To retrospectively evaluate the four-year experience of a quality assurance method for a MRI-guided focused ultrasound system that uses temperature maps acquired during heating in an ultrasound/MRI phantom. This quality assurance method was performed before 148 clinical uterine fibroid thermal ablation treatments. The stability of the peak temperature rise, the targeting accuracy, the shape of the heated zone, and the noise level in the imaging was evaluated. The peak temperature rise was mostly stable for the first three years. An increase in heating was observed when the system was replaced after year three. Detection of this increase was taken into account in the subsequent clinical treatments. A small secondary hotspot was detected by the temperature maps and was seen to be resolved after system calibration. The average standard deviation in unheated regions of the phantom in the temperature maps was 0.5 +/- 0.2 degrees C; it was less than 1 degrees C in all but one procedure. The average initial targeting error was 2.8 +/- 1.8 and 2.8 +/- 2.1 mm in two radial directions and 7.7 +/- 2.9 mm along the ultrasound beam direction. The width of the heating profile was consistent over the four years. This simple method to evaluate the performance appeared to be sensitive to small changes in system performance, which was adequately stable over a four-year time period.
This work describes an integrated system for planning and performing percutaneous procedures-such as prostate biopsy-with robotic assistance under MRI-guidance. The physician interacts with a planning interface in order to specify the set of desired needle trajectories, based on anatomical structures and lesions observed in the patient's MR images. All image-space coordinates are automatically computed, and used to position a needle guide by means of an MRI-compatible robotic manipulator, thus avoiding the limitations of the traditional fixed needle template. Direct control of real-time imaging aids visualization of the needle as it is manually inserted through the guide. Results from in-scanner phantom experiments are provided.
This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.
RATIONALE AND OBJECTIVES: When diagnostic tests are repeated and combined, a number of schemes may be adopted. Guidelines for their interpretations are required.
MATERIALS AND METHODS: Three combination schemes, "and" (A), "or" (O), and "majority" (M), are considered. To evaluate these schemes, dependency by specifying kappa values quantifying repeated test agreement was structured. In a pilot study, the combined accuracies of magnetic resonance imaging using six different pulse sequences of medial collateral ligaments of the elbows of 28 cadavers, with eight having lesions artificially created surgically, were examined. Images were evaluated simultaneously by using a five-point ordinal scale. For each pulse sequence, individuals for whom the diagnosis varied from once to three repetitions were considered.
RESULTS: Scheme M improves diagnostic accuracy when sensitivity and specificity of a single test exceed 0.5, with maximal improvement at 0.79. Under scheme A, sensitivity decreases to 0.38-0.59. Under scheme O, sensitivity increases to 0.53-0.79. Scheme M yields a small improvement, reaching 0.50-0.71. Under scheme A, specificity increases to 0.95-0.98. Under scheme O, specificity decreases to 0.91-0.98. Scheme M also yields a small improvement, reaching 0.94-0.98.
CONCLUSION: Scheme A is recommended for ruling in diagnoses, scheme O is recommended for ruling out diagnoses, and scheme M is neutral. Consequently, different schemes may be used to optimize the target diagnostic accuracy.
PURPOSE: To retrospectively assess the main variables that affect the complete magnetic resonance (MR) imaging-guided resection of supratentorial low-grade gliomas.
MATERIALS AND METHODS: Institutional review board approval was obtained for this retrospective HIPAA-compliant study, with the requirement for informed consent waived. Data from 101 patients (61 men, 40 women; mean age, 39 years; age range, 18-72 years) who had nonenhancing supratentorial mass lesions that were histopathologically diagnosed as low-grade (World Health Organization grade II) gliomas and consecutively underwent surgery with intraoperative MR imaging guidance were analyzed. There were 21 low-grade astrocytomas, 64 oligodendrogliomas, and 16 mixed oligoastrocytomas. Initial and residual tumor volumes were measured on intraoperative T2-weighted MR images and three-dimensional spoiled gradient-echo MR images. The anatomic relationships between the tumor and eloquent cortical and/or subcortical regions and the influence of these relationships on the extent of resection were analyzed on the basis of preoperative MR imaging findings. Summary measures, univariate Fisher exact test and t test, and multivariate logistic regression analyses were performed.
RESULTS: Tumor volume ranged from 2.7-231.0 mL. Univariate analyses revealed the following tumor characteristics to be significant predictive variables of incomplete tumor resection: diffuse tumor margin on T2-weighted MR images, oligodendroglioma or oligoastrocytoma histopathologic type, and large tumor volume (P < .05 for all). Tumor involvement of the following structures was associated with incomplete resection: corpus callosum, corticospinal tract, insular lobe, middle cerebral artery, motor cortex, optic radiation, visual cortex, and basal ganglia (P < .05 for all). Multivariate analyses revealed that incomplete tumor resection was due to tumor involvement of the corticospinal tract (P < .01), large tumor volume (P < .01), and oligodendroglioma histopathologic type (P = .02).
