We introduce a versatile framework for characterizing and extracting salient structures in three-dimensional symmetric second-order tensor fields. The key insight is that degenerate lines in tensor fields, as defined by the standard topological approach, are exactly crease (ridge and valley) lines of a particular tensor invariant called mode. This reformulation allows us to apply well-studied approaches from scientific visualization or computer vision to the extraction of topological lines in tensor fields. More generally, this main result suggests that other tensor invariants, such as anisotropy measures like fractional anisotropy (FA), can be used in the same framework in lieu of mode to identify important structural properties in tensor fields. Our implementation addresses the specific challenge posed by the non-linearity of the considered scalar measures and by the smoothness requirement of the crease manifold computation. We use a combination of smooth reconstruction kernels and adaptive refinement strategy that automatically adjust the resolution of the analysis to the spatial variation of the considered quantities. Together, these improvements allow for the robust application of existing ridge line extraction algorithms in the tensor context of our problem. Results are proposed for a diffusion tensor MRI dataset, and for a benchmark stress tensor field used in engineering research.
Acoustic radiation force impulse imaging is an elastography method developed for ultrasound imaging that maps displacements produced by focused ultrasound pulses systematically applied to different locations. The resulting images are "stiffness weighted" and yield information about local mechanical tissue properties. Here, the feasibility of magnetic resonance acoustic radiation force imaging (MR-ARFI) was tested. Quasistatic MR elastography was used to measure focal displacements using a one-dimensional MRI pulse sequence. A 1.63 or 1.5 MHz transducer supplied ultrasound pulses which were triggered by the magnetic resonance imaging hardware to occur before a displacement-encoding gradient. Displacements in and around the focus were mapped in a tissue-mimicking phantom and in an ex vivo bovine kidney. They were readily observed and increased linearly with acoustic power in the phantom (R2=0.99). At higher acoustic power levels, the displacement substantially increased and was associated with irreversible changes in the phantom. At these levels, transverse displacement components could also be detected. Displacements in the kidney were also observed and increased after thermal ablation. While the measurements need validation, the authors have demonstrated the feasibility of detecting small displacements induced by low-power ultrasound pulses using an efficient magnetic resonance imaging pulse sequence that is compatible with tracking of a dynamically steered ultrasound focal spot, and that the displacement increases with acoustic power. MR-ARFI has potential for elastography or to guide ultrasound therapies that use low-power pulsed ultrasound exposures, such as drug delivery.
We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectation-maximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.
In this work, we describe a white matter trajectory clustering algorithm that allows for incorporating and appropriately weighting anatomical information. The influence of the anatomical prior reflects confidence in its accuracy and relevance. It can either be defined by the user or it can be inferred automatically. After a detailed description of our novel clustering framework, we demonstrate its properties through a set of preliminary experiments.
MR imaging-guided interventions are well established in routine patient care in many parts of the world. There are many approaches, depending on magnet design and clinical need, based on MR imaging providing excellent inherent tissue contrast without ionizing radiation risk for patients. MR imaging-guided minimally invasive therapeutic procedures have advantages over conventional surgical procedures. In the genitourinary tract, MR imaging guidance has a role in tumor detection, localization, and staging and can provide accurate image guidance for minimally invasive procedures. The advent of molecular and metabolic imaging and use of higher strength magnets likely will improve diagnostic accuracy and allow targeted therapy to maximize disease control and minimize side effects.
In this article the current issues of diagnosis and detection of prostate cancer are reviewed. The limitations for current techniques are highlighted and some possible solutions with MR imaging and MR-guided biopsy approaches are reviewed. There are several different biopsy approaches under investigation. These include transperineal open magnet approaches to closed-bore 1.5T transrectal biopsies. The imaging, image processing, and tracking methods are also discussed. In the arena of therapy, MR guidance has been used in conjunction with radiation methods, either brachytherapy or external delivery. The principles of the radiation treatment, the toxicities, and use of images are outlined. The future role of imaging and image-guided interventions lie with providing a noninvasive surrogate for cancer surveillance or monitoring treatment response. The shift to minimally invasive focal therapies has already begun and will be very exciting when MR-guided focused ultrasound surgery reaches its full potential.
