One of the key unmet needs to improve long-term outcomes of heart transplantation is to develop accurate, noninvasive, and practical diagnostic tools to detect transplant rejection. Early intragraft inflammation and endothelial cell injuries occur prior to advanced transplant rejection. We developed a novel diagnostic imaging platform to detect early declines in microvascular perfusion (MP) of cardiac transplants using contrast-enhanced ultrasonography (CEUS). The efficacy of CEUS in detecting transplant rejection was tested in a murine model of heart transplants, a standard preclinical model of solid organ transplant. As compared to the syngeneic groups, a progressive decline in MP was demonstrated in the allografts undergoing acute transplant rejection (40%, 64%, and 92% on days 4, 6, and 8 posttransplantation, respectively) and chronic rejection (33%, 33%, and 92% on days 5, 14, and 30 posttransplantation, respectively). Our perfusion studies showed restoration of MP following antirejection therapy, highlighting its potential to help monitor efficacy of antirejection therapy. Our data suggest that early endothelial cell injury and platelet aggregation contributed to the early MP decline observed in the allografts. High-resolution MP mapping may allow for noninvasive detection of heart transplant rejection. The data presented have the potential to help in the development of next-generation imaging approaches to diagnose transplant rejection.
PURPOSE: To demonstrate the use of anatomic MRI-visible three-dimensional (3D)-printed phantoms and to assess process accuracy and material MR signal properties. METHODS: A cervical spine model was generated from computed tomography (CT) data and 3D-printed using an MR signal-generating material. Printed phantom accuracy and signal characteristics were assessed using 120 kVp CT and 3 Tesla (T) MR imaging. The MR relaxation rates and diffusion coefficient of the fabricated phantom were measured and (1) H spectra were acquired to provide insight into the nature of the proton signal. Finally, T2 -weighted imaging was performed during cryoablation of the model. RESULTS: The printed model produced a CT signal of 102 ± 8 Hounsfield unit, and an MR signal roughly 1/3(rd) that of saline in short echo time/short repetition time GRE MRI (456 ± 36 versus 1526 ± 121 arbitrary signal units). Compared with the model designed from the in vivo CT scan, the printed model differed by 0.13 ± 0.11 mm in CT, and 0.62 ± 0.28 mm in MR. The printed material had T2 ∼32 ms, T2*∼7 ms, T1 ∼193 ms, and a very small diffusion coefficient less than olive oil. MRI monitoring of the cryoablation demonstrated iceball formation similar to an in vivo procedure. CONCLUSION: Current 3D printing technology can be used to print anatomically accurate phantoms that can be imaged by both CT and MRI. Such models can be used to simulate MRI-guided interventions such as cryosurgeries. Future development of the proposed technique can potentially lead to printed models that depict different tissues and anatomical structures with different MR signal characteristics.
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, (1) How much data from the new domain is required for a decent adaptation of the original network?; and, (2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset. The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.
We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.
PURPOSE: To assess whether R2* mapping with a standard Monoexponential (ME) or a Gaussian Augmentation of the Monoexponential (GAME) decay model better characterizes gradient-echo signal decays in gynecological cancers after external beam radiation therapy at 3T, and evaluate implications of modeling for noninvasive identification of intratumoral hypoxia. MATERIALS AND METHODS: Multi-gradient-echo signals were acquired on 25 consecutive patients with gynecologic cancers and three healthy participants during inhalation of different oxygen concentrations at 3T. Data were fitted with both ME and GAME models. Models were compared using F-tests in tumors and muscles in patients, muscles, cervix, and uterus in healthy participants, and across oxygenation levels. RESULTS: GAME significantly improved fitting over ME (P < 0.05): Improvements with GAME covered 34% of tumor regions-of-interest on average, ranging from 6% (of a vaginal tumor) to 68% (of a cervical tumor) in individual tumors. Improvements with GAME were more prominent in areas that would be assumed hypoxic based on ME alone, reaching 90% as ME R2* approached 100 Hz. Gradient echo decay parameters at different oxygenation levels were not significantly different (P = 0.81). CONCLUSION: R2* may prove sensitive to hypoxia; however, inaccurate representations of underlying data may limit the success of quantitative assessments. Although the degree to which R2 or σ values correlate with hypoxia remains unknown, improved characterization with GAME increases the potential for determining any correlates of fit parameters with biomarkers, such as oxygenation status. J. MAGN. RESON. IMAGING 2016;44:1020-1030.
