Monitoring Blood-Brain Barrier Opening in Rats with a Preclinical Focused Ultrasound System.

The brain has a highly selective semipermeable blood barrier, termed the blood-brain barrier (BBB), which prevents the delivery of therapeutic macromolecular agents to the brain. The integration of MR-guided low-intensity pulsed focused ultrasound (FUS) with microbubble pre-injection is a promising technique for non-invasive and non-toxic BBB modulation. MRI can offer superior soft-tissue contrast and various quantitative assessments, such as vascular permeability, perfusion, and the spatial-temporal distribution of MRI contrast agents. Notably, contrast-enhanced MRI techniques with gadolinium-based MR contrast agents have been shown to be the gold standard for detecting BBB openings. This study outlines a comprehensive methodology involving MRI protocols and animal procedures for monitoring BBB opening in a rat model. The rat model provides the added benefit of jugular vein catheter utilization, which facilitates rapid medication administration. A stereotactic-guided preclinical FUS transducer facilitates the refinement and streamlining of animal procedures and MRI protocols. The resulting methods are characterized by reproducibility and simplicity, eliminating the need for specialized surgical expertise. This research endeavors to contribute to the optimization of preclinical procedures with rat models and encourage further investigation into the modulation of the BBB to enhance therapeutic interventions in neurological disorders.

Volumetric hyperthermia delivery using the ExAblate Body MR-guided focused ultrasound system.

OBJECTIVES: To investigate image-guided volumetric hyperthermia strategies using the ExAblate Body MR-guided focused ultrasound ablation system, involving mechanical transducer movement and sector-vortex beamforming. MATERIALS AND METHODS: Acoustic and thermal simulations were performed to investigate volumetric hyperthermia using mechanical transducer movement combined with sector-vortex beamforming, specifically for the ExAblate Body transducer. The system control in the ExAblate Body system was modified to achieve fast transducer movement and MR thermometry-based hyperthermia control, mechanical transducer movements and electronic sector-vortex beamforming were combined to optimize hyperthermia delivery. The experimental validation was performed using a tissue-mimicking phantom. RESULTS: The developed simulation framework allowed for a parametric study with varying numbers of heating spots, sonication durations, and transducer movement times to evaluate the hyperthermia characteristics for mechanical transducer movement and sector-vortex beamforming. Hyperthermic patterns involving 2-4 sequential focal spots were analyzed. To demonstrate the feasibility of volumetric hyperthermia in the system, a tissue-mimicking phantom was sonicated with two distinct spots through mechanical transducer movement and sector-vortex beamforming. During hyperthermia, the average values of Tmax, T10, Tavg, T90, and Tmin over 200 s were measured within a circular ROI with a diameter of 10 pixels. These values were found to be 8.6, 7.9, 6.6, 5.2, and 4.5 °C, respectively, compared to the baseline temperature. CONCLUSIONS: This study demonstrated the volumetric hyperthermia capabilities of the ExAblate Body system. The simulation framework developed in this study allowed for the evaluation of hyperthermia characteristics that could be implemented with the ExAblate MRgFUS system.

Machine learning-based automated scan prescription of lumbar spine MRI acquisitions.

PURPOSE: High quality scan prescription that optimally covers the area of interest with scan planes aligned to relevant anatomical structures is crucial for error-free radiologic interpretation. The goal of this project was to develop a machine learning pipeline for oblique scan prescription that could be trained on localizer images and metadata from previously acquired MR exams. METHODS: A novel Multislice Rotational Region-based Convolutional Neural Network (MS-R2CNN) architecture was developed. Based on this architecture, models for automated prescription sagittal lumbar spine acquisitions from axial, sagittal, and coronal localizer slices were trained. The automated prescription pipeline was integrated with the scanner console software and evaluated in experiments with healthy volunteers (N = 3) and patients with lower-back pain (N = 20). RESULTS: Experiments in healthy volunteers demonstrated high accuracy of automated prescription in all subjects. There was good agreement between alignment and coverage of manual and automated prescriptions, as well as consistent views of the lumbar spine at different positions of the subjects within the scanner bore. In patients with lower-back pain, the generated prescription was applied in 18 cases (90% of the total number). None of the cases required major adjustment, while in 11 cases (55%) there were minor manual adjustments to the generated prescription. CONCLUSIONS: This study demonstrates the ability of oriented object detection-based models to be trained to prescribe oblique lumbar spine MRI acquisitions without the need of manual annotation or feature engineering and the feasibility of using machine learning-based pipelines on the scanner for automated prescription of MRI acquisitions.

