Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. These six tasks were performed by five healthy volunteers and functional images were obtained using a T(*)(2)-weighted echo planar imaging (EPI) sequence. Feature vectors from activation maps, necessary for the classification of neural activity, were automatically extracted from the regions that were consistently and exclusively activated for a given task during the training process. Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean)+/-14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control.
BACKGROUND: A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning.
RESULTS: We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications.
CONCLUSION: Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.
The diffusion coefficient of lipids, D(l), within bone marrow, fat deposits and metabolically active intracellular lipids in vivo will depend on several factors including the precise chemical composition of the lipid distribution (chain lengths, degree of unsaturation, etc.) as well as the temperature. As such, D(l) may ultimately prove of value in assessing abnormal fatty acid distributions linked to diseases such as cystic fibrosis, diabetes and coronary heart disease. A sensitive temperature dependence of D(l) may also prove of value for MR-guided thermal therapies for bone tumors or disease within other fatty tissues like the breast. Measuring diffusion coefficients of high molecular weight lipids in vivo is, however, technically difficult for a number of reasons. For instance, due to the much lower diffusion coefficients compared to water, much higher b factors than those used for central nervous system applications are needed. In addition, the pulse sequence design must incorporate, as much as possible, immunity to motion, susceptibility and chemical shift effects present whenever body imaging is performed. In this work, high b-factor line scan diffusion imaging sequences were designed, implemented and tested for D(l) measurement using a 4.7-T horizontal bore animal scanner. The gradient set available allowed for b factors as high as 0.03 micros/nm(2) (30,000 s/mm(2)) at echo times as short as 42 ms. The methods were used to measure lipid diffusion coefficients within the marrow of rat paws in vivo, yielding lipid diffusion coefficients approximately two orders of magnitude smaller than typical tissue water diffusion coefficients. Phantom experiments that demonstrate the sensitivity of lipid diffusion coefficients to chain length and temperature were also performed.
MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n=10 patients with schizophrenia: 56+/-15 years; n=10 controls: 51+/-20 years) (1.5 T GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest.
We introduce an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts. Using subject-specific tractography bundle segmentations, we generate an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this paper we present a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements. We propose a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. We report TBM results of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower lambda(3), the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher lambda(1), the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, we demonstrate the anatomical locations of the interhemispheric differences. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy.
PURPOSE: To investigate the utility of a proposed clinical diffusion imaging scheme for rapidly generating multiple b-value diffusion contrast in brain magnetic resonance imaging (MRI) with high signal-to-noise ratio (SNR).
MATERIALS AND METHODS: Our strategy for efficient image acquisition relies on the invariance property of the diffusion tensor eigenvectors to b-value. A simple addition to the conventional diffusion tensor MR imaging (DTI) data acquisition scheme used for tractography yields diffusion-weighted images at twice and three times the conventional b-value. An example from a neurosurgical brain tumor is shown. Apparent diffusion-weighted (ADW) images were calculated for b-values 800, 1600, and 2400 s/mm(2), and a map of excess diffusive kurtosis was computed from the three ADWs.
RESULTS: High b-value ADW images demonstrated decreased contrast between normal gray and white matter, while the heterogeneity and contrast of the lesion was emphasized relative to conventional b-value data. Kurtosis maps indicated the deviation from Gaussian diffusive behavior.
CONCLUSION: DTI data with multiple b-values and good SNR can be acquired in clinically reasonable times. High b-value ADW images show increased contrast and add information to conventional DWI. Ambiguity in conventional b-value images over whether hyperintense signal results from abnormally low diffusion, or abnormally long T(2), is better resolved in high b-value images.
MRI-guided focused ultrasound (MRgFUS) surgery is a noninvasive thermal ablation method that uses magnetic resonance imaging (MRI) for target definition, treatment planning, and closed-loop control of energy deposition. Integrating FUS and MRI as a therapy delivery system allows us to localize, target, and monitor in real time, and thus to ablate targeted tissue without damaging normal structures. This precision makes MRgFUS an attractive alternative to surgical resection or radiation therapy of benign and malignant tumors. Already approved for the treatment of uterine fibroids, MRgFUS is in ongoing clinical trials for the treatment of breast, liver, prostate, and brain cancer and for the palliation of pain in bone metastasis. In addition to thermal ablation, FUS, with or without the use of microbubbles, can temporarily change vascular or cell membrane permeability and release or activate various compounds for targeted drug delivery or gene therapy. A disruptive technology, MRgFUS provides new therapeutic approaches and may cause major changes in patient management and several medical disciplines.
Neurosurgical diagnosis and intervention has evolved through improved neuroimaging, allowing better visualization of anatomy and pathology. This article discusses the various systems that have been designed over the last decade to meet the requirements of neurosurgical patients and opines on the potential future developments in the technology and application of intraoperative MRI. Because the greatest amount of experience with intraoperative MRI comes from its use in brain tumor resection, this article focuses on the origins of intraoperative MRI in relation to this field.
