Favazza, Christopher P

Objective: The purpose of this study was to evaluate the potential clinical value of spectral CT for image guidance during percutaneous thermal ablation, particularly investigating high monoenergetic (MonoE) for metal artifact reduction. Materials and Methods: A phantom experiment was performed in which an ablation probe was scanned in “continuous CT” (CCT) mode on a spectral CT scanner. ROI measurements quantified metal artifacts for conventional and MonoE CCT images. Subsequently, a retrospective reader study was conducted with 10 patient cases. Three radiologists reviewed cases to select the optimal MonoE setting and perform a Likert score rating (+2: MonoE preferred, −2: conventional preferred). Results: Phantom measurements indicated a reduction in metal artifact for high MonoE (150 keV: −46 HU) relative to conventional CCT (−122 HU). The MonoE setting preference was 151±21 keV. The mean Likert score was 1.2±0.7. Radiologists indicated a perceived benefit of high MonoE CCT when probe placement was near a critical structure. Conclusions: This study indicates a clinical preference for high MonoE CCT compared with conventional CCT. Insofar as spectral CCT can be offered at a clinically feasible time frame, our results support the assessment of 150 keV image to reduce metal artifact while maintaining satisfactory soft tissue contrast.

Abstract Objective . Conventional computed tomography (CT) imaging does not provide quantitative information on local thermal changes during percutaneous ablative therapy of cancerous and benign tumors, aside from few qualitative, visual cues. In this study, we have investigated changes in CT signal across a wide range of temperatures and two physical phases for two different tissue mimicking materials, each. Approach . A series of experiments were conducted using an anthropomorphic phantom filled with water-based gel and olive oil, respectively. Multiple, clinically used ablation devices were applied to locally cool or heat the phantom material and were arranged in a configuration that produced thermal changes in regions with inconsequential amounts of metal artifact. Eight fiber optic thermal sensors were positioned in the region absent of metal artifact and were used to record local temperatures throughout the experiments. A spectral CT scanner was used to periodically acquire and generate electron density weighted images. Average electron density weighted values in 1 mm 3 volumes of interest near the temperature sensors were computed and these data were then used to calculate thermal volumetric expansion coefficients for each material and phase. Main results . The experimentally determined expansion coefficients well-matched existing published values and variations with temperature—maximally differing by 5% of the known value. As a proof of concept, a CT-generated temperature map was produced during a heating time point of the water-based gel phantom, demonstrating the capability to map changes in electron density weighted signal to temperature. Significance . This study has demonstrated that spectral CT can be used to estimate local temperature changes for different materials and phases across temperature ranges produced by thermal ablations.

AbstractBackgroundComputed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT‐guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal‐induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions.PurposeIn this work, we propose an image‐based neural network (CNN) model for metal artifact reduction for CT‐guided interventional procedures.MethodsAn image domain metal artifact simulation framework was developed and validated for deep‐learning‐based metal artifact reduction for interventional oncology (MARIO). CT scans were acquired for 19 different cryoablation probe configurations. The probe configurations varied in the number of probes and the relative orientations. A combination of intensity thresholding and masking based on maximum intensity projections (MIPs) was used to segment both the probes only and probes + artifact in each phantom image. Each of the probe and probe + artifact images were then inserted into 19 unique patient exams, in the image domain, to simulate metal artifact appearance for CT‐guided interventional oncology procedures. The resulting 361 pairs of simulated image volumes were partitioned into disjoint training and test datasets of 304 and 57 volumes, respectively. From the training partition, 116 600 image patches with a shape of 128 × 128 × 5 pixels were randomly extracted to be used for training data. The input images consisted of a superposition of the patient and probe + artifact images. The target images consisted of a superposition of the patient and probe only images. This dataset was used to optimize a U‐Net type model. The trained model was then applied to 50 independent, previously unseen CT images obtained during renal cryoablations. Three board‐certified radiologists with experience in CT‐guided ablations performed a blinded review of the MARIO images. A total of 100 images (50 original, 50 MARIO processed) were assessed across different aspects of image quality on a 4‐point likert‐type item. Statistical analyses were performed using Wilcoxon signed‐rank test for paired samples.ResultsReader scores were significantly higher for MARIO processed images compared to the original images across all metrics (all p < 0.001). The average scores of the overall image quality, iceball conspicuity, overall metal artifact, needle tip visualization, target region confidence, and worst metal artifact, needle tip visualization, iceball conspicuity, and target region confidence improved by 34.91%, 36.29%, 39.94%, 34.17%, 35.13%, and 45.70%, respectively.ConclusionsThe proposed method of image‐based metal artifact simulation can be used to train a MARIO algorithm to effectively reduce probe‐related metal artifacts in CT‐guided cryoablation procedures.

