Comment on the paper: 'novel approach to estimate kidney and cyst volumes using mid-slice magnetic resonance images in polycystic kidney disease'

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Preliminary Computational Framework to Map MRI-Derived Markers to predict Response to Cardiac Resynchronization Therapy

Prediction of the response to cardiac resynchronization therapy (CRT) is still uncertain. On our previous CRT clinical research, we have found that a decrease in the ratio between the two principal axes of the 3D trajectory of the electrode at the pacing site (S1/S2) recorded before and after pacing could define a marker between responders and non-responders to CRT. The aim of this work is to design a framework to map the S1/S2 marker on the 3D ventricular anatomy as a preliminary test to verify if the concept of the S1/S2 may predict the response to CRT in a pre-implant scenario. Based on MR images of a CRT candidate, the 3D mesh of the left ventricle geometry is constructed. Using image registration we are able to track the deformation of the mesh throughout the cardiac cycle and to compute the trajectory of each point of the mesh. Then the S1/S2 is calculated for every trajectory and mapped on a 3D geometry representation. We have applied this framework to one CRT patient, highlighting that in the area in which the electrode was placed the S1/S2 was low. This value suggests a poor possibility of a pacing-induced decrease for the S1/S2 ratio after implant. Consistently the patient was classified as non-responder at the clinical follow-up. Ongoing work focuses on the clinical validation of S1/S2 as a tool for the prediction of CRT response and the acquisition of MR data of potential candidates to CRT for the assessment of the presented framework

An Automatic Framework for the Non-rigid Alignment of Electroanatomical Maps and Preoperative Anatomical Scans in Atrial Fibrillation

In atrial fibrillation, electro-anatomical maps (EAM) are used for ablation guidance. Yet, the anatomy reconstructed by the navigation system is known to be poorly accurate. This makes catheter navigation challenging and, as such, might affects ablation’s outcome. To ease navigation, existing systems allow co-registering EAMs with pre-operative MR scans by rigidly matching a set of manual landmarks. Nevertheless, the deformation between the two datasets is highly non-rigid. The aim of this work was therefore to develop a framework for the non-rigid alignment of EAMs and anatomical scans to improve ablation guidance

A Computational Framework to Benchmark Basket Catheter Guided Ablation

Rotor ablation guided by basket catheter mapping has shown to be beneficial for AF ablation. Yet, the initial excitement was mitigated by a growing skepticism due to the difficulty in verifying the protocol in multicenter studies. Overall, the underlying assumptions of rotor ablation require further verification. The aim of this study was therefore to test such hypotheses by using computational modeling. A detailed 3D left atrial geometry of an AF patient was segmented from a pre-operative MR scan. Atrial activation was simulated on the 3D anatomy using the monodomain approach and a variant of the Courtemanche action potential model. Ablated tissue was assigned zero conductivity. Reentry was successfully initialized by applying a single suitably delayed extra stimulus. Unipolar electrograms were computed at the simulated electrode positions. The final dataset was generated by varying location of reentry and catheter position within the LA. The effect of inter-electrode distance and distance to the atrial wall was studied in relation to the ability to recover rotor trajectory, as computed by a novel algorithm described here. The effect of rotor ablation was also assessed