Post-reconstruction non-local means filtering methods using CT side information for quantitative SPECT

Feasibility of noise-reduction reconstruction technology based on non-local-mean principle in SiPM-PET/CT

Quantitative values of positron emission tomography (PET) images using non-local-mean in a silicon photomultiplier (SiPM)-PET/computed tomography (CT) system with phantom and clinical images. The evaluation was conducted on a National Electrical Manufacturers Association body phantom with micro-spheres (4, 5, 6, 8, 10, 13 mm) and clinical images using the SiPM-PET/CT system. The signal-to-background ratio of the phantom was set to 4, and all PET image data was obtained and reconstructed using three-dimensional ordered subset expectation maximization, time-of-flight, point-spread function, and a 4-mm Gaussian filter (GF) and clear adaptive low-noise method (CaLM) in mild, standard, and strong intensities. The evaluation included the standardized uptake value (SUV), percent contrast (QH), coefficient of variation of the background area (CVbackground) clinical imaging for SUV of lung nodules, liver signal-to-noise ratio (SNR), and visual evaluation. SUVmax for 8-mm sphere in phantom images at 2 min for GF and CaLM (mild, standard, strong) were 2.11, 2.32, 2.02, and 1.72; the QH, 8 mm was 27.33 %, 27.47 %, 21.81 %, and 16.09 %; and CVbackground was 12.78, 11.35, 7.86, and 4.71, respectively. CaLM demonstrated higher SUVmax in clinical images than GF for all lung nodule sizes. The average SUVmax for nodules with a diameter of ≤ 1 cm were 5.9 ± 2.4, 9.9 ± 4.9, 9.9 ± 5.0, and 9.9 ± 5.0 for GF and CaLM-mild, standard, and strong intensities, respectively. Liver SNRs were higher for CaLM (mild, standard, strong) compared to GF, with increasing CaLM intensity causing higher liver SNR. CaLM-mild and standard demonstrated suitability for diagnosis in visual evaluation.

Qualitative and Quantitative Assessment of Nonlocal Means Reconstruction Algorithm in a Flexible PET Scanner

OBJECTIVE. Flexible PET (fxPET) was designed to fit existing MRI systems. The newly modified nonlocal means (NLM) algorithm is combined with the 3D dynamic row-action maximum likelihood algorithm (DRAMA). We investigated qualitative and quantitative acceptability of fxPET images reconstructed by modified NLM compared with whole-body (WB) PET/CT images and conventional 3D DRAMA reconstruction alone. MATERIALS AND METHODS. Fifty-nine patients with known or suspected malignancies underwent WB PET/CT scanning approximately 1 hour after the injection of ¹⁸F-FDG, after which they underwent fxPET scanning. Two readers rated the quality of fxPET images by consensus. Detection rate (the proportion of lesions found on PET), maximal standardized uptake value (SUV_max), metabolic tumor volume (MTV), total lesion glycolysis (TLG), tumor-to-normal liver ratio (TNR), and background liver signal-to-noise ratio (SNR) were compared among the three datasets. RESULTS. Higher image quality was obtained by modified NLM reconstruction than by conventional reconstruction without statistical significance. The detection rate was comparable among three datasets. SUV_max was significantly higher, and MTV and TLG were significantly lower in the modified NLM dataset (p < 0.002) than in the other two datasets, with significantly positive correlations (p < 0.001; Spearman rank correlation coefficient, 0.87-0.99). The TNRs in modified NLM images were significantly larger than in the other datasets (p < 0.05). The background SNRs in modified NLM images were comparable with those in WB PET/CT images, and significantly higher than in the conventional fxPET images (p < 0.005). CONCLUSION. The modified NLM algorithm was clinically acceptable, yielding higher TNR and background SNR compared with conventional reconstruction. Image quality and the lesion detection rate were comparable in this population.

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.

Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition

BACKGROUND AND OBJECTIVE

The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images.

METHODS

We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic ⁸²Rb MP PET scan data, we optimized and compared its performance with other restoration methods.

RESULTS

The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive ⁸²Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac ⁸²Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms.

CONCLUSIONS

The proposed method is effective for restoration and enhancement of dynamic PET images.

Bounded Self-Weights Estimation Method for Non-Local Means Image Denoising Using Minimax Estimators

A non-local means (NLM) filter is a weighted average of a large number of non-local pixels with various image intensity values. The NLM filters have been shown to have powerful denoising performance, excellent detail preservation by averaging many noisy pixels, and using appropriate values for the weights, respectively. The NLM weights between two different pixels are determined based on the similarities between two patches that surround these pixels and a smoothing parameter. Another important factor that influences the denoising performance is the self-weight values for the same pixel. The recently introduced local James-Stein type center pixel weight estimation method (LJS) outperforms other existing methods when determining the contribution of the center pixels in the NLM filter. However, the LJS method may result in excessively large self-weight estimates since no upper bound is assumed, and the method uses a relatively large local area for estimating the self-weights, which may lead to a strong bias. In this paper, we investigated these issues in the LJS method, and then propose a novel local self-weight estimation methods using direct bounds (LMM-DB) and reparametrization (LMM-RP) based on the Baranchik's minimax estimator. Both the LMM-DB and LMM-RP methods were evaluated using a wide range of natural images and a clinical MRI image together with the various levels of additive Gaussian noise. Our proposed parameter selection methods yielded an improved bias-variance trade-off, a higher peak signal-to-noise (PSNR) ratio, and fewer visual artifacts when compared with the results of the classical NLM and LJS methods. Our proposed methods also provide a heuristic way to select a suitable global smoothing parameters that can yield PSNR values that are close to the optimal values.

Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review

Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

Few-view CT reconstruction via a novel non-local means algorithm

PURPOSE

Non-local means (NLM) based reconstruction method is a promising algorithm for few-view computed tomography (CT) reconstruction, but often suffers from over-smoothed image edges. To address this problem, an adaptive NLM reconstruction method based on rotational invariance (ART-RIANLM) is proposed.

METHODS

The method consists of four steps: 1) Initializing parameters; 2) ART reconstruction using raw data; 3) Positivity constraint of the reconstructed image; 4) Image updating by RIANLM filtering. In RIANLM, two kinds of rotational invariance measures which are average gradient (AG) and region homogeneity (RH) are proposed to calculate the distance between two patches and a novel NLM filter is developed to avoid over-smoothed image. Moreover, the parameter h in RIANLM which controls the decay of the weights is adaptive to avoid over-smoothness, while it is constant in NLM during the whole reconstruction process. The proposed method is validated on two digital phantoms and real projection data.

RESULTS

In our experiments, the searching neighborhood size is set as 15×15 and the similarity window is set as 3×3. For the simulated case of Shepp-Logan phantom, ART-RIANLM produces higher SNR (36.23dB>24.00dB) and lower MAE (0.0006<0.0024) reconstructed images than ART-NLM. The visual inspection demonstrated that the proposed method could suppress artifacts or noises more effectively and recover image edges better. The result of real data case is also consistent with the simulation result.

CONCLUSIONS

A RIANLM based reconstruction method for few-view CT is presented. Compared to the traditional ART-NLM method, SNR and MAE from ART-RIANLM increases 51% and decreases 75%, respectively.

Local and Non-local Regularization Techniques in Emission (PET/SPECT) Tomographic Image Reconstruction Methods

Emission tomographic image reconstruction is an ill-posed problem due to limited and noisy data and various image-degrading effects affecting the data and leads to noisy reconstructions. Explicit regularization, through iterative reconstruction methods, is considered better to compensate for reconstruction-based noise. Local smoothing and edge-preserving regularization methods can reduce reconstruction-based noise. However, these methods produce overly smoothed images or blocky artefacts in the final image because they can only exploit local image properties. Recently, non-local regularization techniques have been introduced, to overcome these problems, by incorporating geometrical global continuity and connectivity present in the objective image. These techniques can overcome drawbacks of local regularization methods; however, they also have certain limitations, such as choice of the regularization function, neighbourhood size or calibration of several empirical parameters involved. This work compares different local and non-local regularization techniques used in emission tomographic imaging in general and emission computed tomography in specific for improved quality of the resultant images.

Quantitative SPECT/CT Imaging of (177)Lu with In Vivo Validation in Patients Undergoing Peptide Receptor Radionuclide Therapy

PURPOSE

The purpose of this study is to extend an established SPECT/CT quantitation protocol to (177)Lu and validate it in vivo using urine samples, thus providing a basis for 3D dosimetry of (177)Lu radiotherapy and improvement over current planar methods which improperly account for anatomical variations, attenuation, and overlapping organs.

PROCEDURES

In our quantitation protocol, counts in images reconstructed using an ordered subset-expectation maximization algorithm are converted to kilobecquerels per milliliter using a calibration factor derived from a phantom experiment. While varying reconstruction parameters, we tracked the ratio of image to true activity concentration (recovery coefficient, RC) in hot spheres and a noise measure in a homogeneous region. The optimal parameter set was selected as the point where recovery in the largest three spheres (16, 8, and 4 ml) stagnated, while the noise continued to increase. Urine samples were collected following 12 SPECT/CT acquisitions of patients undergoing [(177)Lu]DOTATATE therapy, and activity concentrations were measured in a well counter. Data was reconstructed using parameters chosen in the phantom experiment, and estimated activity concentration from the images was compared to the urine values to derive RCs.

RESULTS

In phantom data, our chosen parameter set yielded RCs in 16, 8, and 4 ml spheres of 80.0, 74.1, and 64.5 %, respectively. For patients, the mean bladder RC was 96.1 ± 13.2% (range, 80.6-122.4 %), with a 95 % confidence interval between 88.6 and 103.6 %. The mean error of SPECT/CT concentrations was 10.1 ± 8.3% (range, -19.4-22.4 %).

CONCLUSIONS

Our results show that quantitative (177)Lu SPECT/CT in vivo is feasible but could benefit from improved reconstruction methods. Quantifying bladder activity is analogous to determining the amount of activity in the kidneys, an important task in dosimetry, and our results provide a useful benchmark for future efforts.

The Use of Anatomical Information for Molecular Image Reconstruction Algorithms: Attenuation/Scatter Correction, Motion Compensation, and Noise Reduction

PET and SPECT are important tools for providing valuable molecular information about patients to clinicians. Advances in nuclear medicine hardware technologies and statistical image reconstruction algorithms enabled significantly improved image quality. Sequentially or simultaneously acquired anatomical images such as CT and MRI from hybrid scanners are also important ingredients for improving the image quality of PET or SPECT further. High-quality anatomical information has been used and investigated for attenuation and scatter corrections, motion compensation, and noise reduction via post-reconstruction filtering and regularization in inverse problems. In this article, we will review works using anatomical information for molecular image reconstruction algorithms for better image quality by describing mathematical models, discussing sources of anatomical information for different cases, and showing some examples.

Non-local means denoising of dynamic PET images

Objective

Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM).

Theory

NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch.

Methods

To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches – Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches.

Results

The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.