Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors

Huber, Thomas S.; Rotkopf, Lukas T.; Wiestler, Benedikt; Kunz, Wolfgang G.; Bette, Stefanie; Gempt, Jens; Preibisch, Christine; Ricke, Jens; Zimmer, Claus; Kirschke, Jan S.; Sommer, Wieland H.; Thierfelder, Kolja M.

doi:10.1007/s00330-018-5892-2

Cited by 3 publications

(10 citation statements)

References 26 publications

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“…Analysis of perfusion MRI can be done using many different methods, of which most are semiquantitative or are determined by arterial input function. A new postprocessing technique using wavelet‐based reconstruction might further improve visual assessment, as background structures and vessels are better suppressed . Furthermore, radiomics has the potential to improve complex analysis.…”

Section: New Developments and Perspectivesmentioning

confidence: 99%

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Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Dijken

Laar

Smits

et al. 2018

Magnetic Resonance Imaging

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Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment‐related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion‐weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro‐oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11–22.

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Section: New Developments and Perspectivesmentioning

confidence: 99%

“…A new postprocessing technique using wavelet-based reconstruction might further improve visual assessment, as background structures and vessels are better suppressed. 63 Furthermore, radiomics has the potential to improve complex analysis. Radiomics involves the mining of quantitative radiological features.…”

Section: New Mri Perfusion Techniquesmentioning

confidence: 99%

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Dijken

Laar

Smits

et al. 2018

Magnetic Resonance Imaging

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“…Using a custom Python 3.5 script, a continuous wavelet transform with the Paul wavelet was applied to the bolus time course corresponding to each voxel of the motion-corrected perfusion dataset [5,7]. The maximum power coefficient was then obtained by calculating the maximum of the squared coefficient matrix over all scales.…”

Section: Wavelet Analysismentioning

confidence: 99%

“…The maximum power coefficient was then obtained by calculating the maximum of the squared coefficient matrix over all scales. Briefly, following [7], with Ið r ! ; tÞ being the measured intensity signal dependent on both voxel location r !…”

Section: Wavelet Analysismentioning

confidence: 99%

“…This method was first demonstrated in computed tomography perfusion (CTP) and was recently adapted to dynamic susceptibility contrast (DSC) PWI [5,6]. Wavelet-PWI yields reproducible color-coded maps with a clear depiction of vessel-specific tumor regions in GBM and a strong suppression of background structures leading to an excellent image contrast [7]. The maximum of the wavelet power spectrum (WPS) is a sensitive parameter incorporating both perfusion magnitude and bolus shape, leading to marked signal amplification in vessel-rich tissue.…”

Section: Introductionmentioning

confidence: 99%

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The wavelet power spectrum of perfusion weighted MRI correlates with tumor vascularity in biopsy-proven glioblastoma samples

et al. 2020

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BackgroundWavelet transformed reconstructions of dynamic susceptibility contrast (DSC) MR perfusion (wavelet-MRP) are a new and elegant way of visualizing vascularization. Wavelet-MRP maps yield a clear depiction of hypervascular tumor regions, as recently shown. ResultsThere was a strong correlation between wavelet-MRP power spectrum (median = 4.41) and conventional relative cerebral blood volume (median = 5.97 ml/100g) in Spearman's rankorder correlation (κ = .83, p < .05). In a logistic regression model, the wavelet-MRP power spectrum showed a significant correlation to CD31 dichotomized to no or present staining

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Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning

Rotkopf

Zhang

Tavakoli

et al. 2022

Rofo

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Background Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years. Despite promising results from novel methods, their relatively minimal improvement compared to established methods did not generally warrant significant changes to clinical perfusion processing. Results and Conclusion Machine learning in general and deep learning in particular, which are currently revolutionizing computer-aided diagnosis, may carry the potential to change this situation and truly capture the potential of perfusion imaging. Recent advances in the training of recurrent neural networks make it possible to predict and classify time series data with high accuracy. Combining physics-based tissue models and deep learning, using either physics-informed neural networks or universal differential equations, simplifies the training process and increases the interpretability of the resulting models. Due to their versatility, these methods will potentially be useful in bridging the gap between microvascular architecture and perfusion parameters, akin to MR fingerprinting in structural MR imaging. Still, further research is urgently needed before these methods may be used in clinical practice. Key Points: Citation Format

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Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors

Cited by 3 publications

References 26 publications

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

The wavelet power spectrum of perfusion weighted MRI correlates with tumor vascularity in biopsy-proven glioblastoma samples

Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning

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