Technical advances in motion-robust MR thermometry.

Proton resonance frequency shift (PRFS) MR thermometry is the most common method used in clinical thermal treatments because of its fast acquisition and high sensitivity to temperature. However, motion is the biggest obstacle in PRFS MR thermometry for monitoring thermal treatment in moving organs. This challenge arises because of the introduction of phase errors into the PRFS calculation through multiple methods, such as image misregistration, susceptibility changes in the magnetic field, and intraframe motion during MRI acquisition. Various approaches for motion correction have been developed for real-time, motion-robust, and volumetric MR thermometry. However, current technologies have inherent trade-offs among volume coverage, processing time, and temperature accuracy. These tradeoffs should be considered and chosen according to the thermal treatment application. In hyperthermia treatment, precise temperature measurements are of increased importance rather than the requirement for exceedingly high temporal resolution. In contrast, ablation procedures require robust temporal resolution to accurately capture a rapid temperature rise. This paper presents a comprehensive review of current cutting-edge MRI techniques for motion-robust MR thermometry, and recommends which techniques are better suited for each thermal treatment. We expect that this study will help discern the selection of motion-robust MR thermometry strategies and inspire the development of motion-robust volumetric MR thermometry for practical use in clinics.

Shape-recovery of implanted shape-memory devices remotely triggered via image-guided ultrasound heating.

Shape-memory materials hold great potential to impart medical devices with functionalities useful during implantation, locomotion, drug delivery, and removal. However, their clinical translation is limited by a lack of non-invasive and precise methods to trigger and control the shape recovery, especially for devices implanted in deep tissues. In this study, the application of image-guided high-intensity focused ultrasound (HIFU) heating is tested. Magnetic resonance-guided HIFU triggered shape-recovery of a device made of polyurethane urea while monitoring its temperature by magnetic resonance thermometry. Deformation of the polyurethane urea in a live canine bladder (5 cm deep) is achieved with 8 seconds of ultrasound-guided HIFU with millimeter resolution energy focus. Tissue sections show no hyperthermic tissue injury. A conceptual application in ureteral stent shape-recovery reduces removal resistance. In conclusion, image-guided HIFU demonstrates deep energy penetration, safety and speed.

MR-guided focused ultrasound therapy of extra-abdominal desmoid tumors: a multicenter retrospective study of 105 patients.

