A method for validating the start-to-end accuracy of a 3-D ultrasound (US)-based patient positioning system for radiotherapy is described. A radiosensitive polymer gel is used to record the actual dose delivered to a rigid phantom after being positioned using 3-D US guidance. Comparison of the delivered dose with the treatment plan allows accuracy of the entire radiotherapy treatment process, from simulation to 3-D US guidance, and finally delivery of radiation, to be evaluated. The 3-D US patient positioning system has a number of features for achieving high accuracy and reducing operator dependence. These include using tracked 3-D US scans of the target anatomy acquired using a dedicated 3-D ultrasound probe during both the simulation and treatment sessions, automatic 3-D US-to-US registration and use of infrared LED (IRED) markers of the optical position-sensing system for registering simulation computed tomography to US data. The mean target localization accuracy of this system was 2.5 mm for four target locations inside the phantom, compared with 1.6 mm obtained using the conventional patient positioning method of laser alignment. Because the phantom is rigid, this represents the best possible set-up accuracy of the system. Thus, these results suggest that 3-D US-based target localization is practically feasible and potentially capable of increasing the accuracy of patient positioning for radiotherapy in sites where day-to-day organ shifts are greater than 1 mm in magnitude.
BACKGROUND: We developed an image-guided robot system to provide mechanical assistance for skull base drilling, which is performed to gain access for some neurosurgical interventions, such as tumour resection. The motivation for introducing this robot was to improve safety by preventing the surgeon from accidentally damaging critical neurovascular structures during the drilling procedure.
METHODS: We integrated a Stealthstation navigation system, a NeuroMate robotic arm with a six-degree-of-freedom force sensor, and the 3D Slicer visualization software to allow the robotic arm to be used in a navigated, cooperatively-controlled fashion by the surgeon. We employed virtual fixtures to constrain the motion of the robot-held cutting tool, so that it remained in the safe zone that was defined on a preoperative CT scan.
RESULTS: We performed experiments on both foam skull and cadaver heads. The results for foam blocks cut using different registrations yielded an average placement error of 0.6 mm and an average dimensional error of 0.6 mm. We drilled the posterior porus acusticus in three cadaver heads and concluded that the robot-assisted procedure is clinically feasible and provides some ergonomic benefits, such as stabilizing the drill. We obtained postoperative CT scans of the cadaver heads to assess the accuracy and found that some bone outside the virtual fixture boundary was cut. The typical overcut was 1-2 mm, with a maximum overcut of about 3 mm.
CONCLUSIONS: The image-guided cooperatively-controlled robot system can improve the safety and ergonomics of skull base drilling by stabilizing the drill and enforcing virtual fixtures to protect critical neurovascular structures. The next step is to improve the accuracy so that the overcut can be reduced to a more clinically acceptable value of about 1 mm.
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.
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.
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.
We describe a method for correcting the distortions present in echo planar images (EPI) and registering the EPI to structural MRI. A fieldmap is predicted from an air / tissue segmentation of the MRI using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration. We obtain results that are similar to those obtained using fieldmaps, however neither fieldmaps, nor knowledge of shim coefficients is required.
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.
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.
Diffusion imaging with high-b factors, high spatial resolution and cerebrospinal fluid signal suppression was performed in order to characterize the biexponential nature of the diffusion-related signal decay with b-factor in normal cortical gray and deep gray matter (GM). Integration of inversion pulses with a line scan diffusion imaging sequence resulted in 91% cerebrospinal fluid signal suppression, permitting accurate measurement of the fast diffusion coefficient in cortical GM (1.142+/-0.106 microm2/ms) and revealing a marked similarity with that found in frontal white matter (WM) (1.155+/-0.046 microm2/ms). The reversal of contrast between GM and WM at low vs high b-factors is shown to be due to a significantly faster slow diffusion coefficient in cortical GM (0.338+/-0.027 microm2/ms) than in frontal WM (0.125+/-0.014 microm2/ms). The same characteristic diffusion differences between GM and WM are observed in other brain tissue structures. The relative component size showed nonsignificant differences among all tissues investigated. Cellular architecture in GM and WM are fundamentally different and may explain the two- to threefold higher slow diffusion coefficient in GM.