OBJECTIVE: We report the development and use of MRI-compatible and MRI-visible 3D printed models in conjunction with advanced visualization software models to plan and simulate safe access routes to achieve a theoretic zone of cryoablation for percutaneous image-guided treatment of a C7 pedicle osteoid osteoma and an L1 lamina osteoblastoma. Both models altered procedural planning and patient care. CONCLUSION: Patient-specific MRI-visible models can be helpful in planning complex percutaneous image-guided cryoablation procedures.
BACKGROUND: Bilateral mastectomies (BM) are traditionally performed by single surgeons (SS); a co-surgeon (CS) technique, where each surgeon concurrently performs a unilateral mastectomy, offers an alternative approach. We examined differences in general surgery time (GST), overall surgery time (OST), and patient complications for BM performed by CS and SS.
METHODS: Patients undergoing BM with tissue expander reconstruction (BMTR) between January 2010 and May 2014 at our center were identified through operative case logs. GST (incision to end of BM procedure), reconstruction duration (RST) (plastic surgery start to end of reconstruction) and OST (OST = GST + RST) was calculated. Patient age, presence/stage of cancer, breast weight, axillary procedure performed, and 30-day postoperative complications were extracted from medical records. Differences in GST and OST between CS and SS cases were assessed with a t test. A multivariate linear regression was fit to identify factors associated with GST.
RESULTS: A total of 116 BMTR cases were performed [CS, n = 67 (57.8 %); SS, n = 49 (42.2 %)]. Demographic characteristics did not differ between groups. GST and OST were significantly shorter for CS cases, 75.8 versus 116.8 min, p < .0001, and 255.2 versus 278.3 min, p = .005, respectively. Presence of a CS significantly reduces BMTR time (β = -38.82, p < .0001). Breast weight (β = 0.0093, p = .03) and axillary dissection (β = 28.69, p = .0003) also impacted GST.
CONCLUSIONS: The CS approach to BMTR reduced both GST and OST; however, the degree of time savings (35.1 and 8.3 %, respectively) was less than hypothesized. A larger study is warranted to better characterize time, cost, and outcomes of the CS-approach for BM.
Autism Spectrum Disorder (ASD) has been suggested to associate with alterations in brain connectivity. In this study, we focus on a fiber clustering tractography segmentation strategy to observe white matter connectivity alterations in ASD. Compared to another popular parcellation-based approach for tractography segmentation based on cortical regions, we hypothesized that the clustering-based method could provide a more anatomically correspondent division of white matter. We applied this strategy to conduct a population-based group statistical analysis for the automated prediction of ASD. We obtained a maximum classification accuracy of 81.33% be- tween ASDs and controls, compared to the results of 78.00% from the parcellation-based method.
Significant efforts have been dedicated to minimizing invasiveness associated with surgical interventions, most of which have been possible thanks to the developments in medical imaging, surgical navigation, visualization and display technologies. Image-guided interventions have promised to dramatically change the way therapies are delivered to many organs. However, in spite of the development of many sophisticated technologies over the past two decades, other than some isolated examples of successful implementations, minimally invasive therapy is far from enjoying the wide acceptance once envisioned. This paper provides a large-scale overview of the state-of-the-art developments, identifies several barriers thought to have hampered the wider adoption of image-guided navigation, and suggests areas of research that may potentially advance the field.
The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.