PURPOSE: To develop and assess a needle-guiding manipulator for MRI-guided therapy that allows a physician to freely select the needle insertion path while maintaining remote center of motion (RCM) at the tumor site.
MATERIALS AND METHODS: The manipulator consists of a three-degrees-of-freedom (DOF) base stage and passive needle holder with unconstrained two-DOF rotation. The synergistic control keeps the Virtual RCM at the preplanned target using encoder outputs from the needle holder as input to motorize the base stage.
RESULTS: The manipulator assists in searching for an optimal needle insertion path which is a complex and time-consuming task in MRI-guided ablation therapy for liver tumors. The assessment study showed that accuracy of keeping the virtual RCM to predefined position is 3.0 mm. In a phantom test, the physicians found the needle insertion path faster with than without the manipulator (number of physicians = 3, P = 0.001). However, the alignment time with the virtual RCM was not shorter when imaging time for planning were considered.
CONCLUSION: The study indicated that the robot holds promise as a tool for accurately and interactively selecting the optimal needle insertion path in liver ablation therapy guided by open-configuration MRI.
Over the last decade the focus of published research on MRI-guided cryotherapy has switched from the study of experimental models to the clinical treatment of patients. The latter reports attest to the safety and feasibility of treating lesions in the liver, kidney, and other sites throughout the body. Further, the published images and initial results speak to the utility of MRI for the task of monitoring this specific procedure. This clinical utility is a realization of the promise of the earlier experimental work that showed the clarity with which interstitial ice is seen under MRI under various pulse sequence parameters. Early adopters have taken advantage of access to the patient that is provided by low and mid-field open scanners; the near future will test the suitability of higher field systems. It has been critical that an FDA-approved cryotherapy system and suitably thin probes were customized for the MRI environment a decade ago by which percutaneous cryotherapy could be performed. There is still work to be done to expand the role of percutaneous cryotherapy, to understand various tissue responses, and to optimize visualization of therapeutic isotherms. Also, long-term outcomes need to be assessed. Overall, in a worldwide environment in which the practice of ablation is growing and an appreciation for such therapies is on the rise, the work of these recent years provides sound footing for the advances that lay ahead for clinical MRI-guided cryotherapy.
Magnetic Resonance Imaging (MRI) has potential to be a superior medical imaging modality for guiding and monitoring prostatic interventions. The strong magnetic field prevents the use of conventional mechatronics and the confined physical space makes it extremely challenging to access the patient. We have designed a robotic assistant system that overcomes these difficulties and promises safe and reliable intra-prostatic needle placement inside closed high-field MRI scanners. The robot performs needle insertion under real-time 3T MR image guidance; workspace requirements, MR compatibility, and workflow have been evaluated on phantoms. The paper explains the robot mechanism and controller design and presents results of preliminary evaluation of the system.
Advances in neuroscience have resulted in the development of new diagnostic and therapeutic agents for potential use in the central nervous system (CNS). However, the ability to deliver the majority of these agents to the brain is limited by the blood-brain barrier (BBB), a specialized structure of the blood vessel wall that hampers transport and diffusion from the blood to the brain. Many CNS disorders could be treated with drugs, enzymes, genes, or large-molecule biotechnological products such as recombinant proteins, if they could cross the BBB. This article reviews the problems of the BBB presence in treating the vast majority of CNS diseases and the efforts to circumvent the BBB through the design of new drugs and the development of more sophisticated delivery methods. Recent advances in the development of noninvasive, targeted drug delivery by MRI-guided ultrasound-induced BBB disruption are also summarized.