Parallel MRI (pMRI) achieves imaging acceleration by partially substituting gradient-encoding steps with spatial information contained in the component coils of the acquisition array. Variable-density subsampling in pMRI was previously shown to yield improved two-dimensional (2D) imaging in comparison to uniform subsampling, but has yet to be used routinely in clinical practice. In an effort to reduce acquisition time for 3D fast spin-echo (3D-FSE) sequences, this work explores a specific nonuniform sampling scheme for 3D imaging, subsampling along two phase-encoding (PE) directions on a rectilinear grid. We use two reconstruction methods-2D-GRAPPA-Operator and 2D-SPACE RIP-and present a comparison between them. We show that high-quality images can be reconstructed using both techniques. To evaluate the proposed sampling method and reconstruction schemes, results via simulation, phantom study, and in vivo 3D human data are shown. We find that fewer artifacts can be seen in the 2D-SPACE RIP reconstructions than in 2D-GRAPPA-Operator reconstructions, with comparable reconstruction times.
OBJECTIVE: Preoperative magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, magnetic resonance spectroscopy, and positron-emission tomographic scans may be aligned to intraoperative MRI to enhance visualization and navigation during image-guided neurosurgery. However, several effects (both machine- and patient-induced distortions) lead to significant geometric distortion of intraoperative MRI. Therefore, a precise alignment of these image modalities requires correction of the geometric distortion. We propose and evaluate a novel method to compensate for the geometric distortion of intraoperative 0.5-T MRI in image-guided neurosurgery. METHODS: In this initial pilot study, 11 neurosurgical procedures were prospectively enrolled. The scheme used to correct the geometric distortion is based on a nonrigid registration algorithm introduced by our group. This registration scheme uses image features to establish correspondence between images. It estimates a smooth geometric distortion compensation field by regularizing the displacements estimated at the correspondences. A patient-specific linear elastic material model is used to achieve the regularization. The geometry of intraoperative images (0.5 T) is changed so that the images match the preoperative MRI scans (3 T). RESULTS: We compared the alignment between preoperative and intraoperative imaging using 1) only rigid registration without correction of the geometric distortion, and 2) rigid registration and compensation for the geometric distortion. We evaluated the success of the geometric distortion correction algorithm by measuring the Hausdorff distance between boundaries in the 3-T and 0.5-T MRIs after rigid registration alone and with the addition of geometric distortion correction of the 0.5-T MRI. Overall, the mean magnitude of the geometric distortion measured on the intraoperative images is 10.3 mm with a minimum of 2.91 mm and a maximum of 21.5 mm. The measured accuracy of the geometric distortion compensation algorithm is 1.93 mm. There is a statistically significant difference between the accuracy of the alignment of preoperative and intraoperative images, both with and without the correction of geometric distortion (P < 0.001). CONCLUSION: The major contributions of this study are 1) identification of geometric distortion of intraoperative images relative to preoperative images, 2) measurement of the geometric distortion, 3) application of nonrigid registration to compensate for geometric distortion during neurosurgery, 4) measurement of residual distortion after geometric distortion correction, and 5) phantom study to quantify geometric distortion.
PURPOSE: To clinically assess a previously described method (Rieke et.al., Magn Reson Med 2004) to produce more motion-robust MRI-based temperature images using data acquired during MRI-guided focused ultrasound surgery (MRgFUS) of uterine fibroids. MATERIALS AND METHODS: The method ("referenceless thermometry") uses surface fitting in nonheated regions of individual phase images to extrapolate and then remove background phase variations that are unrelated to temperature changes. We tested this method using images from 100 sonications selected from 33 patient MRgFUS treatments. Temperature measurements and thermal dose contours estimated with the referenceless method were compared with those produced with the standard phase-difference technique. Fitting accuracy and noise level were also measured. RESULTS: In 92/100 sonications, the difference between the two measurements was less than 3 degrees C. The average difference in the measurements was 1.5 +/- 1.4 degrees C. Small motion artifacts were observed in the phase-difference imaging when the difference was greater than 3 degrees C. The method failed in two cases. The mean absolute error in the surface fit in baseline images corresponded to a temperature error of 0.8 +/- 1.4 degrees C. The noise level was approximately 40% lower than the phase-difference method. Thermal dose contours calculated from the two methods agreed well on average. CONCLUSION: Based on the small error when compared with the standard technique, this method appears to be adequate for temperature monitoring of MRgFUS in uterine fibroids and may prove useful for monitoring temperature changes in moving organs.
Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the language-specific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.
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.
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.
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.
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.
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.