AbstractBackgroundPrior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X‐ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects.PurposeThis work builds up on prior established CHO methods and introduces a new approach to switch from the already established “multiple‐sample” CHO implementation to a “single‐sample” technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index.MethodsTo assess the proposed method, two image data sets were acquired with a clinical X‐ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple‐sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD) levels.ResultsFor the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single‐sample approach versus the multiple‐sample method was 0.997 ± 0.056 (range 0.884–1.159). The average single‐direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple‐sample method was 0.38 ± 0.43 (range 0.03–2.20). For the single‐sample approach, the average width was 2.52 ± 0.63 (range 1.11–5.44). For the anthropomorphic background image set, the results were consistent with classical quantum‐limited signal‐to‐noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphic background.ConclusionsA new single‐sample variant of the CHO model to assess the performance of X‐ray angiography imaging systems is proposed. The new approach is consistent with quantum‐limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinical care.

AbstractPurposeTo determine the suitability of a quality assurance (QA) program based on the American College of Radiology's (ACR) CT quality control (QC) manual to fully evaluate the unique capabilities of a clinical photon‐counting‐detector (PCD) CT system.MethodsA daily QA program was established to evaluate CT number accuracy and artifacts for both standard and ultra‐high‐resolution (UHR) scan modes. A complete system performance evaluation was conducted in accordance with the ACR CT QC manual by scanning the CT Accreditation Phantom with routine clinical protocols and reconstructing low‐energy‐threshold (T3D) and virtual monoenergetic images (VMIs) between 40 and 120 keV. Spatial resolution was evaluated by computing the modulation transfer function (MTF) for the UHR mode, and multi‐energy performance was evaluated by scanning a body phantom containing four iodine inserts with concentrations between 2 and 15 mg I/cc.ResultsThe daily QA program identified instances when the detector needed recalibration or replacement. CT number accuracy was impacted by image type: CT numbers at 70 keV VMI were within the acceptable range (defined for 120 kV). Other keV VMIs and the T3D reconstruction had at least one insert with CT number outside the acceptable range. The limiting resolution was nearly 40 lp/cm based on MTF measurements, which far exceeds the 12 lp/cm maximum capability of the ACR phantom. The CT numbers in the iodine inserts were accurate on all VMIs (3.8% average percentage error), while the iodine concentrations had an average root mean squared error of 0.3 mg I/cc.ConclusionProtocols and parameters must be properly selected on PCD‐CT to meet current accreditation requirements with the ACR CT phantom. Use of the 70 keV VMI allowed passing all tests prescribed in the ACR CT manual. Additional evaluations such an MTF measurement and multi‐energy phantom scans are also recommended to comprehensively evaluate PCD‐CT scanner performance.

PurposeChannelized Hotelling observer (CHO) models have been implemented to assess imaging performance in x‐ray angiography systems. While current methods are appropriate for assessing unprocessed images of moving test objects upon uniform‐exposure backgrounds, they are inadequate for assessing conditions which more appropriately mimic clinical imaging conditions including the combination of moving test objects, complex anthropomorphic backgrounds, and image processing. In support of this broad goal, the purpose of this work was to develop theory and methods to automatically select a subset of task‐specific efficient Gabor channels from a task‐generic Gabor channel base set. Also, previously described theory and methods to manage detectability index (d′) bias due to nonrandom temporal variations in image electronic noise will be revisited herein.MethodsStarting with a base set of 96 Gabor channels, backward elimination of channels was used to automatically identify an “efficient” channel subset which reduced the number of channels retained in the subset while maintaining the magnitude of the d′ estimate. The concept of a pixelwise Hotelling observer (PHO) model was introduced and similarly implemented to assess the performance of the efficient‐channel CHO model. Bias in d′ estimates arising from temporally variable nonstationary noise was modeled as a bivariate probability density function for normal distributions, where one variable corresponds to the signal from the test object and the other variable corresponds to the signal from temporally variable nonstationary noise. Theory and methods were tested on uniform‐exposure unprocessed angiography images with detector target dose (DTD) of 6, 18, and 120 nGy containing static disk‐shaped test objects with diameter in the range of 0.5 to 4 mm.ResultsConsidering all DTD levels and test object sizes, the proposed method reduced the number of Gabor channels in the final subset by 63–82% compared to the original 96 Gabor channel base set, while maintaining a mean relative performance () of 95% 4% with respect to the reference PHO model. Experimental results demonstrated that the bivariate approach to account for bias due to temporally variable nonstationary noise resulted in improved correlation between the CHO and PHO models as compared to a previously proposed univariate approach.ConclusionsComputationally efficient backward elimination can be used to select an efficient subset of Gabor channels from an initial channel base set without substantially compromising the magnitude of the d′ estimate. Bias due to temporally variable nonstationary noise can be modeled through a bivariate approach leading to an improved unbiased estimate of d′.