Recent advances in bioinformatics have opened entire new avenues for organizing, integrating and retrieving neuroscientific data, in a digital, machine-processable format, which can be at the same time understood by humans, using ontological, symbolic data representations. Declarative information stored in ontological format can be perused and maintained by domain experts, interpreted by machines, and serve as basis for a multitude of decision support, computerized simulation, data mining, and teaching applications. We have developed a prototype symbolic model of canonical neuroanatomy of the motor system. Our symbolic model is intended to support symbolic look up, logical inference and mathematical modeling by integrating descriptive, qualitative and quantitative functional neuroanatomical knowledge. Furthermore, we show how our approach can be extended to modeling impaired brain connectivity in disease states, such as common movement disorders. In developing our ontology, we adopted a disciplined modeling approach, relying on a set of declared principles, a high-level schema, Aristotelian definitions, and a frame-based authoring system. These features, along with the use of the Unified Medical Language System (UMLS) vocabulary, enable the alignment of our functional ontology with an existing comprehensive ontology of human anatomy, and thus allow for combining the structural and functional views of neuroanatomy for clinical decision support and neuroanatomy teaching applications. Although the scope of our current prototype ontology is limited to a particular functional system in the brain, it may be possible to adapt this approach for modeling other brain functional systems as well.
Simultaneous capturing of ultrasound (US) and magnetic resonance (MR) images allows fusion of information obtained from both modalities. We propose an MR-compatible US system where MR images are acquired in a known orientation with respect to the US imaging plane and concurrent real-time imaging can be achieved. Compatibility of the two imaging devices is a major issue in the physical setup. Tests were performed to quantify the radio frequency (RF) noise introduced in MR and US images, with the US system used in conjunction with MRI scanner of different field strengths (0.5 T and 3 T). Furthermore, simultaneous imaging was performed on a dual modality breast phantom in the 0.5 T open bore and 3 T close bore MRI systems to aid needle-guided breast biopsy. Fiducial based passive tracking and electromagnetic based active tracking were used in 3 T and 0.5 T, respectively, to establish the location and orientation of the US probe inside the magnet bore. Our results indicate that simultaneous US and MR imaging are feasible with properly-designed shielding, resulting in negligible broadband noise and minimal periodic RF noise in both modalities. US can be used for real time display of the needle trajectory, while MRI can be used to confirm needle placement.
A software strategy to provide intuitive navigation for MRI-guided robotic transperineal prostate therapy is presented. In the system, the robot control unit, the MRI scanner, and open-source navigation software are connected to one another via Ethernet to exchange commands, coordinates, and images. Six states of the system called "workphases" are defined based on the clinical scenario to synchronize behaviors of all components. The wizard-style user interface allows easy following of the clinical workflow. On top of this framework, the software provides features for intuitive needle guidance: interactive target planning; 3D image visualization with current needle position; treatment monitoring through real-time MRI. These features are supported by calibration of robot and image coordinates by the fiducial-based registration. The performance test shows that the registration error of the system was 2.6 mm in the prostate area, and it displayed real-time 2D image 1.7 s after the completion of image acquisition.
Alzheimer's disease is a neurodegenerative disorder typified by the accumulation of a small protein, beta-amyloid, which aggregates and is the primary component of amyloid plaques. Many new therapeutic and diagnostic agents for reducing amyloid plaques have limited efficacy in vivo because of poor transport across the blood-brain barrier. Here we demonstrate that low-intensity focused ultrasound with a microbubble contrast agent may be used to transiently disrupt the blood-brain barrier, allowing non-invasive, localized delivery of imaging fluorophores and immunotherapeutics directly to amyloid plaques. We administered intravenous Trypan blue, an amyloid staining red fluorophore, and anti-amyloid antibodies, concurrently with focused ultrasound therapy in plaque-bearing, transgenic mouse models of Alzheimer's disease with amyloid pathology. MRI guidance permitted selective treatment and monitoring of plaque-heavy anatomical regions, such as the hippocampus. Treated brain regions exhibited 16.5+/-5.4-fold increase in Trypan blue fluorescence and 2.7+/-1.2-fold increase in anti-amyloid antibodies that localized to amyloid plaques. Ultrasound-enhanced delivery was consistently reproduced in two different transgenic strains (APPswe:PSEN1dE9, PDAPP), across a large age range (9-26 months), with and without MR guidance, and with little or no tissue damage. Ultrasound-mediated, transient blood-brain barrier disruption allows the delivery of both therapeutic and molecular imaging agents in Alzheimer's mouse models, which should aid pre-clinical drug screening and imaging probe development. Furthermore, this technique may be used to deliver a wide variety of small and large molecules to the brain for imaging and therapy in other neurodegenerative diseases.
