4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging

Reilhac, Anthonin; Charil, Arnaud; Wimberley, Catriona; Angelis, Georgios I.; Hamze, Hasar; Callaghan, Paul D.; Boisson, Fréderic; Ryder, William J.; Meikle, Steven R.; Grégoire, Marie Claude

doi:10.1016/j.neuroimage.2015.06.029

Cited by 12 publications

(17 citation statements)

References 28 publications

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“…FBP has been largely replaced nowadays and noise characteristics may differ between the various reconstruction methods. Nevertheless, in all types of image reconstruction, the impact of noise on dynamic images and especially in voxel-wise quantification remains an important issue [17, 18, 28, 45, 53] and, in accordance with the conclusion of the present work, denoising dynamic images may enhance the statistical power of studies using SPM for voxel-wise quantification analysis. Another limitation is that the noise model was quite simple and added directly on TACs.…”

Section: Discussionsupporting

confidence: 78%

“…Indeed, the objective of this paper was to demonstrate the benefits of denoising on voxel-wise quantification and SPM analysis in molecular neuroimaging, not necessarily of FA in particular, which is employed here as an example of a denoising method. The quality of other denoising methods that have been recently described for human and small animal imaging [17, 18, 28, 45, 53, 54] is not challenged. Nevertheless, these methods have fundamental differences with each other and they may thus have a different impact on voxel-wise quantification.…”

Section: Discussionmentioning

confidence: 99%

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Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging

Tsartsalis

Tournier

et al. 2018

PLoS ONE

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PurposePET and SPECT voxel kinetics are highly noised. To our knowledge, no study has determined the effect of denoising on the ability to detect differences in binding at the voxel level using Statistical Parametric Mapping (SPM).MethodsIn the present study, groups of subject-images with a 10%- and 20%- difference in binding of [123I]iomazenil (IMZ) were simulated. They were denoised with Factor Analysis (FA). Parametric images of binding potential (BPND) were produced with the simplified reference tissue model (SRTM) and the Logan non-invasive graphical analysis (LNIGA) and analyzed using SPM to detect group differences. FA was also applied to [123I]IMZ and [11C]flumazenil (FMZ) clinical images (n = 4) and the variance of BPND was evaluated.ResultsEstimations from FA-denoised simulated images provided a more favorable bias-precision profile in SRTM and LNIGA quantification. Simulated differences were detected in a higher number of voxels when denoised simulated images were used for voxel-wise estimations, compared to quantification on raw simulated images. Variability of voxel-wise binding estimations on denoised clinical SPECT and PET images was also significantly diminished.ConclusionIn conclusion, noise removal from dynamic brain SPECT and PET images may optimize voxel-wise BPND estimations and detection of biological differences using SPM.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging

Tsartsalis

Tournier

et al. 2018

PLoS ONE

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“…Regularisation of this noise can be achieved using spatial priors but at the cost of reduced PVC. Therefore, similar to Reilhac et al [15], a denoising strategy was evaluated that can control noise with a minimal effect on spatial resolution. While in this study HYPR was used, Reilhac et al proposed a wavelet and spectral analysis based technique.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, they discussed that MR based PVC require accurate and complete segmentation (implying that the regions with high uptake are delineated), accurate PET-MR co-registration, and homogeneous distributions within VOIs. Hence, despite the lack of full signal recovery, PET-only-based PVC approaches [15] may therefore be an attractive alternative to MR-based methods when a consistent and high quality of MR data cannot be assured and, of course, in cases when MR data are not available. In our paper, we explored the feasibility and performance of a purely PET-based PVC method in combination with HYPR denoising.…”

Section: Discussionmentioning

confidence: 99%

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Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

et al. 2017

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BackgroundAccurate quantification of PET studies depends on the spatial resolution of the PET data. The commonly limited PET resolution results in partial volume effects (PVE). Iterative deconvolution methods (IDM) have been proposed as a means to correct for PVE. IDM improves spatial resolution of PET studies without the need for structural information (e.g. MR scans). On the other hand, deconvolution also increases noise, which results in lower signal-to-noise ratios (SNR). The aim of this study was to implement IDM in combination with HighlY constrained back-PRojection (HYPR) denoising to mitigate poor SNR properties of conventional IDM.MethodsAn anthropomorphic Hoffman brain phantom was filled with an [18F]FDG solution of ~25 kBq mL−1 and scanned for 30 min on a Philips Ingenuity TF PET/CT scanner (Philips, Cleveland, USA) using a dynamic brain protocol with various frame durations ranging from 10 to 300 s. Van Cittert IDM was used for PVC of the scans. In addition, HYPR was used to improve SNR of the dynamic PET images, applying it both before and/or after IDM. The Hoffman phantom dataset was used to optimise IDM parameters (number of iterations, type of algorithm, with/without HYPR) and the order of HYPR implementation based on the best average agreement of measured and actual activity concentrations in the regions. Next, dynamic [11C]flumazenil (five healthy subjects) and [11C]PIB (four healthy subjects and four patients with Alzheimer’s disease) scans were used to assess the impact of IDM with and without HYPR on plasma input-derived distribution volumes (V T) across various regions of the brain.ResultsIn the case of [11C]flumazenil scans, Hypr-IDM-Hypr showed an increase of 5 to 20% in the regional V T whereas a 0 to 10% increase or decrease was seen in the case of [11C]PIB depending on the volume of interest or type of subject (healthy or patient). References for these comparisons were the V Ts from the PVE-uncorrected scans.ConclusionsIDM improved quantitative accuracy of measured activity concentrations. Moreover, the use of IDM in combination with HYPR (Hypr-IDM-Hypr) was able to correct for PVE without increasing noise.Electronic supplementary materialThe online version of this article (doi:10.1186/s13550-017-0284-1) contains supplementary material, which is available to authorized users.

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A modern review of the uncertainties in volumetric imaging of respiratory‐induced target motion in lung radiotherapy

Vergalasova

Cai

2020

Medical Physics

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Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

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4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging

Cited by 12 publications

References 28 publications

Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging

Dynamic image denoising for voxel-wise quantification with Statistical Parametric Mapping in molecular neuroimaging

Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

A modern review of the uncertainties in volumetric imaging of respiratory‐induced target motion in lung radiotherapy

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