3D printing is emerging as a new diagnostic tool for pre-surgical planning. 3D printed models are extremely advantageous to surgeons in their preoperative planning. Handling these physical replicas engages active spatial perception skills, enabling a more comprehensive understanding of the presented information in an inherently intuitive manner that cannot be achieved with conventional methods of imaging review that use screen based 2D and volume rendered representations. The investigators are developing a novel technique to create 3D models derived directly from extremely high-resolution medical images that are superior in spatial and contrast resolution to current 3D modelling methods. This produces patient specific models that contain previously unachievable special fidelity and soft tissue differentiation.

Investigators hypothesize that the preoperative use of these new diagnostic quality models will reduce surgical time and improve post-surgical outcomes in the near future. This prospective project will optimize the quality of these 3D models to create highly useful pre-surgical models. Investigators will target those subspecialist areas of the multidisciplinary surgical and imaging team where it is believed these models will have the most impact. The proposed prospective study has two major goals: 1) Investigate the use of uncompressed, ultrahigh resolution CT/MR datasets to produce diagnostic 3D models with identical spatial/contrast resolution to the acquired datasets in the target areas of congenital cardiothoracic surgery, neurosurgical tumor resection and nephrectomy. 2) Compare the accuracy of this innovative method for 3D printing to radiological images and pathological data when available.

The tangible outcome of this research will be measured in a comparative study. The investigators will compare the 3D printed models, resulting from the development of this iterative process of generating models from ultra-high resolution medical images and review by all parties, to radiological images and pathological data. The study team will employ non-biased methods to quantify the results of this comparative study. There will be no randomization and all subjects will receive the same treatments and all outcomes are data focused. Models will not be used for patient treatment and will be used only as to aid preparation and will not be used as a therapeutic device. For this reason, the study team will not prospectively consent subjects.

Inclusion Criteria:

• - Patients seen at UCHealth/CHCO and NOC sites between 6/1/2021 - 6/1/2026
• Pregnant women and their fetuses

Exclusion Criteria:

• - Patients 65 and older