Abstract Objective The quality of all clinical MRI is dependent on B0 homogeneity, which is optimized during the shimming part of a prescan or preparatory phase before image acquisition. The purpose of this study was to assess shimming techniques clinically employed for breast MRI across our practice, and to determine factors that correlate with higher image quality for contrast-enhanced breast MRI at 1.5T. Methods One hundred consecutive female patients were retrospectively collected with Institutional Review Board approval. Shimming-related parameters, including shim-box placement and shimming gradient offsets were extracted from prior contrast-enhanced 3D fat-suppressed T1-weighted gradient echo image acquisitions. Three breast radiologists evaluated these images for fat saturation, breast density, overall image quality, and artifacts. Technologist experience was also evaluated for variability of shimming. Generalized linear mixed models were used to compare acquisition parameters between fat saturation. P &lt; 0.05 was considered as statistical significance. Results The percentage of soft tissue inside the field of view (FOV) (ie, Tissue/FOV) in the good fat-saturation group (0.37 ± 0.06) was significantly lower (P &lt; 0.01) than that in the poor fat-saturation group (0.39 ± 0.06). Other shimming-related parameters were found not significantly affecting the fat-saturation outcomes. Technologists with more experience tended to have less variable shimming performance than junior technologists did. Conclusions The quality of clinical MRI and especially breast MRI is highly dependent on shimming. Decreasing Tissue/FOV was associated with good image quality (good fat saturation). Optimization of shimming may require manual shimming or higher-order field-correction strategies.

AbstractWe present the development of a two‐component magnetic resonance (MR) fiducial system, that is, a fiducial marker device combined with an auto‐segmentation algorithm, designed to be paired with existing ultrasound probe tracking and image fusion technology to automatically fuse MR and ultrasound (US) images. The fiducial device consisted of four ~6.4 mL cylindrical wells filled with 1 g/L copper sulfate solution. The algorithm was designed to automatically segment the device in clinical abdominal MR images. The algorithm's detection rate and repeatability were investigated through a phantom study and in human volunteers. The detection rate was 100% in all phantom and human images. The center‐of‐mass of the fiducial device was robustly identified with maximum variations of 2.9 mm in position and 0.9° in angular orientation. In volunteer images, average differences between algorithm‐measured inter‐marker spacings and actual separation distances were 0.53 ± 0.36 mm. “Proof‐of‐concept” automatic MR‐US fusions were conducted with sets of images from both a phantom and volunteer using a commercial prototype system, which was built based on the above findings. Image fusion accuracy was measured to be within 5 mm for breath‐hold scanning. These results demonstrate the capability of this approach to automatically fuse US and MR images acquired across a wide range of clinical abdominal pulse sequences.

Abstract BACKGROUND AND IMPORTANCE Laser interstitial thermal therapy (LiTT) has increasingly been used as a treatment option for medically refractory epilepsy, tumors, and radiation necrosis. The use of LiTT requires intraoperative magnetic resonance (MR) thermography. This can become an issue in patients with other implanted therapeutic devices such as pacemakers and vagal nerve stimulators due to concerns regarding increases in the specific absorption rate (SAR). This is a technical case report demonstrating a successfully and safely performed LiTT in a 1.5-T magnetic resonance imaging (MRI) in a patient with a pacemaker for mesial temporal sclerosis. CLINICAL PRESENTATION An 83-yr-old gentleman who had an implanted cardiac pacemaker presented with medically intractable epilepsy and was confirmed to have mesial temporal sclerosis on imaging. Video electroencephalography demonstrated concordant ipsilateral seizures and semiology. He underwent LiTT for ablation of the mesial temporal lobe. This was performed with the below described protocol with a cardiology nurse monitoring the patient's cardiac condition and a physicist monitoring SAR, and MR imaging quality without any adverse events. CONCLUSION This study reports on a protocol of cardiac and MR SAR to safely perform MR-guided LiTT in the setting of traditional pacemakers in patients who are not pacemaker dependent.