3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

Perk Tutor: an open-source training platform for ultrasound-guided needle insertions

Image-guided needle placement, including ultrasound (US)-guided techniques, have become commonplace in modern medical diagnosis and therapy. To ensure that the next generations of physicians are competent using this technology, efficient and effective educational programs need to be developed. This paper presents the Perk Tutor: a configurable, open-source training platform for US-guided needle insertions. The Perk Tutor was successfully tested in three different configurations to demonstrate its adaptability to different procedures and learning objectives. 1) The Targeting Tutor, designed to develop US-guided needle targeting skills, 2) the Lumbar Tutor, designed for practicing US-guided lumbar spinal procedures, and (3) the Prostate Biopsy Tutor, configured for US-guided prostate biopsies. The Perk Tutor provides the trainee with quantitative feedback on progress toward the specific learning objectives of each configuration. Configurations were implemented through simple rearrangement of hardware and software components, attesting to the modularity and ease of configuration. The Perk Tutor is provided as a free resource to enable research and development of educational programs for US-guided intervention.

MR image overlay guidance: system evaluation for preclinical use

PURPOSE

A clinical augmented reality guidance system was developed for MRI-guided musculoskeletal interventions Magnetic Resonance Image Overlay System (MR-IOS). The purpose of this study was to assess MRI compatibility, system accuracy, technical efficacy, and operator performance of the MR-IOS.

METHODS AND MATERIALS

The impact of the MR-IOS on the MR environment was assessed by measuring image quality with signal-to-noise ratio (SNR) and signal intensity uniformity with the system in various on/off states. The system accuracy was assessed with an in-room preclinical experiment by performing 62 needle insertions on a spine phantom by an expert operator measuring entry, depth, angle, and target errors. Technical efficacy and operator performance were tested in laboratory by running an experiment with 40 novice operators (20 using freehand technique versus 20 MR-IOS-guided) with each operator inserting 10 needles into a geometric phantom. Technical efficacy was measured by comparing the success rates of needle insertions between the two operator groups. Operator performance was assessed by comparing total procedure times, total needle path distance, presumed tissue damage, and speed of individual insertions between the two operator groups.

RESULTS

The MR-IOS maximally altered SNR by 2% with no perceptible change in image quality or uniformity. Accuracy assessment showed mean entry error of 1.6 ± 0.6 mm, depth error of 0.7 ± 0.5 mm, angle error of 1.5 ± 1.1°, and target error of 1.9 ± 0.8 mm. Technical efficacy showed a statistically significant difference (p = 0.031) between success rates (freehand 35.0% vs. MR-IOS 80.95%). Operator performance showed: mean total procedure time of 40.3 ± 4.4 (s) for freehand and 37.0 ± 3.7 (s) for MR-IOS (p = 0.584), needle path distances of 152.6 ± 15.0 mm for freehand and 116.9 ± 8.7 mm for MR-IOS (p = 0.074), presumed tissue damage of 7,417.2 ± 955.6 mm(2) for freehand and 6062.2 ± 678.5 mm(2) for MR-IOS (p = 0.347), and speed of insertion 5.9 ± 0.4 mm/s for freehand and 4.3 ± 0.3 mm/s for MR-IOS (p = 0.003).

CONCLUSION

The MR-IOS is compatible within a clinical MR imaging environment, accurate for needle placement, technically efficacious, and improves operator performance over the unassisted insertion technique. The MR-IOS was found to be suitable for further testing in a clinical setting.

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

Navigation concepts for magnetic resonance imaging-guided musculoskeletal interventions

Image-guided musculoskeletal (MSK) interventions are a widely used alternative to open surgical procedures for various pathological findings in different body regions. They traditionally involve one of the established x-ray imaging techniques (radiography, fluoroscopy, computed tomography) or ultrasound scanning. Over the last decades, magnetic resonance imaging (MRI) has evolved into one of the most powerful diagnostic tools for nearly the whole body and has therefore been increasingly considered for interventional guidance as well.The strength of MRI for MSK applications is a combination of well-known general advantages, such as multiplanar and functional imaging capabilities, wide choice of tissue contrasts, and absence of ionizing radiation, as well as a number of MSK-specific factors, for example, the excellent depiction of soft-tissue tumors, nonosteolytic bone changes, and bone marrow lesions. On the downside, the magnetic resonance-compatible equipment needed, restricted space in the magnet, longer imaging times, and the more complex workflow have so far limited the number of MSK procedures under MRI guidance.Navigation solutions are generally a natural extension of any interventional imaging system, in particular, because powerful hardware and software for image processing have become routinely available. They help to identify proper access paths, provide accurate feedback on the instrument positions, facilitate the workflow in an MRI environment, and ultimately contribute to procedural safety and success.The purposes of this work were to describe some basic concepts and devices for MRI guidance of MSK procedures and to discuss technical and clinical achievements and challenges for some selected implementations.