CONCLUSION: The main variables associated with incomplete tumor resection in 101 patients were identified by using statistical predictive analyses.
The blood-brain barrier (BBB) is a persistent obstacle for the local delivery of macromolecular therapeutic agents to the central nervous system (CNS). Many drugs that show potential for treating CNS diseases cannot cross the BBB and there is a need for a non-invasive targeted drug delivery method that allows local therapy of the CNS using larger molecules. We developed a non-invasive technique that allows the image-guided delivery of antibody across the BBB into the murine CNS. Here, we demonstrate that subsequent to MRI-targeted focused ultrasound induced disruption of BBB, intravenously administered dopamine D(4) receptor-targeting antibody crossed the BBB and recognized its antigens. Using MRI, we were able to monitor the extent of BBB disruption. This novel technology should be useful in delivering macromolecular therapeutic or diagnostic agents to the CNS for the treatment of various CNS disorders.
PURPOSE: To retrospectively evaluate magnetic resonance (MR) imaging-based thermometry and thermal dosimetry during focused ultrasound treatments of uterine leiomyomas (ie, fibroids).
MATERIALS AND METHODS: All patients gave written informed consent for the focused ultrasound treatments and the current HIPAA-compliant retrospective study, both of which were institutional review board approved. Thermometry performed during the treatments of 64 fibroids in 50 women (mean age, 46.6 years +/- 4.5 [standard deviation]) was used to create thermal dose maps. The areas that reached dose values of 240 and 18 equivalent minutes at 43 degrees C were compared with the nonperfused regions measured on contrast material-enhanced MR images by using the Bland-Altman method. Volume changes in treated fibroids after 6 months were compared with volume changes in nontreated fibroids and with MR-based thermal dose estimates.
RESULTS: While the thermal dose estimates were shown to have a clear relationship with resulting nonperfused regions, the nonperfused areas were, on average, larger than the dose estimates (means of 1.9 +/- 0.7 and 1.2 +/- 0.4 times as large for areas that reached 240- and 18-minute threshold dose values, respectively). Good correlation was observed for smaller treatment volumes at the lower dose threshold (mean ratio, 1.0 +/- 0.3), but for larger treatment volumes, the nonperfused region extended to locations within the fibroid that clearly were not heated. Variations in peak temperature increase were as large as a factor of two, both between patients and within individual treatments. On average, the fibroid volume reduction at 6 months increased as the ablated volume estimated by using the thermal dose increased.
CONCLUSION: Study results showed good correlation between thermal dose estimates and resulting nonperfused areas for smaller ablated volumes. For larger treatment volumes, nonperfused areas could extend within the fibroid to unheated areas.
A method is presented to validate the segmentation of computed tomography (CT) image sequences, and im prove the accuracy and efficiency of the subsequent registration of the 3D surfaces that are reconstructed from the segmented slices. The method compares the shapes of contours extracted from neighborhoods of slices in CT stacks of tibias. The bone is first segmented by an automatic segmentation technique, and the bone contour for each slice is parameterized as a 1-D function of normalized arc length versus inscribed angle. These functions are represented as vectors within a K-dimensional space comprising the first K amplitude coefficients of their Fourier Descriptors. The similarity or coherency of neighboring contours is measured by comparing statistical properties of their vector representations within this space. Experimentation has demonstrated this technique to be very effective at automatically identifying low coherency segmentations, the removal of which significantly improved the accuracy and time efficiency of the registration of 3-D bone surface models.
A method is presented to validate the segmentation of computed tomography (CT) image sequences, and improve the accuracy and efficiency of the subsequent registration of the three-dimensional surfaces that are reconstructed from the segmented slices. The method compares the shapes of contours extracted from neighborhoods of slices in CT stacks of tibias. The bone is first segmented by an automatic segmentation technique, and the bone contour for each slice is parameterized as a one-dimensional function of normalized arc length versus inscribed angle. These functions are represented as vectors within a K-dimensional space comprising the first K amplitude coefficients of their Fourier Descriptors. The similarity or coherency of neighboring contours is measured by comparing statistical properties of their vector representations within this space. Experimentation has demonstrated this technique to be very effective at identifying low-coherency segmentations. Compared with experienced human operators, in a set of 23 CT stacks (1,633 slices), the method correctly detected 87.5% and 80% of the low-coherency and 97.7% and 95.5% of the high coherency segmentations, respectively from two different automatic segmentation techniques. Removal of the automatically detected low-coherency segmentations also significantly improved the accuracy and time efficiency of the registration of 3-D bone surface models. The registration error was reduced by over 500% (i.e., a factor of 5) and 280%, and the computational performance was improved by 540% and 791% for the two respective segmentation methods.
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 to 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 labeling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, amongst others, surface, distance transform or level set representations of segmentations, and can be used to assess whether or not a rater consistently over-estimates or under-estimates 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.