OBJECTIVE: To assess the safety and efficacy of magnetic resonance-guided focused ultrasound (MRgFUS) for the treatment extra-abdominal desmoids. METHODS: A total of 105 patients with desmoid fibromatosis (79 females, 26 males; 35 ± 14 years) were treated with MRgFUS between 2011 and 2021 in three centers. Total and viable tumors were evaluated per patient at last follow-up after treatment. Response and progression-free survival (PFS) were assessed with (modified) response evaluation criteria in solid tumors (RECIST v.1.1 and mRECIST). Change in Numerical Rating Scale (NRS) pain and 36-item Short Form Health Survey (SF-36) scores were compared. Treatment-related adverse events were recorded. RESULTS: The median initial tumor volume was 114 mL (IQR 314 mL). After MRgFUS, median total and viable tumor volume decreased to 51 mL (95% CI: 30-71 mL, n = 101, p < 0.0001) and 29 mL (95% CI: 17-57 mL, n = 88, p < 0.0001), respectively, at last follow-up (median: 15 months, 95% CI: 11-20 months). Based on total tumor measurements (RECIST), 86% (95% CI: 75-93%) had at least stable disease or better at last follow-up, but 50% (95% CI: 38-62%) of remaining viable nodules (mRECIST) progressed within the tumor. Median PFS was reached at 17 and 13 months for total and viable tumors, respectively. NRS decreased from 6 (IQR 3) to 3 (IQR 4) (p < 0.001). SF-36 scores improved (physical health (41 (IQR 15) to 46 (IQR 12); p = 0.05, and mental health (49 (IQR 17) to 53 (IQR 9); p = 0.02)). Complications occurred in 36%, most commonly 1st/2nd degree skin burns. CONCLUSION: MRgFUS reduced tumor volume, reduced pain, and improved quality of life in this series of 105 patients with extra-abdominal desmoid fibromatosis. CLINICAL RELEVANCE STATEMENT: Imaging-guided ablation is being increasingly used as an alternative to surgery, radiation, and medical therapy for the treatment of desmoid fibromatosis. MR-guided high-intensity focused ultrasound is an incisionless ablation technique that can be used to reduce tumor burden effectively and safely. KEY POINTS: • Desmoid fibromatosis was treated with MR-guided high-intensity focused ultrasound in 105 patients. • MR-guided focused ultrasound ablation reduced tumor volume and pain and improved quality of life. • MR-guided focused ultrasound is a treatment option for patients with extra-abdominal desmoid tumors.

MR Thermometry during Transcranial MR Imaging-Guided Focused Ultrasound Procedures: A Review.

Interest in transcranial MR imaging-guided focused ultrasound procedures has recently grown. These incisionless procedures enable precise focal ablation of brain tissue using real-time monitoring by MR thermometry. This article will provide an updated review on clinically applicable technical underpinnings and considerations of proton resonance frequency MR thermometry, the most common clinically used MR thermometry sequence.

Estimate of fetal brain temperature using proton resonance frequency thermometry during 3 Tesla fetal magnetic resonance imaging.

Background: T2-weighted Single Shot Fast Spin Echo (SSFSE) scans at 3 Tesla (3T) are increasingly used to image fetal pathology due to their excellent tissue contrast resolution and signal-to-noise ratio (SNR). Temperature changes that may occur in response to radio frequency (RF) pulses used for these sequences at 3T have not been studied in human fetal brains. To evaluate the safety of T2-weighted SSFSE for fetal brains at 3T, magnetic resonance (MR) thermometry was used to measure relative temperature changes in a typical clinical fetal brain MR exam. Methods: Relative temperature was estimated using sets of gradient recalled echo (GRE) images acquired before and after T2-weighted SSFSE images which lasted 27.47±8.19 minutes. Thirty-one fetuses with cardiac abnormalities, and 20 healthy controls were included in this study. Fetal brain temperature was estimated by proton resonance frequency (PRF) thermometry and compared to the estimated temperature in the gluteal muscle of the mother. Seven scans with excessive motion were excluded. Local outlier factor (LOF) was performed to remove 12 additional scans with spurious phase measurements due to motion degradation and potential field drift. Linear regression was performed to determine if temperature changes are dependent on the rate of energy deposition during the scan. Results: For the 32 participants used in the analysis, 17 with cardiac abnormalities and 15 healthy controls, the average relative fetal temperate change was 0.19±0.73 ℃ higher than the mother, with no correlation between relative temperature change and the rate of images acquired during the scans (regression coefficient =-0.05, R-squared =0.05, P=0.22, F-statistic =1.60). The difference in the relative temperature changes between the fetal brain and mother's gluteal tissue in the healthy controls was on average 0.08 ℃ lower and found not to be statistically different (P=0.76) to the group with cardiac abnormalities. Conclusions: Our results indicate that the estimated relative temperature changes of the fetal brain compared to the mother's gluteal tissue from RF pulses during the course of the T2-weighted SSFSE fetal MR exam are minimal. The differences in acquired phase between these regions through the exam were found not to be statistically different. These findings support that fetal brain imaging at 3T is within FDA limits and safe.

Motion-robust, multi-slice, real-time MR thermometry for MR-guided thermal therapy in abdominal organs.

