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

Abstract

Quantitative SPECT techniques are important for many applications including internal emitter therapy dosimetry where accurate estimation of total target activity and activity distribution within targets are both potentially important for dose-response evaluations. We investigated non-local means (NLM) post-reconstruction filtering for accurate I-131 SPECT estimation of both total target activity and the 3D activity distribution. We first investigated activity estimation versus number of ordered-subsets expectation-maximization (OSEM) iterations. We performed simulations using the XCAT phantom with tumors containing a uniform and a non-uniform activity distribution, and measured the recovery coefficient (RC) and the root mean squared error (RMSE) to quantify total target activity and activity distribution, respectively. We observed that using more OSEM iterations is essential for accurate estimation of RC, but may or may not improve RMSE. We then investigated various post-reconstruction filtering methods to suppress noise at high iteration while preserving image details so that both RC and RMSE can be improved. Recently, NLM filtering methods have shown promising results for noise reduction. Moreover, NLM methods using high-quality side information can improve image quality further. We investigated several NLM methods with and without CT side information for I-131 SPECT imaging and compared them to conventional Gaussian filtering and to unfiltered methods. We studied four different ways of incorporating CT information in the NLM methods: two known (NLM CT-B and NLM CT-M) and two newly considered (NLM CT-S and NLM CT-H). We also evaluated the robustness of NLM filtering using CT information to erroneous CT. NLM CT-S and NLM CT-H yielded comparable RC values to unfiltered images while substantially reducing RMSE. NLM CT-S achieved −2.7 to 2.6% increase of RC compared to no filtering and NLM CT-H yielded up to 6% decrease in RC while other methods yielded lower RCs than them: Gaussian filtering (up to 11.8% decrease in RC), NLM method without CT (up to 9.5% decrease in RC), and NLM CT-M and NLM CT-B (up to 19.4% decrease in RC). NLM CT-S and NLM CT-H achieved 8.2 to 33.9% and −0.9 to 36% decreased RMSE on tumors compared to no filtering respectively while other methods yielded less reduced or increased RMSE: Gaussian filtering (up to 7.9% increase in RMSE), NLM method without CT (up to 18.3% increase in RMSE), and NLM CT-M and NLM CT-B (up to 31.5% increase in RMSE). NLM CT-S and NLM CT-H also yielded images with tumor shapes that better-matched the true shapes than other methods. All NLM methods using CT information were robust to small misregistration between SPECT and CT, but NLM CT-S and NLM CT-H were more sensitive than NLM CT-M and NLM CT-B to missing CT information.

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

“…19 so that (20) where anatomical image g was incorporated into filters. For example, in NLM type filters, weights can be determined based on the patch similarities from both PET (or SPECT) and the anatomical image or weights from the molecular image can be only used when the corresponding pixel pairs have high similarity for the anatomical image [44,45]. In variants of guided image filters, the low-noise anatomical image can be used as a guide image to improve the quality of filtered image, especially when both molecular and anatomical images share the same edge structures [46,47].…”

“…Ideas of using structural couplings between molecular and anatomical images for reconstruction have been studied a couple of decades ago [41][42][43]. Recently, interesting advances for noise reduction of molecular images using anatomical information have been introduced with state-of-the-art methods for post-reconstruction filtering [44][45][46][47] or regularization in inverse problems [48][49][50][51][52][53].…”

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

“…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.…”

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

“…However, these functions produce overly smoothed image regions [8], induce staircase or piecewise blocky artefacts and result in contrast loss because they can provide indiscriminate local prior information available in the image and are less efficient due to their local behaviour [12,14]. Recently, several non-local regularization schemes have been introduced to combat these issues of local regularization [2,12,[14][15][16][17][18][19][20]. Non-local regularization makes use of the global image connectivity and continuity available in the objective image and can prevent, somehow, over-smoothness with robust edge preservation at the same time, without appreciable staircase effects or contrast loss [2,8,[17][18][19][21][22][23].…”