Conventional spectral-spatial pulses used for water-selective excitation in proton resonance frequency-shift MR thermometry require increased sequence length compared to shorter wideband pulses. This is because spectral-spatial pulses are longer than wideband pulses, and the echo time period starts midway through them. Therefore, for a fixed echo time, one must increase sequence length to accommodate conventional spectral-spatial pulses in proton resonance frequency-shift thermometry. We introduce improved water-selective spectral-spatial pulses for which the echo time period starts near the beginning of excitation. Instead of requiring increased sequence length, these pulses extend into the long echo time periods common to PRF sequences. The new pulses therefore alleviate the traditional tradeoff between sequence length and fat suppression. We experimentally demonstrate an 11% improvement in frame rate in a proton resonance frequency imaging sequence compared to conventional spectral-spatial excitation. We also introduce a novel spectral-spatial pulse design technique that is a hybrid of previous model- and filter-based techniques and that inherits advantages from both. We experimentally validate the pulses' performance in suppressing lipid signal and in reducing sequence length compared to conventional spectral-spatial pulses.
Traditional non-rigid registration algorithms are incapable of accurately registering intra-operative with pre-operative images whenever tissue has been resected or retracted. In this work we present methods for detecting and handling retraction and resection. The registration framework is based on the bijective Demons algorithm using an anisotropic diffusion smoother. Retraction is detected at areas of the deformation field with high internal strain and the estimated retraction boundary is integrated as a diffusion boundary in the smoother to allow discontinuities to develop across the resection boundary. Resection is detected by a level set method evolving in the space where image intensities disagree. The estimated resection is integrated into the smoother as a diffusion sink to restrict image forces originating inside the resection from being diffused to surrounding areas. In addition, the deformation field is continuous across the diffusion sink boundary which allow us to move the boundary of the diffusion sink without changing values in the deformation field (no interpolation or extrapolation is needed). We present preliminary results on both synthetic and clinical data which clearly shows the added value of explicitly modeling these processes in a registration framework.
PURPOSE: To determine if focused ultrasonography (US) combined with a diagnostic microbubble-based US contrast agent can be used to modulate glomerular ultrafiltration and size selectivity.
MATERIALS AND METHODS: The experiments were approved by the animal care committee. The left kidney of 17 healthy rabbits was sonicated by using a 260-kHz focused US transducer in the presence of a microbubble-based US contrast agent. The right kidney served as the control. Three acoustic power levels were applied: 0.4 W (six rabbits), 0.9 W (six rabbits), and 1.7 W (five rabbits). Three rabbits were not treated with focused US and served as control animals. The authors evaluated changes in glomerular size selectivity by measuring the clearance rates of 3000- and 70,000-Da fluorescence-neutral dextrans. The creatinine clearance was calculated for estimation of the glomerular filtration rate. The urinary protein-creatinine ratio was monitored during the experiments. The authors assessed tubular function by evaluating the fractional sodium excretion, tubular reabsorption of phosphate, and gamma-glutamyltransferase-creatinine ratio. Whole-kidney histologic analysis was performed. For each measurement, the values obtained before and after sonication were compared by using the paired t test.
RESULTS: Significant (P < .05) increases in the relative (ratio of treated kidney value/nontreated kidney value) clearance of small- and large-molecule agents and the urine flow rates that resulted from the focused US treatments were observed. Overall, 1.23-, 1.23-, 1.61-, and 1.47-fold enhancement of creatinine clearance, 3000-Da dextran clearance, 70 000-Da dextran clearance, and urine flow rate, respectively, were observed. Focal tubular hemorrhage and transient functional tubular alterations were observed at only the highest (1.7-W) acoustic power level tested.
CONCLUSION: Glomerular ultrafiltration and size selectivity can be temporarily modified with simultaneous application of US and microbubbles. This method could offer new opportunities for treatment of renal disease.
In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learnt from data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learnt from training data without supervision, they can be used as priors to cluster/classify fibers of new subjects. When clustering fibers of new subjects, new clusters can be created for structures not observed in the training data. Our approach does not require computing pairwise distances between fibers and can cluster a huge set of fibers across multiple subjects without subsampling. We present results on multiple data sets, the largest of which has more than 120, 000 fibers.
Thermal imaging measurements using ultrasound phase contrast have been performed in tissue phantoms heated with a focused ultrasound source. Back projection and reflex transmission imaging principles were used to detect sound speed-induced changes in the phase caused by an increase in the temperature. The temperature was determined from an empirical relationship for the temperature dependence on sound speed. The phase contrast was determined from changes in the sound field measured with a hydrophone scan conducted before and during applied heating. The lengthy scanning routine used to mimic a large two-dimensional array required a steady-state temperature distribution within the phantom. The temperature distribution in the phantom was validated with magnetic resonance (MR) thermal imaging measurements. The peak temperature was found to agree within 1 degrees C with MR, and good agreement was found between the temperature profiles. The spatial resolution was 0.3x0.3x0.3mm, comparing favorably with the 0.625x0.625x1.5-mm MR spatial resolution.