PURPOSE: To develop an effective and practical reconstruction pipeline to achieve motion-robust, multi-slice, real-time MR thermometry for monitoring thermal therapy in abdominal organs. METHODS: The application includes a fast spiral magnetic resonance imaging (MRI) pulse sequence and a real-time reconstruction pipeline based on multi-baseline proton resonance frequency shift (PRFS) method with visualization of temperature imaging. The pipeline supports multi-slice acquisition with minimal reconstruction lag. Simulations with a virtual motion phantom were performed to investigate the influence of the number of baselines and respiratory rate on the accuracy of temperature measurement. Phantom experiments with ultrasound heating were performed using a custom-made motion phantom to evaluate the performance of the pipeline. Lastly, experiments in healthy volunteers (N = 2) without heating were performed to evaluate the accuracy and stability of MR thermometry in abdominal organs (liver and kidney). RESULTS: The multi-baseline approach with greater than 25 baselines resulted in minimal temperature errors in the simulation. Phantom experiments demonstrated a 713 ms update time for 3-slice acquisitions. Temperature maps with 30 baselines showed clear temperature distributions caused by ultrasound heating in the respiratory phantom. Finally, the pipeline was evaluated with physiologic motions in healthy volunteers without heating, which demonstrated the accuracy (root mean square error [RMSE]) of 1.23 ± 0.18 °C (liver) and 1.21 ± 0.17 °C (kidney) and precision of 1.13 ± 0.11 °C (liver) and 1.16 ± 0.15 °C (kidney) using 32 baselines. CONCLUSIONS: The proposed real-time acquisition and reconstruction pipeline allows motion-robust, multi-slice, real-time temperature monitoring within the abdomen during free breathing.

Magnetic Resonance Imaging of the Lumbar Spine: Recommendations for Acquisition and Image Evaluation from the BACPAC Spine Imaging Working Group.

Management of patients suffering from low back pain (LBP) is challenging and requires development of diagnostic techniques to identify specific patient subgroups and phenotypes in order to customize treatment and predict clinical outcome. The Back Pain Consortium (BACPAC) Research Program Spine Imaging Working Group has developed standard operating procedures (SOPs) for spinal imaging protocols to be used in all BACPAC studies. These SOPs include procedures to conduct spinal imaging assessments with guidelines for standardizing the collection, reading/grading (using structured reporting with semi-quantitative evaluation using ordinal rating scales), and storage of images. This article presents the approach to image acquisition and evaluation recommended by the BACPAC Spine Imaging Working Group. While the approach is specific to BACPAC studies, it is general enough to be applied at other centers performing magnetic resonance imaging (MRI) acquisitions in patients with LBP. The herein presented SOPs are meant to improve understanding of pain mechanisms and facilitate patient phenotyping by codifying MRI-based methods that provide standardized, non-invasive assessments of spinal pathologies. Finally, these recommended procedures may facilitate the integration of better harmonized MRI data of the lumbar spine across studies and sites within and outside of BACPAC studies.

Technical note: Low-cost MR-compatible pneumatic respiratory organ motion simulator for the development of MR-guided thermal therapy.

BACKGROUND: In magnetic resonance (MR)-guided thermal therapy, respiratory motion can cause a significant temperature error in MR thermometry and reduce the efficiency of the treatment. A respiratory motion simulator is necessary for the development of new MR imaging (MRI) and motion compensation techniques. PURPOSE: The purpose of this study is to develop a low-cost and simple MR-compatible respiratory motion simulator to support proof-of-concept studies of MR monitoring approaches with respiratory-induced abdominal organ motion. METHODS: The phantom motion system integrates pneumatic control via an actuator subsystem located outside the MRI and coupled via plastic tubing to a compressible bag for distention and retraction within the MRI safe motion subsystem and phantom positioned within the MRI scanner. Performance of the respiratory motion simulator was evaluated with a real-time gradient echo MRI pulse sequence. RESULTS: The motion simulator can produce respiratory rates in the range of 8-16 breaths/min. Our experiments showed the consistent periodic motion of the phantom during MRI acquisition in the range of 3.7-9 mm with 16 breaths/min. The operation of the simulator did not cause interference with MRI acquisition. CONCLUSIONS: In this study, we have demonstrated the ability of the motion simulator to generate controlled respiratory motion of a phantom. The low-cost MR-compatible respiratory motion simulator can be easily constructed from off-the-shelf and 3D-printed parts based on open-source 3D models and instructions. This could lower the barriers to the development of new MRI techniques with motion compensation.

Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging.

The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.

Sonication strategies toward volumetric ultrasound hyperthermia treatment using the ExAblate body MRgFUS system.

PURPOSE: The ExAblate body MRgFUS system requires advanced beamforming strategies for volumetric hyperthermia. This study aims to develop and evaluate electronic beam steering, multi-focal patterns, and sector vortex beamforming approaches in conjunction with partial array activation using an acoustic and biothermal simulation framework along with phantom experiments. METHODS: The simulation framework was developed to calculate the 3D acoustic intensity and temperature distribution resulting from various beamforming and scanning strategies. A treatment cell electronically sweeping a single focus was implemented and evaluated in phantom experiments. The acoustic and thermal focal size of vortex beam propagation was quantified according to the vortex modes, number of active array elements, and focal depth. RESULTS: Turning off a percentage of the outer array to increase the f-number increased the focal size with a decrease in focal gain. 60% active elements allowed generating a sonication cell with an off-axis of 10 mm. The vortex mode number 4 with 60% active elements resulted in a larger heating volume than using the full array. Volumetric hyperthermia in the phantom was evaluated with the vortex mode 4 and respectively performed with 100% and 80% active elements. MR thermometry demonstrated that the volumes were found to be 18.8 and 29.7 cm3, respectively, with 80% array activation producing 1.58 times larger volume than the full array. CONCLUSIONS: This study demonstrated that both electronic beam steering and sector vortex beamforming approaches in conjunction with partial array activation could generate large volume heating for HT delivery using the ExAblate body array.

Anatomic thermochromic tissue-mimicking phantom of the lumbar spine for pre-clinical evaluation of MR-guided focused ultrasound (MRgFUS) ablation of the facet joint.

OBJECTIVE: To develop a thermochromic tissue-mimicking phantom (TTMP) with an embedded 3D-printed bone mimic of the lumbar spine to evaluate MRgFUS ablation of the facet joint and medial branch nerve. MATERIALS AND METHODS: Multiple 3D-printed materials were selected and characterized by measurements of speed of sound and linear acoustic attenuation coefficient using a through-transmission technique. A 3D model of the lumbar spine was segmented from a de-identified CT scan, and 3D printed. The 3D-printed spine was embedded within a TTMP with thermochromic ink color change setpoint at 60 °C. Multiple high energy sonications were targeted to the facet joints and medial branch nerve anatomical location using an ExAblate MRgFUS system connected to a 3T MR scanner. The phantom was dissected to assess sonication targets and the surrounding structures for color change as compared to the expected region of ablation on MR-thermometry. RESULTS: The measured sound attenuation coefficient and speed of sound of gypsum was 240 Np/m-MHz and 2471 m/s, which is the closest to published values for cortical bone. Following sonication, dissection of the TTMP revealed good concordance between the regions of color change within the phantom and expected areas of ablation on MR-thermometry. No heat deposition was observed in critical areas, including the spinal canal and nerve roots from either color change or MRI. CONCLUSION: Ablated regions in the TTMP correlated well with expected ablations based on MR-thermometry. These findings demonstrate the utility of an anatomic spine phantom in evaluating MRgFUS sonication for facet joint and medial branch nerve ablations.

Computer-Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI.

