Postreconstruction Nonlocal Means Filtering of Whole-Body PET With an Anatomical Prior

Chan, Chung; Fulton, Roger; Barnett, R.J.; Feng, David Dagan; Meikle, Steven R.

doi:10.1109/tmi.2013.2292881

Cited by 91 publications

(62 citation statements)

References 40 publications

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“…This parameter was set to 0.7 as previously suggested in Ref. 34. This similarity measurement allows using more voxels for smoothing within an organ and less voxels on boundaries.…”

Section: B Map Reconstruction With An Anatomical Prior (Amap)mentioning

confidence: 99%

Noise suppressed partial volume correction for cardiac SPECT/CT

Chan

Liu

Grobshtein³

et al. 2016

Medical Physics

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Purpose: Partial volume correction (PVC) methods typically improve quantification at the expense of increased image noise and reduced reproducibility. In this study, the authors developed a novel voxel-based PVC method that incorporates anatomical knowledge to improve quantification while suppressing noise for cardiac SPECT/CT imaging. Methods: In the proposed method, the SPECT images were first reconstructed using anatomicalbased maximum a posteriori (AMAP) with Bowsher's prior to penalize noise while preserving boundaries. A sequential voxel-by-voxel PVC approach (Yang's method) was then applied on the AMAP reconstruction using a template response. This template response was obtained by forward projecting a template derived from a contrast-enhanced CT image, and then reconstructed using AMAP to model the partial volume effects (PVEs) introduced by both the system resolution and the smoothing applied during reconstruction. To evaluate the proposed noise suppressed PVC (NS-PVC), the authors first simulated two types of cardiac SPECT studies: a 99m Tc-tetrofosmin myocardial perfusion scan and a 99m Tc-labeled red blood cell (RBC) scan on a dedicated cardiac multiple pinhole SPECT/CT at both high and low count levels. The authors then applied the proposed method on a canine equilibrium blood pool study following injection with 99m Tc-RBCs at different count levels by rebinning the list-mode data into shorter acquisitions. The proposed method was compared to MLEM reconstruction without PVC, two conventional PVC methods, including Yang's method and multitarget correction (MTC) applied on the MLEM reconstruction, and AMAP reconstruction without PVC. Results: The results showed that the Yang's method improved quantification, however, yielded increased noise and reduced reproducibility in the regions with higher activity. MTC corrected for PVE on high count data with amplified noise, although yielded the worst performance among all the methods tested on low-count data. AMAP effectively suppressed noise and reduced the spill-in effect in the low activity regions. However it was unable to reduce the spill-out effect in high activity regions. NS-PVC yielded superior performance in terms of both quantitative assessment and visual image quality while improving reproducibility. Conclusions:The results suggest that NS-PVC may be a promising PVC algorithm for application in low-dose protocols, and in gated and dynamic cardiac studies with low counts. C 2016 American Association of Physicists in Medicine. [http://dx

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“…This parameter was set to 0.7 as previously suggested in Ref. 34. This similarity measurement allows using more voxels for smoothing within an organ and less voxels on boundaries.…”

Section: B Map Reconstruction With An Anatomical Prior (Amap)mentioning

confidence: 99%

Noise suppressed partial volume correction for cardiac SPECT/CT

Chan

Liu

Grobshtein³

et al. 2016

Medical Physics

Self Cite

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“…However, increasing the acquisition time tends to cause patient discomfort and motion artifacts [8]. Another approach to improving the SNR in PET is to apply a smoothing filter after reconstruction [9][10][11]. The Gaussian filter is widely used as a postfiltering method for noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Visual and Quantitative Assessment of a New Anisotropic Diffusion Filter (Statistical Transfer with Optimizing Noise and Edge Sensing) for Positron Emission Tomography

Iizuka¹,

Kaneta²,

Ogawa³

et al. 2017

J Mol Imaging Dynam

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Post-filtering with a Gaussian filter is commonly used to reduce noise in positron emission tomography (PET) images. However, its non-selective smoothing obscures the edges of lesions or organs. We compared the performance of a newly developed anisotropic diffusion filter called "Statistical Transfer with Optimizing Noise and Edge Sensing" (STONES) with that of the Gaussian filter for small lesions on PET images. We selected seven PET/ computed tomography (CT) image slices of the lungs from three patients with multiple lung metastases. For each slice, the lesion detection rates by two physicians (A and B) were compared for Gaussian-and STONES-filtered PET images. The maximum standardized uptake (SUV max ) values of the detected lesions were also compared for non-, Gaussian-, and STONES-filtered images. Physician A detected 19 lesions in the Gaussian-filtered images and 23 lesions in the STONES-filtered images, while Physician B detected 14 lesions in the Gaussian-filtered images and 19 lesions in the STONES-filtered images. SUV max for the STONES-filtered images was significantly higher and closer to that of the non-filtered images compared to those for the Gaussian-filtered images. STONES improved the detection rate and increased SUV max in comparison with Gaussian filter. Thus, it should be more advantageous for the detection of small lesions with PET.

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“…In [8], a nonlocal regularizer is developed, which can selectively consider the anatomical information only when it is reliable, and this information can come from MRI or CT. In the post-reconstruction process, CT [9, 10] or MRI [11] information can be incorporated. In [12], both CT and MRI are combined in the post-reconstruction process.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Adeli

et al. 2016

IEEE Trans. on Image Process.

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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 standard-dose 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 (mCCA) scheme to solve this problem. Specifically, 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. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

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Postreconstruction Nonlocal Means Filtering of Whole-Body PET With an Anatomical Prior

Cited by 91 publications

References 40 publications

Noise suppressed partial volume correction for cardiac SPECT/CT

Noise suppressed partial volume correction for cardiac SPECT/CT

Visual and Quantitative Assessment of a New Anisotropic Diffusion Filter (Statistical Transfer with Optimizing Noise and Edge Sensing) for Positron Emission Tomography

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

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