One of the most basic trade-offs in ultrasound imaging involves frame rate, depth, and number of lines. Achieving good spatial resolution and coverage requires a large number of lines, leading to decreases in frame rate. An even more serious imaging challenge occurs with imaging modes involving spatial compounding and 3-D/4-D imaging, which are severely limited by the slow speed of sound in tissue. The present work can overcome these traditional limitations, making ultrasound imaging many-fold faster. By emitting several beams at once, and by separating the resulting overlapped signals through spatial and temporal processing, spatial resolution and/or coverage can be increased by many-fold while leaving frame rates unaffected. The proposed approach can also be extended to imaging strategies that do not involve transmit beamforming, such as synthetic aperture imaging. Simulated and experimental results are presented where imaging speed is improved by up to 32-fold, with little impact on image quality. Object complexity has little impact on the method's performance, and data from biological systems can readily be handled. The present work may open the door to novel multiplexed and/or multidimensional protocols considered impractical today.
The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.
We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with the regional approach, FIRE can be efficiently applied to a dense grid of sources. Inspection of our optimization procedure reveals that FIRE is related to the re-weighted minimum-norm algorithms, the difference being that the weights in the proposed approach are computed from both the current estimates and fMRI data. Analysis of both simulated and human fMRI-MEG data shows that FIRE reduces the ambiguities in source localization present in the minimum-norm estimates. Comparisons with several joint fMRI-E/MEG algorithms demonstrate robustness of FIRE in the presence of sources silent to either fMRI or E/MEG measurements.
Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in sampling structure from unsegmented data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an n-D image as an (n+1)-D stack of images at different blurring levels. Our scale-space particles move through continuous four-dimensional scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from a small number of pre-computed blurrings at optimally selected scales. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and the major white matter structures in brain DTI.
Recent papers have demonstrated that acoustic standing waves can be inhibited by frequency-modulated spread-spectrum excitation. An alternative method is studied here that is designed to be more practical for implementation in phased arrays. The method operates using phase-shift-keying (PSK), which introduces phase shifts into the driving signal to break wave symmetry. Sequential and random binary-PSK (BPSK) and quadrature-PSK (QPSK) excitations are studied in water, using a carrier frequency of 250 kHz and a time segment of 10 cycles. The resulting acoustic field is measured with a transducer inside a plastic-walled chamber and compared with continuous wave excitation. Results indicate that both the random BPSK and QPSK methods can reduce time-averaged spatial intensity variation caused by standing waves by approximately six times.
We describe a method for atlas-based segmentation of structural MRI for calculation of magnetic fieldmaps. CT data sets are used to construct a probabilistic atlas of the head and corresponding MR is used to train a classifier that segments soft tissue, air, and bone. Subject-specific fieldmaps are computed from the segmentations using a perturbation field model. Previous work has shown that distortion in echo-planar images can be corrected using predicted fieldmaps. We obtain results that agree well with acquired fieldmaps: 90% of voxel shifts from predicted fieldmaps show subvoxel disagreement with those computed from acquired fieldmaps. In addition, our fieldmap predictions show statistically significant improvement following inclusion of the atlas.
We propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized to determine the maximum a priori (MAP) transform between images, which results in registration that is more robust than the alternatives of omitting locality (i.e. global registration) or jointly maximizing locality and transform (i.e. iconic registration). A mathematical link is established between the Bayesian registration formulation and the mutual information (MI) similarity measure. This leads to a novel technique for selecting informative image regions for registration, based on the MI of image intensity and spatial location. Experimental results demonstrate the effectiveness of the marginalization formulation and the MI-based region selection technique for ultrasound (US) to magnetic resonance (MR) registration in an image-guided neurosurgical application.
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.
OBJECTIVE: We report nine consecutive percutaneous image-guided cryoablation procedures of head and neck tumors in seven patients (four men and three women; mean age, 68 years; age range, 50-78 years). Ablation of the entire tumor for local control or ablation of a region of tumor for pain relief or preservation of function was achieved in eight of nine procedures. One patient experienced intraprocedural bradycardia, and another developed a neopharyngeal abscess. There were no deaths, permanent neurologic or functional deficits, vascular complications, or adverse cosmetic sequelae due to the procedures. CONCLUSION: Percutaneous image-guided cryoablation offers a potentially less morbid minimally invasive treatment option than salvage head and neck surgery. The complications that we encountered may be avoidable with increased experience. Further work is needed to continue improving the safety and efficacy of cryoablation of head and neck tumors and to continue expanding the use of cryoablation in patients with head and neck tumors that cannot be treated surgically.