Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization

Lehnert, Judith; Wübbeler, Gerd; Kolbitsch, Christoph; Chiribiri, Amedeo; Coquelin, Loïc; Ebrard, Géraldine; Smith, Nadia; Schaeffter, Tobias; Elster, Clemens

doi:10.1088/1361-6560/aae758

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…Lehnert et al. have also recently proposed the use of spatial regularisation (Lehnert et al., 2018). In this work, a Tikhonov (L2-norm) regularisation term is added to the cost function to be used in a gradient-based optimisation process.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Bayesian myocardial perfusion quantification

Scannell

Chiribiri

Villa

et al. 2020

Medical Image Analysis

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Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data. However, the model-fitting can produce unreliable results due to the limited data acquired and the high noise levels. Such problems are especially prevalent in myocardial perfusion MRI leading to the compromise of constrained numerical deconvolutions and segmental signal averaging being commonly used as alternatives to the more complex tracer-kinetic models.Methods: In this work, the use of hierarchical Bayesian inference for the parameter estimation is explored. It is shown that with Bayesian inference it is possible to reliably fit the two-compartment exchange model to perfusion data. The use of prior knowledge on the ranges of kinetic parameters and the fact that neighbouring voxels are likely to have similar kinetic properties combined with a Markov chain Monte Carlo based fitting procedure significantly improves the reliability of the perfusion estimates with compared to the traditional least-squares approach. The method is assessed using both simulated and patient data. Results:The average (standard deviation) normalised mean square error for the distinct noise realisations of a simulation phantom falls from 0.32 (0.55) with the least-squares fitting to 0.13 (0.2) using Bayesian inference. The assessment of the presence of coronary artery disease based purely on the quantitative MBF maps obtained using Bayesian inference matches the visual assessment in all 24 slices. When using the maps obtained by the least-squares fitting, a corresponding assessment is only achieved in 16/24 slices.Conclusion: Bayesian inference allows a reliable, fully automated and user-independent assessment of myocardial perfusion on a voxel-wise level using the two-compartment exchange model.

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Section: Discussionmentioning

confidence: 99%

Hierarchical Bayesian myocardial perfusion quantification

Scannell

Chiribiri

Villa

et al. 2020

Medical Image Analysis

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“…This work extends our previous work on pixelwise quantification of perfusion where we used spatial Tikhonov regularization to account for spatial correlations [30]. For the same value of the smoothing degree \lambda the solution of the Tikhonov regularization is the MAP estimate of the Bayesian approach.…”

Section: Sensitivity Analysismentioning

confidence: 57%

“…A dual bolus protocol was used for contrast injection as previously described in [23]. For more details on the acquisition, see [30]. From the MR images only the pixels belonging to the myocardium were considered for the analysis, which resulted in a typical size of about 1400 pixels.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In reference [30], the quantification of myocardial perfusion on a pixel level using a Tikhonov regularization in space was suggested. Using the numerical phantom, the performance of the Tikhonov regularization, a single-pixel fit without regularization or filtering, and the Bayesian approach are evaluated and compared.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…However, the criterion to choose \lambda differs. For the application of the Tikhonov regularization in [30], \lambda was determined as the point of largest curvature from an L-curve [18], while in the Bayesian approach \lambda is modeled as a random parameter and is sampled from the posterior. This means that the Bayesian approach allows for a full quantification of uncertainties, including the uncertainty of the choice of \lambda which is not done in the Tikhonov approach.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging

Lehnert¹,

Kolbitsch²,

Wübbeler³

et al. 2019

SIAM J. Imaging Sci.

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We develop a hierarchical Bayesian approach for the inference of large-scale spatial-temporal regression as often encountered in the analysis of imaging data. For each spatial location a linear temporal Gaussian regression model is considered. Large-scale refers to the large number of spatially distributed regression parameters to be inferred. The spatial distribution of the sought regression parameters, which typically represent physical quantities, is assumed to be smooth and bounded from below. Truncated, intrinsic Gaussian Markov random field priors are employed to express this prior knowledge. The dimensionality of the spatially distributed parameters is high, which challenges the calculation of results using standard Markov chain Monte Carlo procedures. A calculation scheme is developed that utilizes an approximate analytical expression for the marginal posterior of the amount of smoothness and the strength of the noise in the data, in conjunction with a conditional high-dimensional truncated Gaussian distribution for the spatial distribution of the regression parameters. We prove propriety of the posterior and explore the existence of its moments. The proposed approach is applicable to high-dimensional imaging problems arising in the use of Gaussian Markov random field priors for inferring spatially distributed parameters from spatial or spatial-temporal data. We demonstrate its application for the quantification of myocardial perfusion imaging from first-pass contrast-enhanced cardiovascular magnetic resonance data. The Bayesian approach allows for a quantification of perfusion, including a complete characterization of uncertainties which is desirable in diagnostics. It therefore provides not just a perfusion estimate but also a measure for how reliable this estimate is.

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Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI

Scannell

Veta

Villa

et al. 2019

Magnetic Resonance Imaging

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BackgroundQuantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation.PurposeTo evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis.Study TypeRetrospective.PopulationIn all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test).Field Strength/Sequence3.0T/2D multislice saturation recovery T1‐weighted gradient echo sequence.AssessmentAccuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL‐based processing were compared to the results obtained with the manually processed images.Statistical TestsBland–Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator.ResultsThe mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland–Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per‐myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90].Data ConclusionWe showed high accuracy, compared to manual processing, for the DL‐based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing.Level of Evidence: 3Technical Efficacy Stage: 1J. Magn. Reson. Imaging 2020;51:1689–1696.

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Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization

Cited by 7 publications

References 37 publications

Hierarchical Bayesian myocardial perfusion quantification

Hierarchical Bayesian myocardial perfusion quantification

Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging

Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI

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