THREE-DIMENSIONAL IMAGING, SURGICAL PLANNING, AND IMAGE-GUIDED THERAPY

Comparison of 3-Dimensional and Augmented Reality Kidney Models With Conventional Imaging Data in the Preoperative Assessment of Children With Wilms Tumors

IMPORTANCE

Nephron-sparing surgery can be considered in well-defined cases of unilateral and bilateral Wilms tumors, but the surgical procedure can be very challenging for the pediatric surgeon to perform.

OBJECTIVE

To assess the added value of personalized 3-dimensional (3-D) kidney models derived from conventional imaging data to enhance preoperative surgical planning.

DESIGN, SETTING, AND PARTICIPANTS

In a survey study, the conventional imaging data of 10 Dutch children with Wilms tumors were converted to 3-D prints and augmented reality (AR) holograms and a panel of pediatric oncology surgeons (n = 7) assessed the quality of the different imaging methods during preoperative evaluation. Kidney models were created with 3-D printing and AR using a mixed reality headset for visualization.

MAIN OUTCOMES AND MEASURES

Differences in the assessment of 4 anatomical structures (tumor, arteries, veins, and urinary collecting structures) using questionnaires. A Likert scale measured differences between the imaging methods, with scores ranging from 1 (completely disagree) to 5 (completely agree).

RESULTS

Of the 10 patients, 7 were girls, and the mean (SD) age was 3.7 (1.7) years. Compared with conventional imaging, the 3-D print and the AR hologram models were evaluated by the surgeons to be superior for all anatomical structures: tumor (median scores for conventional imaging, 4.07; interquartile range [IQR], 3.62-4.15 vs 3-D print, 4.67; IQR, 4.14-4.71; P = .008 and AR hologram, 4.71; IQR, 4.26-4.75; P = .002); arteries (conventional imaging, 3.62; IQR, 3.43-3.93 vs 3-D print, 4.54; IQR, 4.32-4.71; P = .002 and AR hologram, 4.83; IQR, 4.64-4.86; P < .001), veins (conventional imaging, 3.46; IQR 3.39-3.62 vs 3-D print, 4.50; IQR, 4.39-4.68; P < .001 and AR hologram, 4.83; IQR, 4.71-4.86; P < .001), and urinary collecting structures (conventional imaging, 2.76; IQR, 2.42-3.00 vs 3-D print, 3.86; IQR, 3.64-4.39; P < .001 and AR hologram, 4.00; IQR, 3.93-4.58; P < .001). There were no differences in anatomical assessment between the two 3-D techniques (the 3-D print and AR hologram).

CONCLUSIONS AND RELEVANCE

In this study, the 3-D kidney models were associated with improved anatomical understanding among the surgeons and can be helpful in future preoperative planning of nephron-sparing surgery for Wilms tumors. These models may be considered as a supplementary visualization in clinical care.

Toward Holographic-Guided Surgery

The implementation of augmented reality (AR) in image-guided surgery (IGS) can improve surgical interventions by presenting the image data directly on the patient at the correct position and in the actual orientation. This approach can resolve the switching focus problem, which occurs in conventional IGS systems when the surgeon has to look away from the operation field to consult the image data on a 2-dimensional screen. The Microsoft HoloLens, a head-mounted AR display, was combined with an optical navigation system to create an AR-based IGS system. Experiments were performed on a phantom model to determine the accuracy of the complete system and to evaluate the effect of adding AR. The results demonstrated a mean Euclidean distance of 2.3 mm with a maximum error of 3.5 mm for the complete system. Adding AR visualization to a conventional system increased the mean error by 1.6 mm. The introduction of AR in IGS was promising. The presented system provided a solution for the switching focus problem and created a more intuitive guidance system. With a further reduction in the error and more research to optimize the visualization, many surgical applications could benefit from the advantages of AR guidance.

Reference-free error estimation for multiple measurement methods

We present a computational framework to select the most accurate and precise method of measurement of a certain quantity, when there is no access to the true value of the measurand. A typical use case is when several image analysis methods are applied to measure the value of a particular quantitative imaging biomarker from the same images. The accuracy of each measurement method is characterized by systematic error (bias), which is modeled as a polynomial in true values of measurand, and the precision as random error modeled with a Gaussian random variable. In contrast to previous works, the random errors are modeled jointly across all methods, thereby enabling the framework to analyze measurement methods based on similar principles, which may have correlated random errors. Furthermore, the posterior distribution of the error model parameters is estimated from samples obtained by Markov chain Monte-Carlo and analyzed to estimate the parameter values and the unknown true values of the measurand. The framework was validated on six synthetic and one clinical dataset containing measurements of total lesion load, a biomarker of neurodegenerative diseases, which was obtained with four automatic methods by analyzing brain magnetic resonance images. The estimates of bias and random error were in a good agreement with the corresponding least squares regression estimates against a reference.