We present a novel approach for joint clustering and point-by-point mapping of white matter fiber pathways. Knowledge of the point correspondence along the fiber pathways is not only necessary for accurate clustering of the trajectories into fiber bundles, but also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, i.e. spatial mean and variance, and point correspondences. The fiber bundles are modeled by the mean trajectory and its spatial variation. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. The proposed method has the potential to benefit from an anatomical atlas of fiber tracts by incorporating it as prior information in the EM algorithm. The algorithm is also capable of handling outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI.
Two strategies are widely used in parallel MRI to reconstruct subsampled multicoil image data. SENSE and related methods employ explicit receiver coil spatial response estimates to reconstruct an image. In contrast, coil-by-coil methods such as GRAPPA leverage correlations among the acquired multicoil data to reconstruct missing k-space lines. In self-referenced scenarios, both methods employ Nyquist-rate low-frequency k-space data to identify the reconstruction parameters. Because GRAPPA does not require explicit coil sensitivities estimates, it needs considerably fewer autocalibration signals than SENSE. However, SENSE methods allow greater opportunity to control reconstruction quality though regularization and thus may outperform GRAPPA in some imaging scenarios. Here, we employ GRAPPA to improve self-referenced coil sensitivity estimation in SENSE and related methods using very few auto-calibration signals. This enables one to leverage each methods' inherent strength and produce high quality self-referenced SENSE reconstructions.
PURPOSE: To retrospectively assess the magnetic resonance (MR) imaging predictors of success at reducing uterine leiomyoma volume and achieving patient symptom relief 12 months after MR imaging-guided focused ultrasound surgery. MATERIALS AND METHODS: This single-center retrospective analysis of 71 symptomatic fibroids in 66 women was approved by the institutional review board and was HIPAA-compliant. Patients were treated with MR imaging-guided focused ultrasound surgery. The volume of treated fibroid and nonperfused volume (NPV) were calculated with software, while symptom outcome was assessed with a symptom severity score (SSS). Fibroids were classified as hyperintense or hypointense relative to skeletal muscle on pretreatment T2-weighted MR images. RESULTS: Baseline volume of treated fibroids was 255.5 cm(3) +/- 201.7 (standard deviation), and baseline SSS was 61.5 +/- 14.9. Both pretreatment fibroid signal intensity (SI) and posttreatment NPV predicted 12-month volume reduction independently: Fibroids with an NPV of at least 20% or with low SI both showed significantly larger volume reduction (17.0% +/- 13.0 and 17.2% +/- 20.1, respectively) than fibroids with an NPV less than 20% or with high SI (10.7% +/- 18.2 and no significant change, respectively). Patients whose fibroids demonstrated an NPV of at least 20% also experienced a larger decrease in SSS than did patients with fibroids with an NPV less than 20% (50.1% +/- 19.8 vs 32.6% +/- 29.9). CONCLUSION: Fibroids with low SI on pretreatment T2-weighted MR images were more likely to shrink than were ones with high SI. The larger the NPV immediately after treatment, the greater the volume reduction and symptom relief were. These findings may help both in selecting appropriate patients for MR-guided focused ultrasound surgery and in predicting patient outcome.
The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a 'ground truth' or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically, these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labelling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, among others, surface, distance transform or level-set representations of segmentations, and can be used to assess whether or not a rater consistently overestimates or underestimates the position of a boundary.
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.