Advanced 3D Visualization and 3D Printing in Radiology

Research and application discussion of cranial bone model preparation method based on three-dimensional reconstruction and 3D printing technology

PURPOSE

The aim of this study was to find an alternative method to meet traditional human anatomy teaching and clinical needs in order to solve the problem of cranial specimen attrition and specimen resource shortage due to long-term use.

METHODS

We performed a computed tomography (CT) scan of a well-preserved male cranial specimen and used Mimics 19.0 software for 3D reconstruction and cranial block separation. Subsequently, we compared the recognition ability of the processed cranial digital model with that of the 3D body digital model and used 3D printing to create the cranial model and compare it with the physical specimen.

RESULTS

Twenty-two cranial bone block models were obtained, excluding the hyoid bone. Their 3D reconstructed digital models had better bony landmark recognition than the 3D body human digital models, and the differences between the 3D printed models and the physical specimens were minimal. In addition, only one stereolithography (STL) file was required to produce the cranial models, which facilitates repetitive printing at any time.

CONCLUSION

By isolating cranial bone blocks through 3D reconstruction techniques and preparing high-quality cranial models in combination with 3D printing techniques, this study solves the problem of shortage of cranial teaching specimens for the sustainable development of clinical and medical schools.

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Current and emerging 3D visualization technologies in radiology

As the field of three-dimensional (3D) visualization rapidly advances, how healthcare professionals perceive and interact with real and virtual objects becomes increasingly complex. Lack of clear vocabulary to navigate the changing landscape of 3D visualization hinders clinical and scientific advancement, particularly within the field of radiology. In this article, we provide foundational definitions and illustrative examples for 3D visualization in clinical care, with a focus on the pediatric patient population. We also describe how understanding 3D visualization tools enables better alignment of hardware and software products with intended use-cases, thereby maximizing impact for patients, families, and healthcare professionals.

Shaping the Future of Cardiovascular Disease by 3D Printing Applications in Stent Technology and its Clinical Outcomes

Cardiovascular disease (CVD) is a leading cause of death worldwide. In recent years, 3D printing technology has ushered in a new era of innovation in cardiovascular medicine. 3D printing in CVD management encompasses various aspects, from patient-specific models and preoperative planning to customized medical devices and novel therapeutic approaches. In-stent technology, 3D printing has revolutionized the design and fabrication of intravascular stents, offering tailored solutions for complex anatomies and individualized patient needs. The advantages of 3D-printed stents, such as improved biocompatibility, enhanced mechanical properties, and reduced risk of in-stent restenosis. Moreover, the clinical trials and case studies that shed light on the potential of 3D printing technology to improve patient outcomes and revolutionize the field has been comprehensively discussed. Furthermore, regulatory considerations, and challenges in implementing 3D-printed stents in clinical practice are also addressed, underscoring the need for standardization and quality assurance to ensure patient safety and device reliability. This review highlights a comprehensive resource for clinicians, researchers, and policymakers seeking to harness the full potential of 3D printing technology in the fight against CVD.

The Educational Impact of Radiology in Anatomy Teaching: A Field Study Using Cross-Sectional Imaging and 3D Printing for the Study of the Spine

INTRODUCTION

Cross-sectional imaging and 3D printing represent state-of-the-art approaches to improve anatomy teaching compared to traditional learning, but their use in medical schools remains limited. This study explores the utility of these educational tools for teaching normal and pathological spinal anatomy, aiming to improve undergraduate medical education.

MATERIALS AND METHODS

A field study was conducted on a cohort of undergraduate medical students who were exposed to anatomy lessons of the spine considering three learning paradigms: traditional learning, cross-sectional imaging examinations, and 3D printed models. 20 students (intervention group) received the three approaches, and other 20 students (control group) received the conventional (traditional) approach. The students were examined through a multiple-choice test and their results were compared to those of a control group exposed to traditional learning matched by age, sex and anatomy grades. In addition, students in the experimental group were assessed for their satisfaction with each learning method by means of an ad hoc questionnaire.

RESULTS

Students exposed to cross-sectional imaging and 3D printing demonstrated better knowledge outcomes compared to the control group. They showed high satisfaction rates and reported that these technologies enhanced spatial understanding and facilitated visualization of specific pathologies. However, limitations such as the representativeness of non-bone conditions in 3D printed models and the need for further knowledge on imaging fundamentals were highlighted.

CONCLUSION

Cross-sectional imaging and 3D printing offer valuable tools for enhancing the teaching of spinal anatomy in undergraduate medical education. Radiologists are well positioned to lead the integration of these technologies, and further research should explore their potential in teaching anatomy across different anatomical regions.