BACKGROUND: Accurate interpretation of hip MRI is time-intensive and difficult, prone to inter- and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities. PURPOSE: To 1) develop and evaluate a deep-learning-based model for binary classification of hip osteoarthritis (OA) morphological abnormalities on MR images, and 2) develop an artificial intelligence (AI)-based assist tool to find if using the model predictions improves interreader agreement in hip grading. STUDY TYPE: Retrospective study aimed to evaluate a technical development. POPULATION: A total of 764 MRI volumes (364 patients) obtained from two studies (242 patients from LASEM [FORCe] and 122 patients from UCSF), split into a 65-25-10% train, validation, test set for network training. FIELD STRENGTH/SEQUENCE: 3T MRI, 2D T2 FSE, PD SPAIR. ASSESSMENT: Automatic binary classification of cartilage lesions, bone marrow edema-like lesions, and subchondral cyst-like lesions using the MRNet, interreader agreement before and after using network predictions. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, area under curve (AUC), specificity and sensitivity, and balanced accuracy. RESULTS: For cartilage lesions, bone marrow edema-like lesions and subchondral cyst-like lesions the AUCs were: 0.80 (95% confidence interval [CI] 0.65, 0.95), 0.84 (95% CI 0.67, 1.00), and 0.77 (95% CI 0.66, 0.85), respectively. The sensitivity and specificity of the radiologist for binary classification were: 0.79 (95% CI 0.65, 0.93) and 0.80 (95% CI 0.59, 1.02), 0.40 (95% CI -0.02, 0.83) and 0.72 (95% CI 0.59, 0.86), 0.75 (95% CI 0.45, 1.05) and 0.88 (95% CI 0.77, 0.98). The interreader balanced accuracy increased from 53%, 71% and 56% to 60%, 73% and 68% after using the network predictions and saliency maps. DATA CONCLUSION: We have shown that a deep-learning approach achieved high performance in clinical classification tasks on hip MR images, and that using the predictions from the deep-learning model improved the interreader agreement in all pathologies. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:1163-1172.

Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.

Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis still relies on expert radiologists' readings. The potential of such a model in clinical routine should be investigated. Purpose To develop a multitask deep learning model for grading radiographic hip osteoarthritis features on radiographs and compare its performance to that of attending-level radiologists. Materials and Methods This retrospective study analyzed hip joints seen on weight-bearing anterior-posterior pelvic radiographs from participants in the Osteoarthritis Initiative (OAI). Participants were recruited from February 2004 to May 2006 for baseline measurements, and follow-up was performed 48 months later. Femoral osteophytes (FOs), acetabular osteophytes (AOs), and joint-space narrowing (JSN) were graded as absent, mild, moderate, or severe according to the Osteoarthritis Research Society International atlas. Subchondral sclerosis and subchondral cysts were graded as present or absent. The participants were split at 80% (n = 3494), 10% (n = 437), and 10% (n = 437) by using split-sample validation into training, validation, and testing sets, respectively. The multitask neural network was based on DenseNet-161, a shared convolutional features extractor trained with multitask loss function. Model performance was evaluated in the internal test set from the OAI and in an external test set by using temporal and geographic validation consisting of routine clinical radiographs. Results A total of 4368 participants (mean age, 61.0 years ± 9.2 [standard deviation]; 2538 women) were evaluated (15 364 hip joints on 7738 weight-bearing anterior-posterior pelvic radiographs). The accuracy of the model for assessing these five features was 86.7% (1333 of 1538) for FOs, 69.9% (1075 of 1538) for AOs, 81.7% (1257 of 1538) for JSN, 95.8% (1473 of 1538) for subchondral sclerosis, and 97.6% (1501 of 1538) for subchondral cysts in the internal test set, and 82.7% (86 of 104) for FOS, 65.4% (68 of 104) for AOs, 80.8% (84 of 104) for JSN, 88.5% (92 of 104) for subchondral sclerosis, and 91.3% (95 of 104) for subchondral cysts in the external test set. Conclusion A multitask deep learning model is a feasible approach to reliably assess radiographic features of hip osteoarthritis. © RSNA, 2020 Online supplemental material is available for this article.

Wireless Resonant Circuits Printed Using Aerosol Jet Deposition for MRI Catheter Tracking.

