Kobe Bamps

Background and ObjectivesCharcot-Marie-Tooth neuropathy type 1A (CMT1A) is a slowly progressive demyelinating neuropathy with distal muscle weakness and atrophy. Sensitive, objective outcome measur...

Background: Differentiating myogenic from neurogenic neuromuscular diseases (NMDs) can be clinically challenging. While quantitative muscle MRI (qMRI) with proton density fat fraction (PDFF,%) quantifies fat replacement, it misses micro‐spatial patterns linked to underlying pathology. This study investigates whether qMRI with 3D radiomic texture analysis (TA) might improve differentiation between myogenic and neurogenic diseases, using myotonic dystrophy type 1 (DM1) and Charcot–Marie‐Tooth neuropathy type 1A (CMT1A) as proof‐of‐concept models. Methods: Thirty‐three adults with DM1, 33 with CMT1A, and 33 matched healthy controls were included. qMRI on a 3T Philips Achieva system using a 6‐point Dixon sequence generated PDFF(%) maps of the lower limbs, and a convolutional neural network performed 3D segmentation of 28 lower limb muscles. We extracted macroscopic features, including muscle volume, asymmetry, and disto‐proximal gradients, alongside micro‐spatial radiomic features (entropy, contrast, homogeneity) to quantify tissue heterogeneity. Results: Both patient cohorts exhibited higher PDFF(%) in all lower limb muscles compared to controls ( p < 0.001). DM1 predominantly involved the posterior compartment, while CMT1A targeted the anterolateral compartment with significantly steeper disto‐proximal fat gradients ( p < 0.05). TA revealed higher entropy and contrast, and lower homogeneity in CMT1A compared to DM1, reflecting a more reticular pattern of fat infiltration vs. the confluent pattern in DM1. Conclusion: In this proof‐of‐concept study, 3D radiomic texture analysis of PDFF(%) maps revealed distinct spatial patterns of fat replacement in DM1 and CMT1A. Integrating radiomic and conventional qMRI features may enhance the non‐invasive distinction between DM1 and CMT1A and warrants further investigation in a broader range of NMDs.

Mycobacterium avium pulmonary disease is an emerging global health challenge for which drug development remains limited by preclinical models that rely on laboratory strains and invasive endpoint analyses. Here, we compared recent clinical M. avium isolates with the reference strain ATCC 700898 across macrophage, Galleria mellonella , and murine infection models and evaluated longitudinal micro-computed tomography (µCT) as a non-invasive tool to monitor disease progression and treatment response. While extracellular growth rates were comparable, clinical isolates demonstrated enhanced host-associated fitness and induced higher bacterial burdens and more severe pulmonary pathology in mice than the reference strain. These strain-dependent differences were detected by quantitative µCT imaging. Using the hypervirulent isolate MYC_0069, we further show that clarithromycin monotherapy and standard-of-care triple therapy significantly reduced bacterial burden and lung pathology. Together, these findings establish a clinically relevant chronic M. avium model that combines clinical isolates with longitudinal imaging to enable preclinical anti-mycobacterial drug evaluation in vivo .

PurposeVirtual reality (VR) and augmented reality (AR) have led to significant advancements in cardiac preoperative planning, shaping the world in profound ways. A noticeable gap exists in the availability of a comprehensive multi-user, multi-device mixed reality application that can be used in a multidisciplinary team meeting.ApproachA multi-user, multi-device mixed reality application was developed, supporting AR and VR implementations. Technical validation involved a standardized testing protocol and comparison of AR and VR measurements regarding absolute error and time. Preclinical validation engaged experts in interventional cardiology, evaluating the clinical applicability prior to clinical validation. Clinical validation included patient-specific measurements for five patients in VR compared with standard computed tomography (CT) for preoperative planning. Questionnaires were used at all stages for subjective evaluation.ResultsTechnical validation, including 106 size measurements, demonstrated an absolute median error of 0.69 mm (0.25 to 1.18 mm) compared with ground truth. The time to complete the entire task was 892±407 s on average, with VR measurements being faster than AR (804±483 versus 957±257 s, P=0.045). On clinical validation of five preoperative patients, there was no statistically significant difference between paired CT and VR measurements (0.58 [95% CI, −1.58 to 2.74], P=0.586). Questionnaires showcased unanimous agreement on the user-friendly nature, effectiveness, and clinical value.ConclusionsThe mixed reality application, validated through technical, preclinical, and clinical assessments, demonstrates precision and user-friendliness. Further research of our application is needed to validate the generalizability and impact on patient outcomes.

