Hepatic tumor diagnosis by analysing dense transport fields in contrast-enhanced ultrasound

International audienceDynamic contrast agent enhanced ultrasound (DCEUS) is considered as a safe, noninvasive, accurate, and economic tool for analysing blood perfusion of various organs [1]. Gas-filled mi-crobubble contrast agents are used as intravascular flow tracers. In this study, a new methodology is proposed to quantify the divergence (i.e sources, sinks), curl (i.e sheering) and amplitude in the apparent microbubble transports during the bolus arrival. The efficiency of proposed methodology is evaluated in-vivo, for the classification of focal nodular hyperplasia (FNH) and inflammatory hepatic adenomas (I-HCA)

A framework for continuous target tracking during MR-guided high intensity focused ultrasound thermal ablations in the abdomen

Scatterplot showing percentage changes in stroke volume index (ΔSVI, %) and functional hemodynamic markers, Stroke Volume Variation (SVV, %) Pulse Pressure Variation (PPV, %), with the three tested tidal volumes (V T ), 6, 12 and 18 ml/kg during intra-abdominal hypertension. Solid line shows regression line between variables. (PDF 56 kb

Hepatic tumor diagnosis by analysing dense transport fields in contrast-enhanced ultrasound

International audienceDynamic contrast agent enhanced ultrasound (DCEUS) is considered as a safe, noninvasive, accurate, and economic tool for analysing blood perfusion of various organs [1]. Gas-filled mi-crobubble contrast agents are used as intravascular flow tracers. In this study, a new methodology is proposed to quantify the divergence (i.e sources, sinks), curl (i.e sheering) and amplitude in the apparent microbubble transports during the bolus arrival. The efficiency of proposed methodology is evaluated in-vivo, for the classification of focal nodular hyperplasia (FNH) and inflammatory hepatic adenomas (I-HCA)

A framework for continuous target tracking during MR-guided high intensity focused ultrasound thermal ablations in the abdomen

International audienceBackground: During lengthy magnetic resonance-guided high intensity focused ultrasound (MRg-HIFU) thermal ablations in abdominal organs, the therapeutic work-flow is frequently hampered by various types of physiological motion occurring at different time-scales. If left un-addressed this can lead to an incomplete therapy and/or to tissue damage of organs-at-risk. While previous studies focus on correction schemes for displacements occurring at a particular time-scale within the work-flow of an MRg-HIFU therapy, in the current work we propose a motion correction strategy encompassing the entire work-flow.Methods: The proposed motion compensation framework consists of several linked components, each being adapted to motion occurring at a particular time-scale. While respiration was addressed through a fast correction scheme, long term organ drifts were compensated using a strategy operating on time-scales of several minutes. The framework relies on a periodic examination of the treated area via MR scans which are then registered to a reference scan acquired at the beginning of the therapy. The resulting displacements were used for both on-the-fly re-optimization of the interventional plan and to ensure the spatial fidelity between the different steps of the therapeutic work-flow. The approach was validated in three complementary studies: an experiment conducted on a phantom undergoing a known motion pattern, a study performed on the abdomen of 10 healthy volunteers and during 3 in-vivo MRg-HIFU ablations on porcine liver.Results: Results have shown that, during lengthy MRg-HIFU thermal therapies, the human liver and kidney can manifest displacements that exceed acceptable therapeutic margins. Also, it was demonstrated that the proposed framework is capable of providing motion estimates with sub-voxel precision and accuracy. Finally, the 3 successful animal studies demonstrate the compatibility of the proposed approach with the work-flow of an MRg-HIFU intervention under clinical conditions.Conclusions: In the current study we proposed an image-based motion compensation framework dedicated to MRg-HIFU thermal ablations in the abdomen, providing the possibility to re-optimize the therapy plan on-the-fly with the patient on the interventional table. Moreover, we have demonstrated that even under clinical conditions, the proposed approach is fully capable of continuously ensuring the spatial fidelity between the different phases of the therapeutic work-flow

Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects

OBJECTIVE: This study aimed to develop and validate an automated artificial intelligence (AI)-driven quantification of pleural plaques in a population of retired workers previously occupationally exposed to asbestos. METHODS: CT scans of former workers previously occupationally exposed to asbestos who participated in the multicenter APEXS (Asbestos PostExposure Survey) study were collected retrospectively between 2010 and 2017 during the second and the third rounds of the survey. A hundred and forty-one participants with pleural plaques identified by expert radiologists at the 2nd and the 3rd CT screenings were included. Maximum Intensity Projection (MIP) with 5 mm thickness was used to reduce the number of CT slices for manual delineation. A Deep Learning AI algorithm using 2D-convolutional neural networks was trained with 8280 images from 138 CT scans of 69 participants for the semantic labeling of Pleural Plaques (PP). In all, 2160 CT images from 36 CT scans of 18 participants were used for AI testing versus ground-truth labels (GT). The clinical validity of the method was evaluated longitudinally in 54 participants with pleural plaques. RESULTS: The concordance correlation coefficient (CCC) between AI-driven and GT was almost perfect (>0.98) for the volume extent of both PP and calcified PP. The 2D pixel similarity overlap of AI versus GT was good (DICE = 0.63) for PP, whether they were calcified or not, and very good (DICE = 0.82) for calcified PP. A longitudinal comparison of the volumetric extent of PP showed a significant increase in PP volumes (p < 0.001) between the 2nd and the 3rd CT screenings with an average delay of 5 years. CONCLUSIONS: AI allows a fully automated volumetric quantification of pleural plaques showing volumetric progression of PP over a five-year period. The reproducible PP volume evaluation may enable further investigations for the comprehension of the unclear relationships between pleural plaques and both respiratory function and occurrence of thoracic malignancy