In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation

Purpose: Using 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts related to relative motion. Our approach demonstrates the feasibility for bimodal IVOCT and MPI in-vitro experiments. Material and Methods: During IVOCT imaging of a stenosis phantom the catheter is tracked using MPI. A 4D trajectory of the catheter tip is determined from the MPI data using center of mass sub-voxel strategies. A custom built IVOCT imaging adapter is used to perform different catheter motion profiles: no motion artifacts, motion artifacts due to catheter bending, and heart beat motion artifacts. Two IVOCT volume reconstruction methods are compared qualitatively and quantitatively using the DICE metric and the known stenosis length. Results: The MPI-tracked trajectory of the IVOCT catheter is validated in multiple repeated measurements calculating the absolute mean error and standard deviation. Both volume reconstruction methods are compared and analyzed whether they are capable of compensating the motion artifacts. The novel approach of MPI-guided catheter tracking corrects motion artifacts leading to a DICE coefficient with a minimum of 86% in comparison to 58% for a standard reconstruction approach. Conclusions: IVOCT catheter tracking with MPI in real time is an auspicious method for radiation free MPI-guided IVOCT interventions. The combination of MPI and IVOCT can help to reduce motion artifacts due to catheter bending and heart beat for optimized IVOCT volume reconstructions.Comment: 19 pages, 11 figure

Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography

Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification

Identification of the PTPN22 functional variant R620W as susceptibility genetic factor for giant cell arteritis

Objective: To analyse the role of the PTPN22 and CSK genes, previously associated with autoimmunity, in the predisposition and clinical phenotypes of giant cell arteritis (GCA). Methods: Our study population was composed of 911 patients diagnosed with biopsy-proven GCA and 8136 unaffected controls from a Spanish discovery cohort and three additional independent replication cohorts from Germany, Norway and the UK. Two functional PTPN22 polymorphisms (rs2476601/R620W and rs33996649/R263Q) and two variants of the CSK gene (rs1378942 and rs34933034) were genotyped using predesigned TaqMan assays. Results: The analysis of the discovery cohort provided evidence of association of PTPN22 rs2476601/R620W with GCA (PFDR=1.06E-04, OR=1.62, CI 95% 1.29 to 2.04). The association did not appear to follow a specific GCA subphenotype. No statistically significant differences between allele frequencies for the other PTPN22 and CSK genetic variants were evident either in the case/control or in stratified case analysis. To confirm the detected PTPN22 association, three replication cohorts were genotyped, and a consistent association between the PTPN22 rs2476601/R620W variant and GCA was evident in the overall meta-analysis (PMH=2.00E-06, OR=1.51, CI 95% 1.28 to 1.79). Conclusions: Our results suggest that the PTPN22 polymorphism rs2476601/R620W plays an important role in the genetic risk to GCA