Interventional magnetic resonance imaging (MRI) could allow for diagnosis and immediate treatment of ischemic stroke; however, such endovascular catheter-based procedures under MRI guidance are inherently difficult. One major challenge is tracking the tip of the catheter, as standard fabrication methods for building inductively coupled coil markers are rigid and bulky. Here, we report a new approach that uses aerosol jet deposition to three-dimensional (3-D) print an inductively coupled RF coil marker on a polymer catheter. Our approach enables lightweight conforming markers on polymer catheters and these low-profile markers allow the catheter to be more safely navigated in small caliber vessels. Prototype markers with an inductor with the geometry of a double helix are incorporated on catheters for in vitro studies, and we show that these markers exhibit good signal amplification. We report temperature measurements and, finally, demonstrate feasibility in a preliminary in vivo experiment. We provide material properties and electromagnetic simulation performance analysis. This paper presents fully aerosol jet-deposited and functional wireless resonant markers on polymer catheters for use in 3T clinical scanners.

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning.

PURPOSE: To investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation. MATERIALS AND METHODS: Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics. RESULTS: The binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification. CONCLUSION: A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

T2 mapping as a predictor of nonperfused volume in MRgFUS treatment of desmoid tumors.

Objective: The objective of this study was to develop an alternative method of non-contrast monitoring of tissue ablation during focused ultrasound treatment. Desmoid tumors are benign but locally aggressive soft tissue tumors that arise from fibroblast cells. Magnetic resonance-guided focused ultrasound (MRgFUS) has emerged as an alternative to conventional therapies, showing promising results in reduction of tumor volume without significant side effects. The gold-standard assessment of the reduction of viable tumor volume post-treatment is non-perfused volume (NPV) and evaluation of NPV is typically performed with post-treatment gadolinium enhanced MR imaging. However, as gadolinium cannot be repeatedly administered during treatments, there is a need for alternative non-contrast monitoring of the tissue to prevent over and under treatment. Methods: Double-echo and multi-echo images were acquired before, during and after the MRgFUS treatment. T2 maps were generated with an exponential fit and T2 maps were compared to post-treatment post-contrast images.Results: In all five MRgFUS treatment sessions, T2 mapping showed excellent qualitative agreement with the post-contrast NPV.Conclusions: T2 mapping may be used to visualize the extent of ablation with focused ultrasound and can be used as a predictor of NPV prior to the administration of contrast during the post-treatment assessment.

The impact of technical parameters on ablation volume during MR-guided focused ultrasound of desmoid tumors.

PURPOSE: Desmoid tumors are benign, locally aggressive soft tissue tumors derived from fibroblasts. Magnetic resonance-guided focused ultrasound (MRgFUS) is a safe and effective treatment for desmoid tumors. The purpose of this study was to retrospectively review the MRgFUS treatments of desmoid tumors at our institution to determine which technical treatment parameters contributed most significantly to the accumulation of thermal dose. MATERIALS AND METHODS: The study protocol was approved by the local IRB. We retrospectively reviewed data from MRgFUS treatments performed in histologically-confirmed desmoid tumors, over a period of 18 months. Sonication parameter means were compared with ANOVA. Mixed effects and linear regression models were used to evaluate the relative contribution of different parameters to thermal dose volume. RESULTS: Nine-hundred thirty-six sonications were reviewed in 13 treatments. Accumulated dose per sonication was greatest for elongated sonications (0.96 cc ± 0.90) compared to short (0.88 ± 0.93 cc) and nominal (0.55 ± 0.70 cc) sonications, p < .001. 65.2% of short sonications resulted in high percentage ablations, compared to 46.0% of nominal and 35.1% of elongated sonications. Standardized beta coefficients (anticipated increased volume in cc per unit) for power, duration, energy and average temperature were 0.006, 0.057, 0.00035 and 0.03, p < .001. Regarding dose efficacy, dose area contributed the greatest to this variability - 50.7% (45.5-54.8%), followed by distance - 16.6% (12.9-20.0%). CONCLUSIONS: A variety of sonication parameters significantly contributed to thermal ablation volume following MRgFUS of desmoid tumors, in reproducible patterns. This work can serve as the basis for future models working toward improved planning for MRgFUS treatments.