Combination of high resolution MRI with 3D-printed needle guides for ex vivo myocardial biopsies.

Magnetic resonance imaging (MRI) provides a multitude of techniques to detect and characterize myocardial infarction. To correlate MRI findings with histology, in most cases terminal animal studies are performed; however, precise extraction and spatial correlation of myocardial tissue samples to MRI image data is difficult. In this proof of concept study, we present a 3D-printing technique to facilitate the extraction of tissue samples from myocardial regions. Initially, seven pig hearts embedded in formaldehyde were imaged on a clinical 3 T system to define biopsy targets on high resolution ex vivo images. Magnitude images and R2*-maps acquired with a 3D multi-echo gradient echo sequence and 0.58 mm isotropic resolution were used to create digital models of the cardiac anatomy. Biopsy guides were 3D-printed to steer the extraction of myocardial samples. In total, 61 tissue samples were extracted with an average offset of the tissue sample location from the target location of 0.59 ± 0.36 mm. This offset was not dependent on the distance of the target point to the epicardial surface. Myocardial tissue could be extracted from all samples. The presented method enables extraction of myocardial tissue samples that are selected by ex vivo MRI with submillimeter precision.

RF-induced heating of interventional devices at 23.66 MHz.

OBJECTIVE: Low-field MRI systems are expected to cause less RF heating in conventional interventional devices due to lower Larmor frequency. We systematically evaluate RF-induced heating of commonly used intravascular devices at the Larmor frequency of a 0.55 T system (23.66 MHz) with a focus on the effect of patient size, target organ, and device position on maximum temperature rise. MATERIALS AND METHODS: To assess RF-induced heating, high-resolution measurements of the electric field, temperature, and transfer function were combined. Realistic device trajectories were derived from vascular models to evaluate the variation of the temperature increase as a function of the device trajectory. At a low-field RF test bench, the effects of patient size and positioning, target organ (liver and heart) and body coil type were measured for six commonly used interventional devices (two guidewires, two catheters, an applicator and a biopsy needle). RESULTS: Electric field mapping shows that the hotspots are not necessarily localized at the device tip. Of all procedures, the liver catheterizations showed the lowest heating, and a modification of the transmit body coil could further reduce the temperature increase. For common commercial needles no significant heating was measured at the needle tip. Comparable local SAR values were found in the temperature measurements and the TF-based calculations. CONCLUSION: At low fields, interventions with shorter insertion lengths such as hepatic catheterizations result in less RF-induced heating than coronary interventions. The maximum temperature increase depends on body coil design.

Assessing critical temperature dose areas in the kidney by magnetic resonance imaging thermometry in an ex vivo Holmium:YAG laser lithotripsy model.

PURPOSE: We aimed to assess critical temperature areas in the kidney parenchyma using magnetic resonance thermometry (MRT) in an ex vivo Holmium:YAG laser lithotripsy model. METHODS: Thermal effects of Ho:YAG laser irradiation of 14 W and 30 W were investigated in the calyx and renal pelvis of an ex vivo kidney with different laser application times (tL) followed by a delay time (tD) of tL/tD = 5/5 s, 5/10 s, 10/5 s, 10/10 s, and 20/0 s, with irrigation rates of 10, 30, 50, 70, and 100 ml/min. Using MRT, the size of the area was determined in which the thermal dose as measured by the Cumulative Equivalent Minutes (CEM43) method exceeded a value of 120 min. RESULTS: In the calyx, CEM43 never exceeded 120 min for flow rates ≥ 70 ml/min at 14 W, and longer tL (10 s vs. 5 s) lead to exponentially lower thermal affection of tissue (3.6 vs. 21.9 mm2). Similarly at 30 W and ≥ 70 ml/min CEM43 was below 120 min. Interestingly, at irrigation rates of 10 ml/min, tL = 10 s and tD = 10 s CEM43 were observed > 120 min in an area of 84.4 mm2 and 49.1 mm2 at tD = 5 s. Here, tL = 5 s revealed relevant thermal affection of 29.1 mm2 at 10 ml/min. CONCLUSION: We demonstrate that critical temperature dose areas in the kidney parenchyma were associated with high laser power and application times, a low irrigation rate, and anatomical volume of the targeted calyx.

