Deep convolutional‐neural‐network‐based metal artifact reduction for CT‐guided interventional oncology procedures (MARIO)
Authors: Cao Wenchao , Parvinian Ahmad , Adamo Daniel , Welch Brian , Callstrom Matthew , Ren Liqiang , Missert Andrew , Favazza Christopher P. 
Published: February 2024

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.