Gynecologic malignancies, including cervical, endometrial, ovarian, vaginal and vulvar cancers, cause significant mortality in women worldwide. The standard care for many primary and recurrent gynecologic cancers consists of chemoradiation followed by brachytherapy. In high dose rate (HDR) brachytherapy, intracavitary applicators and/or interstitial needles are placed directly inside the cancerous tissue so as to provide catheters to deliver high doses of radiation. Although technology for the navigation of catheters and needles is well developed for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is notably lacking for gynecologic HDR brachytherapy. Using a benchtop study that closely mimics the clinical interstitial gynecologic brachytherapy procedure, we developed a method for evaluating the accuracy of image-guided catheter placement. Future bedside translation of this technology offers the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs, for example bladder, rectum and bowel. In the study, two independent experiments were performed on a phantom model to evaluate the targeting accuracy of an electromagnetic (EM) tracking system. The procedure was carried out using a laptop computer (2.1GHz Intel Core i7 computer, 8GB RAM, Windows 7 64-bit), an EM Aurora tracking system with a 1.3mm diameter 6 DOF sensor, and 6F (2 mm) brachytherapy catheters inserted through a Syed-Neblett applicator. The 3D Slicer and PLUS open source software were used to develop the system. The mean of the targeting error was less than 2.9mm, which is comparable to the targeting errors in commercial clinical navigation systems.
PURPOSE: High-fidelity 12-lead electrocardiogram (ECG) is important for physiological monitoring of patients during MR-guided intervention and cardiac MRI. Issues in obtaining noncorrupted ECGs inside MRI include a superimposed magneto-hydro-dynamic voltage, gradient switching-induced voltages, and radiofrequency heating. These problems increase with magnetic field. The aim of this study is to develop and clinically validate a 1.5T MRI-conditional 12-lead ECG system. METHODS: The system was constructed with transmission lines to reduce radiofrequency induction and switching circuits to remove induced voltages. Adaptive filters, trained by 12-lead measurements outside MRI and in two orientations inside MRI, were used to remove the magneto-hydro-dynamic voltage. The system was tested on 10 (one exercising) volunteers and four arrhythmia patients. RESULTS: Switching circuits removed most imaging-induced voltages (residual noise <3% of the R-wave). Magneto-hydro-dynamic voltage removal provided intra-MRI ECGs that varied by <3.8% from those outside the MRI, preserving the true S-wave to T-wave segment. In premature ventricular contraction (PVC) patients, clean ECGs separated premature ventricular contraction and sinus rhythm beats. Measured heating was <1.5°C. The system reliably acquired multiphase (steady-state free precession) wall-motion-cine and phase-contrast-cine scans, including subjects in whom 4-lead gating failed. The system required a minimum repetition time of 4 ms to allow robust ECG processing. CONCLUSION: High-fidelity intra-MRI 12-lead ECG is possible.
PURPOSE: To develop a technique that accurately detects the QRS complex in 1.5 Tesla (T), 3T, and 7T MRI scanners. METHODS: During early systole, blood is rapidly ejected into the aortic arch, traveling perpendicular to the MRI's main field, which produces a strong voltage (V(MHD)) that eclipses the QRS complex. Greater complexity arises in arrhythmia patients, since V(MHD) varies between sinus-rhythm and arrhythmic beats. The 3DQRS method uses a kernel consisting of 6 electrocardiogram (ECG) precordial leads (V1-V6), compiled from a 12-lead ECG performed outside the magnet. The kernel is cross-correlated with signals acquired inside the MRI to identify the QRS complex in real time. The 3DQRS method was evaluated against a vectorcardiogram (VCG)-based approach in two premature ventricular contraction (PVC) and two atrial fibrillation (AF) patients, a healthy exercising athlete, and eight healthy volunteers, within 1.5T and 3T MRIs, using a prototype MRI-conditional 12-lead ECG system. Two volunteers were recorded at 7T using a Holter recorder. RESULTS: For QRS complex detection, 3DQRS subject-averaged sensitivity levels, relative to VCG were: 1.5T (100% versus 96.7%), 3T (98.9% versus 92.2%), and 7T (96.2% versus 77.7%). CONCLUSION: The 3DQRS method was shown to be more effective in cardiac gating than a conventional VCG-based method.