PurposeThe objective of this study is to evaluate the accuracy of an augmented reality (AR) system in improving guidance, accuracy, and visualization during the subxiphoidal approach for epicardial ablation.ApproachAn AR application was developed to project real-time needle trajectories and patient-specific 3D organs using the Hololens 2. Additionally, needle tracking was implemented to offer real-time feedback to the operator, facilitating needle navigation. The AR application was evaluated through three different experiments: examining overlay accuracy, assessing puncture accuracy, and performing pre-clinical evaluations on a phantom.ResultsThe results of the overlay accuracy assessment for the AR system yielded 2.36±2.04 mm. Additionally, the puncture accuracy utilizing the AR system yielded 1.02±2.41 mm. During the pre-clinical evaluation on the phantom, needle puncture with AR guidance showed 7.43±2.73 mm, whereas needle puncture without AR guidance showed 22.62±9.37 mm.ConclusionsOverall, the AR platform has the potential to enhance the accuracy of percutaneous epicardial access for mapping and ablation of cardiac arrhythmias, thereby reducing complications and improving patient outcomes. The significance of this study lies in the potential of AR guidance to enhance the accuracy and safety of percutaneous epicardial access.

Minimally invasive interventional cardiology procedures can be guided by fluoroscopic 2-D projections from a C-arm device. However, 2-D projections typically offer a restricted view on the anatomical structures of interest. An augmented reality (AR) view could realises an enhanced understanding of the anatomy. . Therefore, we propose a deep-learning framework for automated detection of semi-opaque markers in fluoroscopy, which enables prediction of the C-arm pose with respect to the preinterventional CT. A convolutional neural network is trained on datasets from both synthetically generated X-ray and real X-ray images to label the markers. The 3-D/2-D point correspondences of the detected markers are used to determine the pose of the C-ARM. The detection of semi-opaque markers yielded an accuracy of 0.60 ± 0.74 mm on real 2D fluoroscopy. The pose of the X-ray source was estimated within a translation and rotation error of 1.01 ± 0.17 mm and 0.30 ± 0.39^◦ respectively. Finally, the target registration error was 1.88 ± 0.30 mm for a realistic percutaneous-epicardial-access scenario. The presented approach can in real-time estimate the C-arm pose with respect to the pre-interventional CT and intra-interventional X-ray image which is a key element in the practical application of AR in fluoroscopy guided interventions.

Nowadays, navigating therapeutic catheters takes place under 2D fluoroscopic imaging. This requires considerable training of the clinician and exposes him/her to X-ray radiation. Researchers have increasingly investigated alternative sensing techniques. In this respect Fiber Bragg Grating (FBG)-based shape sensing is gaining popularity. This paper proposes two approaches to fuse FBG with fluoroscropy, improve the understanding of the 3- dimensional shape while reducing fluoroscopy use. This paper proposes 2 FBG-fluoroscopy fusion approaches that combine fluoroscopy and FBG measurements. A comparison is performed between 3D shape reconstructions based on biplane fluoroscopy, rigidly fused multi-core FBG and dynamically fused FBG shape reconstruction. To verify the performance of the different approaches experiments were performed with custom made catheter in a CathLab on 3D printed tubes with known ground truth shape. The experiments showed overall acceptable errors for the targeted application with a maximum below 2 mm. The error of shape reconstruction through biplane fluoroscopy, rigid and dynamic fusion were found to be 1.51+-0.04 mm, 1.77+-0.29 mm and 1.47+-0.15 mm, respectively. Thus, FBG-fluoroscopy fusion offers comparable results to fluoroscopy and may substantially reduce the radiation dose through optimal acquisition frequency.