OBJECTIVE: Magnetic resonance imaging (MRI) is a widely used imaging modality for studies of knee osteoarthritis (OA). Compared to radiography, MRI offers exceptional soft tissue imaging and true three-dimensional (3D) visualization. However, MRI is expensive both due to the cost of acquisition and evaluation of the images. The goal of our study is to develop a new method to address the cost of MRI by combining innovative acquisition methods and automated post-processing software. METHODS: Ten healthy volunteers were scanned with three different MRI protocols: A standard 3D dual-echo steady state (DESS) pulse sequence, an accelerated DESS (DESS), acquired at approximately half the time compared to DESS, and a multi-echo time DESS (DESS), which is capable of producing measurements of T2 relaxation time. A software tool was used to measure cartilage volume. Accuracy was quantified by comparing DESS to DESS and DESS and precision was measured using repeat readings and acquisitions. T2 precision was determined using duplicate DESS acquisitions. Intra-class correlation coefficients (ICCs), root-mean square standard deviation (RMSSD), and the coefficient of variation (CoV) were used to quantify accuracy and precision. RESULTS: The accuracies of DESS and DESS were CoV = 3.7% and CoV = 6.6% respectively, while precision was 3.8%, 3.0%, and 3.1% for DESS, DESS and DESS. T2 repositioning precision was 5.8%. CONCLUSION: The results demonstrate that accurate and precise quantification of cartilage volume is possible using a combination of substantially faster MRI acquisition and post-processing software. Precise measurements of cartilage T2 and volume can be made using the same acquisition.
Purpose To investigate the safety and targeting errors of deep-brain stimulation (DBS) electrodes placed under interventional magnetic resonance (MR) imaging, which allows near real-time anatomic placement without physiologic mapping. Materials and Methods Retrospectively evaluated were 10 consecutive patients (five women, five men) with a mean age of 59.9 years (age range, 17-79 years). These patients underwent interventional MR imaging-guided DBS placement for movement disorders from September 2013 to August 2014 for placement of 19 DBS electrodes in cases where traditional frame-based surgery may be challenging because of the following: dystonia resulting in difficulty in placing the patients in frame, patient's inability or unwillingness to tolerate awake surgery, or anatomic anomaly or variant that could increase the risk of bleeding from microelectrode mapping. Outcomes measured included perioperative hemorrhage, death, and stroke, and electrode functionality assessed at 2 weeks after the operation. In addition, the mean radial error and mean trajectory error were calculated. Results No intraoperative neurologic complications (n = 10 [95% confidence interval: 0%, 31%]) were observed. One patient developed aspiration pneumonia in the postoperative period. Mean radial error was 0.7 mm ± 0.4 (standard deviation) and mean trajectory error was 0.5 mm ± 0.4. All leads delivered clinically effective stimulation. Conclusion Interventional MR imaging-guided DBS electrode placement may be a safe and effective alternative to conventional frame-based surgery in well-selected patients.
Subjects undergoing cardiac arrest within a magnetic resonance imaging (MRI) scanner are currently removed from the bore and then from the MRI suite, before the delivery of cardiopulmonary resuscitation and de brillation, potentially increasing the risk of mortality. This precludes many higher-risk (acute ischemic and acute stroke) patients from undergoing MRI and MRI-guided intervention. An MRI-conditional cardiac de brillator should enable scanning with de brillation pads attached and the generator ON, enabling application of de brillation within the seconds of MRI after a cardiac event. An MRI-conditional external de brillator may improve patient acceptance for MRI procedures. Methods and Results—A commercial external de brillator was rendered 1.5 Tesla MRI-conditional by the addition of novel radiofrequency lters between the generator and commercial disposable surface pads. The radiofrequency lters reduced emission into the MRI scanner and prevented cable/surface pad heating during imaging, while preserving all the de brillator monitoring and delivery functions. Human volunteers were imaged using high speci c absorption rate sequences to validate MRI image quality and lack of heating. Swine were electrically brillated (n=4) and thereafter de brillated both outside and inside the MRI bore. MRI image quality was reduced by 0.8 or 1.6 dB, with the generator in monitoring mode and operating on battery or AC power, respectively. Commercial surface pads did not create artifacts deeper than 6 mm below the skin surface. Radiofrequency heating was within US Food and Drug Administration guidelines. De brillation was completely successful inside and outside the MRI bore. Conclusions—A prototype MRI-conditional de brillation system successfully de brillated in the MRI without degrading the image quality or increasing the time needed for de brillation. It can increase patient acceptance for MRI procedures.