PURPOSE: To reduce image distortion in MR diffusion imaging using an accelerated multi-shot method. METHODS: The proposed method exploits the fact that diffusion-encoded data tend to be sparse when represented in the kb-kd space, where kb and kd are the Fourier transform duals of b and d, the b-factor and the diffusion direction, respectively. Aliasing artifacts are displaced toward under-used regions of the kb-kd plane, allowing nonaliased signals to be recovered. A main characteristic of the proposed approach is how thoroughly the navigator information gets used during reconstruction: The phase of navigator images is used for motion correction, while the magnitude of the navigator signal in kb-kd space is used for regularization purposes. As opposed to most acceleration methods based on compressed sensing, the proposed method reduces the number of ky lines needed for each diffusion-encoded image, but not the total number of images required. Consequently, it tends to be most effective at reducing image distortion rather than reducing total scan time. RESULTS: Results are presented for three volunteers with acceleration factors ranging from 4 to 8, with and without the inclusion of parallel imaging. CONCLUSION: An accelerated motion-corrected diffusion imaging method was introduced that achieves good image quality at relatively high acceleration factors.
Distinguishing tumor from normal glandular breast tissue is an important step in breast-conserving surgery. Because this distinction can be challenging in the operative setting, up to 40% of patients require an additional operation when traditional approaches are used. Here, we present a proof-of-concept study to determine the feasibility of using desorption electrospray ionization mass spectrometry imaging (DESI-MSI) for identifying and differentiating tumor from normal breast tissue. We show that tumor margins can be identified using the spatial distributions and varying intensities of different lipids. Several fatty acids, including oleic acid, were more abundant in the cancerous tissue than in normal tissues. The cancer margins delineated by the molecular images from DESI-MSI were consistent with those margins obtained from histological staining. Our findings prove the feasibility of classifying cancerous and normal breast tissues using ambient ionization MSI. The results suggest that an MS-based method could be developed for the rapid intraoperative detection of residual cancer tissue during breast-conserving surgery.
Deformable image registration is used increasingly in image-guided interventions and other applications. However, validation and characterization of registration performance remain areas that require further study. We propose an analysis methodology for deriving tolerance limits on the initial conditions for deformable registration that reliably lead to a successful registration. This approach results in a concise summary of the probability of registration failure, while accounting for the variability in the test data. The (β, γ) tolerance limit can be interpreted as a value of the input parameter that leads to successful registration outcome in at least 100β% of cases with the 100γ% confidence. The utility of the methodology is illustrated by summarizing the performance of a deformable registration algorithm evaluated in three different experimental setups of increasing complexity. Our examples are based on clinical data collected during MRI-guided prostate biopsy registered using publicly available deformable registration tool. The results indicate that the proposed methodology can be used to generate concise graphical summaries of the experiments, as well as a probabilistic estimate of the registration outcome for a future sample. Its use may facilitate improved objective assessment, comparison and retrospective stress-testing of deformable.
PURPOSE: To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast-enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. MATERIALS AND METHODS: Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise, and fitting algorithms. We modeled the underlying dynamics of the tumor by an LDS and used the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist's segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). RESULTS: The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared with the radiologist's segmentation and 82.1% accuracy and 100% sensitivity when compared with the CADstream output. The overlap of the algorithm output with the radiologist's segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72, respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC = 0.95. CONCLUSION: The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI.
