Denoising of Dynamic Contrast-enhanced Ultrasound Sequences: A Multilinear Approach

Calis, Metin; Mischi, Massimo; Veen, Alle‐Jan van der; Hunyadi, Borbála

doi:10.5220/0010838000003123

Cited by 1 publication

(2 citation statements)

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“…The data is then downsampled to a voxel spacing of 0.75 mm, and cropped according to the prostate boundaries which were delineated by two urologists in consensus. Following the method proposed in [15], the DCE-US tensor is unfolded in each mode, SVD is applied and truncated according to the estimated ranks. By considering the multidimensional nature of the DCE-US recording, we expect MLSVD to be a better approach to highlight the bubble kinetics and hence, improve the classification performance of all of the CUDI features.…”

Section: B Preprocessing and Denosingmentioning

confidence: 99%

“…Due to the flattening process, the information regarding the voxel locations is lost. In [15], multilinear singular value decomposition (MLSVD) is proposed as an extension that generalizes the concept of SVD to multiple dimensions. The volumetric information that is retained by considering the tensor format of the recording is expected to improve the classification performance of the TIC dispersion modeling.…”

Section: Introductionmentioning

confidence: 99%

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Multiparametric ultrasound and machine learning for prostate cancer localization

Chen

Calis

Wijkstra

et al. 2022

2022 30th European Signal Processing Conference (EUSIPCO)

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A cost-effective, widely available, and practical diagnostic imaging tool for prostate cancer (PCa) localization is still lacking. Recently, the contrast-ultrasound dispersion imaging (CUDI) technique has been developed for PCa localization by quantifying dynamic contrast-enhanced ultrasound (DCE-US) acquisitions. Tissue stiffness is an additional PCa biomarker that can be quantified by ultrasound shear-wave elastography (SWE). In this work, a dedicated preprocessing of 3D DCE-US acquisitions was investigated by using multilinear singular value decomposition (MLSVD), aiming at improving the CUDI performance. Moreover, the diagnostic potential of a multiparametric ultrasound imaging approach combining 3D CUDI features with SWE tissue elasticity for clinically significant (cs)PCa localization was evaluated by comparison with the histopathological outcome of systematic biopsies. In this multiparametric approach, the performance of five classifiers was evaluated and compared for biopsy-region csPCa classification. The classification performance was assessed by the area under the Receiver Operating Characteristics curve (AUC) in a k-fold cross validation fashion comprising sequential floating forward selection of the features. The combination of CUDI features with MLSVD preprocessing and SWE elasticity yielded the best AUC=0.87 for csPCa localization. Our results suggest 3D multiparametric ultrasound imaging approach combing a dedicated preprocessing step to be a useful tool for PCa diagnostics.

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Section: B Preprocessing and Denosingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%