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.
Patient-mounted needle guide devices for percutaneous ablation are vulnerable to patient motion. The objective of this study is to develop and evaluate a software system for an MRI-compatible patient-mounted needle guide device that can adaptively compensate for displacement of the device due to patient motion using a novel image-based automatic device-to-image registration technique. We have developed a software system for an MRI-compatible patient-mounted needle guide device for percutaneous ablation. It features fully-automated image-based device-to-image registration to track the device position, and a device controller to adjust the needle trajectory to compensate for the displacement of the device. We performed: (a) a phantom study using a clinical MR scanner to evaluate registration performance; (b) simulations using intraoperative time-series MR data acquired in 20 clinical cases of MRI-guided renal cryoablations to assess its impact on motion compensation; and (c) a pilot clinical study in three patients to test its feasibility during the clinical procedure. FRE, TRE, and success rate of device-to-image registration were [Formula: see text] mm, [Formula: see text] mm, and 98.3% for the phantom images. The simulation study showed that the motion compensation reduced the targeting error for needle placement from 8.2 mm to 5.4 mm (p < 0.0005) in patients under general anesthesia (GA), and from 14.4 mm to 10.0 mm ([Formula: see text]) in patients under monitored anesthesia care (MAC). The pilot study showed that the software registered the device successfully in a clinical setting. Our simulation study demonstrated that the software system could significantly improve targeting accuracy in patients treated under both MAC and GA. Intraprocedural image-based device-to-image registration was feasible.
PURPOSE: To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. MATERIALS AND METHODS: The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. RESULTS: The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. CONCLUSION: The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
OBJECTIVE: The purpose of this article is to report our intermediate to long-term outcomes with image-guided percutaneous hepatic tumor cryoablation and to evaluate its technical success, technique efficacy, local tumor progression, and adverse event rate. MATERIALS AND METHODS: Between 1998 and 2014, 299 hepatic tumors (243 metastases and 56 primary tumors; mean diameter, 2.5 cm; median diameter, 2.2 cm; range, 0.3-7.8 cm) in 186 patients (95 women; mean age, 60.9 years; range, 29-88 years) underwent cryoablation during 236 procedures using CT (n = 126), MRI (n = 100), or PET/CT (n = 10) guidance. Technical success, technique efficacy at 3 months, local tumor progression (mean follow-up, 2.5 years; range, 2 months to 14.6 years), and adverse event rates were calculated. RESULTS: The technical success rate was 94.6% (279/295). The technique efficacy rate was 89.5% (231/258) and was greater for tumors smaller than 4 cm (93.4%; 213/228) than for larger tumors (60.0%; 18/30) (p < 0.0001). Local tumor progression occurred in 23.3% (60/258) of tumors and was significantly more common after the treatment of tumors 4 cm or larger (63.3%; 19/30) compared with smaller tumors (18.0%; 41/228) (p < 0.0001). Adverse events followed 33.8% (80/236) of procedures and were grade 3-5 in 10.6% (25/236) of cases. Grade 3 or greater adverse events more commonly followed the treatment of larger tumors (19.5%; 8/41) compared with smaller tumors (8.7%; 17/195) (p = 0.04). CONCLUSION: Image-guided percutaneous cryoablation of hepatic tumors is efficacious; however, tumors smaller than 4 cm are more likely to be treated successfully and without an adverse event.