A free time point model for dynamic contrast enhanced exploration

Julie, Levebvre; Ikram, Djebali; Mailyn, Perez-Liva; Bouchouicha, Afef; Sourdon, Joevin; Bentoumi, Isma; Cuénod, Charles‐André; Balvay, D.

doi:10.1016/j.mri.2021.04.005

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…To address the problem of K i imaging with a shortened scanning protocol, a data processing method proposed in our previous work, which is based on the application of the cubic Hermite interpolation (THI), was employed to denoise the calculated Patlak data used for K i fitting. The theory of Hermite interpolation had been applied for building a mathematical model for the description of dynamic contrast‐enhanced data, 24 and also applied for matching the sample points of an invasively measured artery input function (AIF) to a specific dynamic PET scan protocol. Therefore, it has been proven to be an effective tool for medical imaging data processing 25 .…”

Section: Introductionmentioning

confidence: 99%

Accurate total‐body K_i parametric imaging with shortened dynamic ¹⁸F‐FDG PET scan durations via effective data processing

et al. 2022

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Background: Total-body dynamic PET (dPET) imaging using 18 Ffluorodeoxyglucose ( 18 F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately one hour seriously limits the application and promotion of this imaging technique. In this study, using Patlak analysis-based Ki parametric imaging as the evaluation standard, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 mins post-injection (PI) with the help of a novel Patlak data processing algorithm.Methods: Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18 F-FDG of 15 patients with different types of tumors were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired one hour PI. Patlak analysis-based Ki parametric imaging was carried out based on the imaging data corresponding to the first 30 mins PI, during which a Patlak data processing method based on third-order Hermite interpolation (THI) was applied. The resulting Ki images and standard Ki images were compared in terms of visual imaging effect and Ki estimation accuracy to evaluate the performance of the proposed data processing algorithm for parametric imaging with dPET with a shortened scan duration.Results: With the help of Patlak data processing, acceptable Ki parametric images were obtained from dPET data acquired with a shortened scan duration. Compared to Ki images obtained from unprocessed Patlak data, the resulting images from the proposed method contained less image noise, leading to remarkably improved imaging quality. Moreover, box plot analysis showed that that 30-min Ki images obtained from processed Patlak data have higher accuracy regarding tumor lesion Ki values.Conclusion: Acceptable Ki parametric images can be acquired from dynamic imaging data corresponding to the first 30 mins PI. Patlak data processing can help achieve higher Ki imaging quality and higher accuracy regarding tumor lesion Ki values. Clinically, it is possible to shorten the dynamic scan duration of 18 F-FDG PET to 30 mins to facilitate the usage of such imaging techniques on uEXPLORER scanners.

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Section: Introductionmentioning

confidence: 99%

Accurate total‐body K_i parametric imaging with shortened dynamic ¹⁸F‐FDG PET scan durations via effective data processing

et al. 2022

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“…In recent years, studies have investigated the development of various deep learning and Convolutional Neural Networks (CNNs) to generate more accurate and stable estimates of PK vascular parameters by extracting time-dependent features from DCE-MRI 24 , 25 , 28 , 74 – 78 . Given DCE-MRI data, accurate estimation of PK parameters strongly relies on appropriate selection of the best PK model to fit the data.…”

Section: Discussionmentioning

confidence: 99%

“…In general, for DCE-MRI data analysis, one of the main challenges is to choose the best PK model among competing models to describe the behavior of the time trace of CA concentration in DCE MR experiments. Compared to recent studies 24 , 25 , 28 , 74 – 78 , one of the novel components of this study is the incorporation of the nested model selection concept into adaptive models for predicting PK parameters.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, adaptive methods have been investigated by multiple groups in an effort to generate more accurate and stable estimates of PK vascular parameters by extracting time-dependent features from DCE-MRI 25 . Bliesener et al 26 estimated a voxel-wise map of K trans from DCE-MRI data using deep learning (DL) method with one-dimensional convolutional neural network (CNN) architecture.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

Bagher‐Ebadian

Brown

Ghassemi

et al. 2023

Sci Rep

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We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.

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“…The new method introduces voxel-level TAC correction based on the theory of third-order Hermite interpolation (THI) [23] into a traditional LS framework. THI has been applied for medical imaging data processing [24,25], and our previous study proved that this mathematic method is suitable for optimizing dynamic images [26]. RLS-VC achieves stable, accurate and noise-controlled low-dose DCP-CT imaging while avoiding spatial over-smoothing and guaranteeing that the resulting images provide accurate hemodynamic parameters.…”

Section: Introductionmentioning

confidence: 99%

Novel reconstruction of low-dose DCP-CT images using a regularized least-squares method based on voxel-level TAC correction (RLS-VC)

Chen

Ying

Zhang

et al. 2022

Preprint

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Dynamic cerebral perfusion computed tomography (DCP-CT) is an advanced imaging technique that helps in the clinical diagnosis of cerebrovascular diseases (CVDs). However, radiation dose deposition during repeated CT scans seriously limits its clinical application. In this study, we propose a regularized least-squares method with high interpretability based on voxel-level time-attenuation curve (TAC) correction (RLS-VC) for DCP-CT image reconstruction with a dual low-dose imaging protocol that involves both sparse sampling and low-mAs X-ray emission. The theory of third-order Hermite interpolation (THI) is applied to voxel-level TAC correction during dynamic image reconstruction. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to quantitatively evaluate the proposed method in terms of imaging accuracy and noise reduction, while hemodynamic maps, including cerebral blood flow (CBF) and cerebral blood volume (CBV), are calculated to validate its ability to restore hemodynamic parameters. It is proven that the proposed RLS-VC method for low-dose DCP-CT imaging has better performance than several state-of-the-art dynamic CT imaging methods, including PICCS, ndiNLM and PIDT, as well as the commercial FBP method. It can be expected that the RLS-VC method can play an important role in the promotion of the clinical application of DCP-CT for the diagnosis of CVD.

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A free time point model for dynamic contrast enhanced exploration

Cited by 4 publications

References 41 publications

Accurate total‐body K_i parametric imaging with shortened dynamic ¹⁸F‐FDG PET scan durations via effective data processing

Accurate total‐body K_i parametric imaging with shortened dynamic ¹⁸F‐FDG PET scan durations via effective data processing

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

Novel reconstruction of low-dose DCP-CT images using a regularized least-squares method based on voxel-level TAC correction (RLS-VC)

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A free time point model for dynamic contrast enhanced exploration

Cited by 4 publications

References 41 publications

Accurate total‐body Ki parametric imaging with shortened dynamic 18F‐FDG PET scan durations via effective data processing

Accurate total‐body Ki parametric imaging with shortened dynamic 18F‐FDG PET scan durations via effective data processing

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

Novel reconstruction of low-dose DCP-CT images using a regularized least-squares method based on voxel-level TAC correction (RLS-VC)

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Accurate total‐body K_i parametric imaging with shortened dynamic ¹⁸F‐FDG PET scan durations via effective data processing

Accurate total‐body K_i parametric imaging with shortened dynamic ¹⁸F‐FDG PET scan durations via effective data processing