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Background: Computed 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. Purpose: In this work, we propose an image‐based neural network (CNN) model for metal artifact reduction for CT‐guided interventional procedures. Methods: An 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. Results: Reader 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. Conclusions: The 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. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to evaluate the role of adrenal venous sampling (AVS) in guiding the management of patients with corticotropin (ACTH)‐independent glucocorticoid secretory autonomy and bilateral adrenal masses. Design and Patients: A cohort with 25 patients underwent AVS and surgical management. Measurements: Cortisol was measured from the adrenal veins (AVs) and inferior vena cava (IVC). AV/IVC cortisol ratio and cortisol lateralization ratio (CLR) (dominant AV cortisol concentration divided by the nondominant AV cortisol concentration) were calculated. Posthoc receiver‐operating characteristic curves were generated to determine the specificity of revised AV/IVC cortisol ratio and CLR in differentiating unilateral from bilateral disease. Results: Patients underwent unilateral (n = 21) or bilateral (n = 4) adrenalectomy. The mean AV/IVC cortisol ratio for unilateral adrenalectomy was 12.1 ± 9.6 (dominant) and 4.7 ± 3.8 (contralateral) with a mean CLR of 3.6 ± 3.5. The mean AV/IVC cortisol ratio for bilateral adrenalectomy was 7.5 ± 2.1, with a mean CLR of 1.1 ± 0.6. At a mean follow‐up of 22 months, one patient who underwent unilateral adrenalectomy for the predicted bilateral disease developed recurrent mild autonomous cortisol secretion. Posthoc analyses demonstrated a specificity of 95%–100% for unilateral disease with AV/IVC cortisol ratio >9 for one side, <2.0 for the opposite side and a CLR > 2.3. The specificity was 80%–90% for bilateral disease with AV/IVC cortisol ratio >5.1 bilaterally and a CLR < 1.1. Conclusions: Among patients with bilateral adrenal masses and ACTH‐independent autonomous cortisol secretion, AVS can distinguish between unilateral and bilateral disease with high specificity and may guide surgical management. [ABSTRACT FROM AUTHOR]

This study investigated the strength of sensory and cognitive components involved in musical priming. In Experiment 1, the harmonic function of the target chord and the number of pitch classes shared by the prime sequence and the target chord were manipulated. In Experiment 2, the temporal course of sensory and cognitive priming was investigated. For both musician and nonmusician listeners, cognitive priming systematically overruled sensory priming even at fast and very fast tempi (300 ms and 150 ms per chord). Cognitive priming continued to challenge sensory priming processes at extremely fast tempo (75 ms per chord) but only for participants who began the experimental session with slower tempi. This outcome suggests that the cognitive component is a fast-acting component that competes with sensory priming.