PURPOSE: Simplified models of non-monoexponential diffusion signal decay are of great interest to study the basic constituents of complex diffusion behavior in tissues. The latebra, a unique structure uniformly present in the yolk of avian eggs, exhibits a non-monoexponential diffusion signal decay. This model is more complex than simple phantoms based on differences between water and lipid diffusion, but is also devoid of microscopic structures with preferential orientation or perfusion effects. METHODS: Diffusion scans with multiple b-values were performed on a clinical 3 Tesla system in raw and boiled chicken eggs equilibrated to room temperature. Diffusion encoding was applied over the ranges 5-5,000 and 5-50,000 s/mm(2). A low read-out bandwidth and chemical shift was used for reliable lipid/water separation. Signal decays were fitted with exponential functions. RESULTS: The latebra, when measured over the 5-5,000 s/mm(2) range, exhibited independent of preparation clearly biexponential diffusion, with diffusion parameters similar to those typically observed in in vivo human brain. For the range 5-50,000 s/mm(2), there was evidence of a small third, very slow diffusing water component. CONCLUSION: The latebra of the avian egg contains membrane structures, which may explain a deviation from a simple monoexponential diffusion signal decay, which is remarkably similar to the deviation observed in brain tissue.
Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.
In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.
In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors.
Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision-making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood-oxygen-level dependent (BOLD) signal change while patients perform carefully timed language vs. control tasks. This task-based fMRI critically depends on task performance, excluding many patients who have difficulty performing language tasks due to neurologic deficits. On the basis of recent discovery of resting-state fMRI (rs-fMRI), we propose a "task-free" paradigm acquiring fMRI data when patients simply are at rest. This paradigm is less demanding for patients to perform and easier for technologists to administer. We investigated the feasibility of this approach in right-handed healthy control subjects. First, group independent component analysis (ICA) was applied on the training group (14 subjects) to identify group level language components based on expert rating results. Then, four empirically and structurally defined language network templates were assessed for their ability to identify language components from individuals' ICA output of the testing group (18 subjects) based on spatial similarity analysis. Results suggest that it is feasible to extract language activations from rs-fMRI at the individual subject level, and two empirically defined templates (that focuses on frontal language areas and that incorporates both frontal and temporal language areas) demonstrated the best performance. We propose a semi-automated language component identification procedure and discuss the practical concerns and suggestions for this approach to be used in clinical fMRI language mapping.
Previously, a static and adjustable image overlay systems were proposed for aiding needle interventions. The system was either fixed to a scanner or mounted over a large articulated counterbalanced arm. Certain drawbacks associated with these systems limited the clinical translation. In order to minimize these limitations, we present the mobile image overlay system with the objective of reduced system weight, smaller dimension, and increased tracking accuracy. The design study includes optimal workspace definition, selection of display device, mirror, and laser source. The laser plane alignment, phantom design, image overlay plane calibration, and system accuracy validation methods are discussed. The virtual image is generated by a tablet device and projected into the patient by using a beamsplitter mirror. The viewbox weight (1.0 kg) was reduced by 8.2 times and image overlay plane tracking precision (0.21 mm, STD = 0.05) was improved by 5 times compared to previous system. The automatic self-calibration of the image overlay plane was achieved in two simple steps and can be done away from patient table. The fiducial registration error of the physical phantom to scanned image volume registration was 1.35 mm (STD = 0.11). The reduced system weight and increased accuracy of optical tracking should enable the system to be hand held by the physician and explore the image volume over the patient for needle interventions.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluates the tissue microvasculature and may have a role in assessing and predicting therapeutic response in prostate cancer (PCa). In this review, we review principles of DCE-MRI and present the potential quantitative information that can be obtained. We discuss how it may be used as a biomarker for treatment with antiangiogenic and antivascular agents and potentially identify patients with PCa who may benefit from this form of therapy. Likewise, DCE-MRI may play a role in assessing response to combined androgen deprivation therapy and radiation therapy and theoretically could be a prognostic biomarker in evaluating second-generation hormone therapies. We also address the challenges of using DCE-MRI in PCa clinical trials and discuss the difficulties with standardization of this methodology to allow for biomarker validation, with particular reference to PCa.
Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods.