Radio-frequency induced heating of intra-cranial EEG electrodes: The more the colder?

Many neurological disorders are analyzed and treated with implantable electrodes. Many patients with such electrodes have to undergo MRI examinations - often unrelated to their implant - at the risk of radio-frequency induced heating. The number of electrode contact sites of these implants keeps increasing due to improvements in manufacturing and computational algorithms. Electrode grids with multiple receive channels couple to the RF fields present in MRI, but, due to their proximity, a combination of leads has a coupling response which is not a superposition of the individual leads' response. To investigate the problem of RF-induced heating of coupled multi-lead implants, temperature mapping was performed on a set of intra-cranial electroencephalogram (icEEG) electrode grid prototypes with increasing number of contact sites (1-16). Additionally, electric field measurements were used to investigate the radio-frequency heating characteristics of the implants in different media combinations, simulating the device being partially immersed inside the patient. MR measurements show RF-induced heating up to 19.6 K for the single electrode, reducing monotonically with larger number of contact sites to a minimum of 0.9 K for the largest grid. The SAR calculated from temperature measurements agrees well with electric field mapping: The same trend is visible for different insertion lengths, however, the energy dissipated by the whole implant varies with the grid size and insertion length. Thus, in the tested circumstances, a larger electrode number either reduced or had a similar risk of RF induced heating, indicating, that the size of electrode grids is a design parameter, which can be used to change an implants RF response and in turn to reduce the risk of RF induced heating and improve the safety of patient with neuro-implants undergoing MRI examinations.

Improvement of diffusion weighted MRI by practical B0 homogenization for head & neck cancer patients undergoing radiation therapy.

BACKGROUND: MRI is a frequently used tool in radiation therapy planning. For MR-based tumor segmentation, diffusion weighted imaging plays a major role, which can fail due to excessive image artifacts for head and neck cancer imaging. Here, an easy-to-use setup is presented for imaging of head and neck cancer patients in a radiotherapy thermoplastic fixation mask. METHODS: In a prospective head and neck cancer study, MRI data of 29 patients has been acquired at 3 different time points during radiation treatment. The data was analyzed with respect to Nyquist ghosting artifacts in the diffusion images in conventional single shot and readout segmented EPI sequences. For 9 patients, an improved setup with water bags for B0 homogenization was used, and the impact on artifact frequency was analyzed. Additionally, volunteer measurements with B0 fieldmaps are presented. RESULTS: The placement of water bags to the sides of the head during MRI measurements significantly reduces artefacts in diffusion MRI. The number of artifact-free images in readout segmented EPI increased from 74% to 95% of the cases. Volunteer measurements showed a significant increase in B0 homogeneity across slices (head foot direction) as well as within each slice. CONCLUSIONS: The placement of water bags for B0 homogenization is easy to implement, cost-efficient and does not impact patient comfort. Therefore, if very sophisticated soft- or hardware solutions are not present at a given site, or cannot be implemented due to restrictions from the thermoplastic mask, this is an excellent alternative to reduce artifacts in diffusion weighted imaging.

Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis.

BACKGROUND: Automatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer. METHODS: Head&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (ktrans, ve) and diffusion (ADC) measurements at 3 time points before and during radiochemotherapy. The 7 different MRI contrasts (input channels) and manually defined gross tumor volumes (primary tumor and lymph node metastases) were used to train CNNs for lesion segmentation. A reference CNN with all input channels was compared to individually trained CNNs where one of the input channels was left out to identify which MRI contrast contributes the most to the tumor segmentation task. A statistical analysis was employed to account for random fluctuations in the segmentation performance. RESULTS: The CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSCGTV-T = 5.7% for primary tumor and ΔDSCGTV-Ln = 5.8% for lymph node metastases compared to the network containing all input channels. Overall, the ADC input channel showed the least impact on segmentation performance, with ΔDSCGTV-T = 2.4% for primary tumor and ΔDSCGTV-Ln = 2.2% respectively. CONCLUSIONS: We developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance. TRIAL REGISTRATION: The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction.

Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (ktrans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use.