Both Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming. To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases. We performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning. (1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent. (3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion. Automatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

Biocompatible polymer films demonstrating excellent thermal stability are highly desirable for high-temperature (>250 °C) applications, especially in the bioelectronic encapsulation domain. Parylene, as an organic thin film, is a well-established polymer material exhibiting excellent barrier properties and is often the material of choice for biomedical applications. This work investigated the thermal impact on the bulk properties of four types of parylene films: parylene N, C, VT4, and AF4. The films, deposited using the standard Gorham process, were analyzed at varying annealing temperatures from room temperature up to 450 °C. Thermal properties were identified by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) methods, while X-ray diffraction (XRD) analysis showed the effect of high-temperature exposure on the structural properties. In addition to thermal and structural analysis, the barrier properties were measured through the helium transmission rate (HTR) and the water vapor transmission rate (WVTR). Fluorinated parylene films were confirmed to be exceptional materials for high-temperature applications. Parylene AF4 film, 25um thick, demonstrated excellent barrier performance after 300 °C exposure, with an HTR and a WVTR of 12.18 × 10³ cm³ (STP) m⁻² day⁻¹ atm⁻¹ and 6.6 g m⁻² day⁻¹, respectively.

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification.

Background: Eight commercially available percutaneous transluminal coronary angioplasty (PTCA), including semi-compliant and non-compliant balloons, have been assessed in detail on their tip, balloon, shaft, RX-Port, and hypotube design. Important performance characteristics such as tip deformation, balloon elongation, and deflation rate have been quantified. Methods: Five catheters of each model were evaluated during various tests. The robustness of the tips was evaluated through compression, measuring any occurrence of damage. The longitudinal growth of the balloons was recorded during inflation up to Rated Burst Pressure (RBP). The forces required to move the catheter forward and retract it into the guide catheter were measured in a simulated use test setup. The deflation behavior was studied by measuring extracted contrast media over time. Furthermore, balloon compliance and catheter dimensions were investigated. Results: The outer dimensions of the catheter were found to be smallest at the hypotube (0.59–0.69 mm) and highest at the balloon, respectively, the crossing profile (0.9–1.2 mm). The tip diameter increased after compression by 1.7–22%. Cross-sections of the folded balloons revealed a tri- and two-fold, respectively. The measured balloon elongation ranged from 0.6 to 2.0 mm. After the inflation of the balloon, an increase in friction between the guide wire and the catheter was observed on four catheters. A maximum increase of 0.12 N to 1.07 N was found. Cross-sections of the RX-Port revealed a semicircular-shaped inflation lumen and a circular guide wire lumen. The measured deflation rate ranged from 0.004 to 0.013 µL/s, resulting in an estimated balloon deflation time of 10.2–28.1 s. Conclusion: This study provides valuable insights into the design characteristics of RX PTCA balloon catheters, which can contribute to facilitating the development of improved catheter designs and enhancing clinical outcomes. Distinctions between SC and NC catheters, such as balloon performance and dimensions, are evident. It is important to note that no single catheter excels in all aspects, as each possesses unique strengths. Therefore, it is essential to consider individual intervention requirements when selecting a catheter. The research also identifies specific catheter weaknesses, such as reduced wall thickness, fringes at the tip, and reduced performance characteristics.

Objective: Cardiac pacemakers are powered by batteries, which become exhausted after a few years. This is a problem in particular for leadless pacemakers since they are difficult to explant. Thus, autonomous devices powered by energy harvesters are desired. Methods: We developed an energy harvester for endocardial implantation. The device contains a microgenerator to convert a flexible turbine runner's rotation into electrical energy. The turbine runner is driven by the intracardiac blood flow, a magnetic coupling allows hermetical sealing. The energy harvester has a volume of 0.34 cm $^3$ and a weight of 1.3 g. Computational simulations were performed to assess the hemodynamic impact of the implant. The device was studied on a mock circulation and an in-vivo trial was performed in a domestic pig. Results: In this article we show that an energy harvester with a 2-bladed 14 mm diameter turbine runner delivers 10.2 $\pm$ 4.8 μW under realistic conditions (heart rate 80/min, stroke volume 75 ml) on the bench. An increased output power (> 80 μW) and power density (237.1 μW/cm $^3$ ) can be achieved by higher stroke volumes, increased heart rates or larger turbine runners. The device was successfully implanted in vivo. Conclusion: The device is the first flow-based energy harvester suitable for catheter-based implantation and provides enough energy to power a leadless pacemaker. Significance: The high power density, the small volume, and the flexible turbine runner blades facilitate the integration of the energy harvester in a pacemaker. This would allow overcoming the need for batteries in leadless pacemakers.

Purpose: Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools. Methods: A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume. Results: The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed. Conclusion: An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future.