For many intraoperative decisions surgeons depend on frozen section pathology, a technique developed over 150 y ago. Technical innovations that permit rapid molecular characterization of tissue samples at the time of surgery are needed. Here, using desorption electrospray ionization (DESI) MS, we rapidly detect the tumor metabolite 2-hydroxyglutarate (2-HG) from tissue sections of surgically resected gliomas, under ambient conditions and without complex or time-consuming preparation. With DESI MS, we identify isocitrate dehydrogenase 1-mutant tumors with both high sensitivity and specificity within minutes, immediately providing critical diagnostic, prognostic, and predictive information. Imaging tissue sections with DESI MS shows that the 2-HG signal overlaps with areas of tumor and that 2-HG levels correlate with tumor content, thereby indicating tumor margins. Mapping the 2-HG signal onto 3D MRI reconstructions of tumors allows the integration of molecular and radiologic information for enhanced clinical decision making. We also validate the methodology and its deployment in the operating room: We have installed a mass spectrometer in our Advanced Multimodality Image Guided Operating (AMIGO) suite and demonstrate the molecular analysis of surgical tissue during brain surgery. This work indicates that metabolite-imaging MS could transform many aspects of surgical care.
To explore use of the Magnetohydrodynamic Voltage (VMHD), observed in intra-MRI 12-lead electrocardiograms (ECG), to indicate the timing of the onset of left-ventricular mechanical activation (LVMA) and the orientation of the aortic-arch (AAO). Blood flow through the aortic arch during systole, in the presence of the MRI magnetic field (B 0), generates VMHD. Since the magnitude and direction of VMHD are determined by the timing and directionality of blood flow relative to B 0, we hypothesized that clinically useful measures, LVMA and AAO, could be extracted from temporal and vectorial VMHD characteristics. VMHD signals were extracted from 12-lead ECG traces by comparing traces obtained inside and outside the MRI scanner. VMHD was converted into the Vectorcardiogram frame of reference. LVMA was quantified in 1 subject at 1.5T and 3 subjects at 3T, and the result compared to CINE MRI. AAO was inferred for 4 subjects at 3T and compared to anatomical imaging of the aortic arch orientation in the transverse plane. A < 10% error was observed in LVMA measurements, while a < 3° error was observed in aortic arch orientation measurements. The temporal and vectorial nature of VMHD is useful in estimating these clinically relevant parameters.
Neuronal intracellular chloride concentration [Cl(-)](i) is an important determinant of γ-aminobutyric acid type A (GABA(A)) receptor (GABA(A)R)-mediated inhibition and cytoplasmic volume regulation. Equilibrative cation-chloride cotransporters (CCCs) move Cl(-) across the membrane, but accumulating evidence suggests factors other than the bulk concentrations of transported ions determine [Cl(-)](i). Measurement of [Cl(-)](i) in murine brain slice preparations expressing the transgenic fluorophore Clomeleon demonstrated that cytoplasmic impermeant anions ([A](i)) and polyanionic extracellular matrix glycoproteins ([A](o)) constrain the local [Cl(-)]. CCC inhibition had modest effects on [Cl(-)](i) and neuronal volume, but substantial changes were produced by alterations of the balance between [A](i) and [A](o). Therefore, CCCs are important elements of Cl(-) homeostasis, but local impermeant anions determine the homeostatic set point for [Cl(-)], and hence, neuronal volume and the polarity of local GABA(A)R signaling.
Transcranial MRI-guided focused ultrasound (TcMRgFUS) is an old idea but a new technology that may change the entire clinical field of the neurosciences. TcMRgFUS has no cumulative effect, and it is applicable for repeatable treatments, controlled by real-time dosimetry, and capable of immediate tissue destruction. Most importantly, it has extremely accurate targeting and constant monitoring. It is potentially more precise than proton beam therapy and definitely more cost effective. Neuro-oncology may be the most promising area of future TcMRgFUS applications.
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer (PCa). 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 10 prospective tpMRgBx cases. Mean Landmark Registration Error (LRE) 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.