all the corrections (i.e., attenuation, collimator blurring, and scatter) were applied. The developed 3D reconstruction algorithm with transmission-dependent scatter compensation is a promising alternative to accurate and efficient SPECT reconstruction.

A rapid CBF/CMRO2 measurement with a single PET scan with dual-tracer/integration technique in human

CBF/CMRO2 may be quantified using PET with 15O-tracers, but the conventional three-step technique (3S) requires a long study period, attributed to need for separate acquisition for three radioactive tracers for CBF (H215O or C15O2), CMRO2 (15O2), and CBV (C15O). Simultaneous determination of CBF, CMRO2 and CBV from a single dynamic 15O2 scan is known to suffer from statistical uncertainty. We recently developed a rapid technique that allows sequential administration of two of three tracers (for CBF and CMRO2) in a single PET scan, and generates quantitative CBF/CMRO2 images with a dual-tracer integration method (DTI). This study asked whether this technique is feasible in normal human subjects and how accurate quantitative values it provides in comparison with the 3S and non-linear fitting method (NLM). Eight normal subjects (24.2+-2.6 y.o) participated in this study. The protocol of the study was approved by the local ethical committee. All subjects received three emission scans, i.e., a 4-min scan after short bolus administration of C15O, a 9.5-min scan with dual administration of C15O2 followed by 15O2, and a 9-min scan with 15O2 followed by H215O. With correction for blood volume using the first C15O scan data, CBF/CMRO2 images were generated by 3S using the first part of the second (CO2) and the third scan (O2), whereas those by DTI method were calculated using both part of PET data in each of the second or the third scan. The calculation used a mathematical formula derived from a single-tissue compartment model including a correction for blood volume and an assumed partition coefficient for water. Regional quantitative values of CBF/CMRO2 in the DTI was compared with those by 3S and NLM. We also compared by voxel-by-voxel CBF or CMRO2 images between the second and the third DTI scan to test the regional difference. Global CBF or CMRO2 values for the DTI did not differ from those by the 3S, and between the second and third DTI scans. The cortical regional values of the DTI neither differed significantly from those of 3S and NLM. In contrast, CMRO2 values of the white matter region in both 3S and DTI differed significantly from NLM, as was predicted from a simulation study for the error in the assumed partition coefficient. Voxel-based comparison of CBF images between the second and the third DTI scan showed significant difference at brain regions around nasal cavity, and at the somatosensory areas that may come from the difference in methodology (inhalation or venous-injection of CBF tracer) and physiology (sensation of intravenous injection), respectively. These were also seen when compared CMRO2 images, but at a lower height threshold. These results suggest that this DTI technique generates CBF/CMRO2 images with a considerably shortened scan period, which are comparable to those by 3S, in terms of quantitative accuracy and image quality. The technique may be of use for clinical studies in patients particularly with acute stroke, and also for investigation of metabolic coupling during neuronal activation or pharmacological challenges in human.

Monte Carlo evaluation of accuracy and noise properties of two scatter correction methods for /sup 201/Tl cardiac SPECT

Two independent scatter correction techniques, transmission dependent convolution subtraction (TDCS) and triple-energy window (TEW) method, were evaluated in terms of quantitative accuracy and noise properties using Monte Carlo simulation (EGS4). Emission projections (primary, scatter and scatter plus primary) were simulated for three numerical phantoms for /sup 201/Tl. Data were reconstructed with ordered-subset EM algorithm including noise-less transmission data based attenuation correction. Accuracy of TDCS and TEW scatter corrections were assessed by comparison with simulated true primary data. The uniform cylindrical phantom simulation demonstrated better quantitative accuracy with TDCS than with TEW (-2.0% vs. 16.7%) and better S/N (6.48 vs. 5.05). A uniform ring myocardial phantom simulation demonstrated better homogeneity with TDCS than TEW in the myocardium; i.e., anterior-to-posterior wall count ratios were 0.99 and 0.76 with TDCS and TEW, respectively. For the MCAT phantom, TDCS provided good visual and quantitative agreement with simulated true primary image without noticeably increasing the noise after scatter correction. Overall TDCS proved to be more accurate and less noisy than TEW, facilitating quantitative assessment of physiological functions with SPECT.

The importance and implementation of accurate 3D compensation methods for quantitative SPECT

The authors investigated the importance of 2D versus 3D compensation methods in SPECT. The compensation methods included in the study addressed two important degrading factors, namely attenuating and collimator-detector response in SPECT. They can be divided into two general categories. The conventional methods are based on the filtered backprojection algorithm, the Chang algorithm for attenuation compensation and the Metz filter for detector response compensation. These methods, which were computationally efficient, could only achieve approximate compensation due to the assumptions made. The quantitative compensation methods provide accurate compensation by modelling the degrading effects at the expense of large computational requirements. Both types of compensation methods were implemented in 2D and 3D reconstructions. The 2D and 3D reconstruction/compensation methods were evaluated using data from simulation of brain and heart, and patient thallium SPECT studies. The authors' results demonstrate the importance of compensation methods in improving the quality and quantitative accuracy of SPECT images and the relative effectiveness of the different 2D and 3D reconstruction/compensation methods. It is concluded that 3D implementation of the quantitative compensation methods provides the best SPECT image in terms of quantitative accuracy, spatial resolution, and noise at a cost of high computational requirements.