On the reliable measurement of specific absorption rates and intrinsic loss parameters in magnetic hyperthermia materials

\u3cp\u3eIn the clinical application of magnetic hyperthermia, the heat generated by magnetic nanoparticles in an alternating magnetic field is used as a cancer treatment. The heating ability of the particles is quantified by the specific absorption rate (SAR), an extrinsic parameter based on the clinical response characteristic of power delivered per unit mass, and by the intrinsic loss parameter (ILP), an intrinsic parameter based on the heating capacity of the material. Even though both the SAR and ILP are widely used as comparative design parameters, they are almost always measured in non-adiabatic systems that make accurate measurements difficult. We present here the results of a systematic review of measurement methods for both SAR and ILP, leading to recommendations for a standardised, simple and reliable method for measurements using non-adiabatic systems. In a representative survey of 50 retrieved datasets taken from published papers, the derived SAR or ILP was found to be more than 5% overestimated in 24% of cases and more than 5% underestimated in 52% of cases.\u3c/p\u3

EFSUMB Young Investigator Award 2019

The winner at EUROSON 2019 of the Young Investigator 3000 euro prize was Rogier R Wildeboer, The Netherlands for the abstract entitled : 3 D Multiparametric Ultrasound for Prostate Cancer Diagnosi

Shear-wave imaging of viscoelasticity using local impulse response identification

\u3cp\u3eImaging technologies that allow assessment of the elastic properties of soft tissue provide clinicians with an important asset for several diagnostic applications. A quantitative measure of stiffness can be obtained by shear-wave (SW) elasticity imaging, a method that uses acoustic radiation force to produce laterally-propagating shear waves that can be tracked to obtain the velocity, which in turn is related to the shear modulus. If one considers the medium to be purely elastic, its local shear modulus can be estimated by determining the local SW velocity. However, this assumption does not hold for many tissue types, whenever the shear viscosity plays an important role. In fact, there is increasing evidence that viscosity itself could be an important marker for malignancy [1]. In this work, we therefore aim at providing a joint local estimate of tissue elasticity and viscosity based on SW elastography.\u3c/p\u3

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

\u3cp\u3eProstate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.\u3c/p\u3

Viscoelasticity mapping by identification of local shear wave dynamics

\u3cp\u3eEstimation of soft tissue elasticity is of interest in several clinical applications. For instance, tumors and fibrotic lesions are notoriously stiff compared with benign tissue. A fully quantitative measure of lesion stiffness can be obtained by shear wave (SW) elastography. This method uses an acoustic radiation force to produce laterally propagating SWs that can be tracked to obtain the velocity, which in turn is related to Young's modulus. However, not only elasticity, but also viscosity plays an important role in the propagation process of SWs. In fact, viscosity itself is a parameter of diagnostic value for the detection and characterization of malignant lesions. In this paper, we describe a new method that enables imaging viscosity from SW elastography by local model-based system identification. By testing the method on simulated data sets and performing in vitro experiments, we show that the ability of the proposed technique to generate parametric maps of the viscoelastic material properties from SW measurements, opening up new possibilities for noninvasive tissue characterization.\u3c/p\u3

Shear wave viscoelasticity imaging using local system identification

Tissue elasticity is an important parameter which relates to the pathological state of soft tissue. Fibrotic lesions or malignant tumors are known to be notoriously stiff compared to benign tissue. Shear wave elastography can provide a fully quantitative measure of lesion stiffness by estimating the speed at which acoustically induced shear waves propagate through the material. This speed is in turn related to the Young's modulus. In soft tissue, elasticity is generally accompanied by viscosity, leading to dispersion of the shear wave. For the detection and characterization of malignant lesions, viscosity has in fact diagnostic value. Here, we describe a new method that enables imaging not only elasticity but also viscosity from shear wave elastography by local model-based system identification. We show that the proposed method can be applied effectively to standard shear wave acquisitions, and is able to generate high-resolution parametric maps of the viscoelastic material properties in an in-vitro setting

3-D multi-parametric contrast-enhanced ultrasound for the prediction of prostate cancer

Trans-rectal ultrasound-guided 12-core systematic biopsy (SBx) is the standard diagnostic pathway for prostate cancer (PCa) because of a lack of sufficiently accurate imaging. Quantification of 3-D dynamic contrast-enhanced ultrasound (US) might open the way for a targeted procedure in which biopsies are directed at lesions suspicious on imaging. This work describes the expansion of contrast US dispersion imaging algorithms to 3-D and compares its performance against malignant and benign disease. Furthermore, we examined the feasibility of a multi-parametric approach to predict SBx-core outcomes using machine learning. An area under the receiver operating characteristic (ROC) curve of 0.76 and 0.81 was obtained for all PCa and significant PCa, respectively, an improvement over previous US methods. We found that prostatitis, in particular, was a source of false-positive readings

Machine learning for the prediction of prostate cancer biopsy based on 3D dynamic contrast-enhanced ultrasound quantification:2018 IEEE International Ultrasonics Symposium (IUS)

Non-targeted transrectal-ultrasound-guided 12-core systematic biopsy (SBx) is the current guideline-recommended clinical pathway for prostate cancer (PCa) diagnosis, despite being associated with a risk of complications as well as un-derdiagnosis or overtreatment. Quantification algorithms for dynamic contrast-enhanced ultrasound (DCE-US) have shown good potential for PCa localisation in two dimensions (2D), and a few have recently been expanded to 3D. In this work, we present a 3D implementation of all estimators in the contrast ultrasound dispersion imaging (CUDI) family and exploit combinations of the extracted parameters to predict individual SBx-core outcomes. We show that machine-learning approaches can improve the classification performance compared to individual CUDI parameters and foresee potential for further development in image-based PCa localisation