Developed in the 1970s, computerized tomography (CT) or X-ray computed tomography is a widely used imaging modality. It allows rapid high-resolution tissue imaging providing excellent delineation of anatomy. CT is a mainstay in the diagnosis and follow-up of clinical conditions in almost every medical and surgical specialty. CT has the capacity to guide the performance of percutaneous procedures, providing accurate location of instruments within three-dimensional regions of the body [16]. The main advantage of CT is its fast image acquisition and high resolution; significant disadvantages include exposure of the patient to ionizing radiation (especially cumulative exposure secondary to serial imaging) [17]. Ionizing radiation may expose patients to long-term cancer risk, particularly when exposed at early ages [18].

Fluoroscopy utilizes low-intensity X-rays to obtain “real-time” radiographic imaging. It is utilized as a diagnostic modality and during image-guided therapy [19]. The administration of intravascular contrast can allow for vessel mapping, facilitating therapeutic interventions such as drug delivery or angioembolization.

Typical radiographic imaging, X-ray, US, or fluoroscopy, is seen in the original plane as a two-dimensional (2D) image. More advanced imaging, CT, PET, and MRI, is computer processed allowing rendering in multiple planes. The multi-planar reconstruction (MPR) technique allows new images to be reformatted from their original plane (mostly axial) in coronal, sagittal, or oblique planes utilizing the pixel data acquired from the stack of planar images. MPR aids in visualization of anatomy by providing the perspective of structures within the entire volume that may not be readily apparent from base images. However, quality of MPR is dependent on base image acquisition and quality. The various approaches for 3D visualization and analysis of imaging datasets are subsequently described.

Surface rendering, or shaded surface display (SSD), operates on the assumption that a structure or organ can be displayed based on the surface estimated from an image (volume dataset) [20]. The surface is initially determined by segmentation which is accomplished by thresholding, or assigning each voxel intensity within the dataset to be within a predetermined attenuation range. Surface contours derived from the volume dataset are modeled as overlapping polygons, to represent the 3D object. SSD allows for swift image rendering; however, only a small portion of the dataset is employed, and it may be of limited utility where a discrete interface or boundary is not readily apparent [21].

Maximum intensity projection (MIP) rendering appraises each voxel along a continuous line throughout a dataset [22]. The maximum value voxel, or volumetric picture element, which essentially corresponds to a value on a grid in 3D space, is selected and used as the displayed value. Based on these displayed voxels, a 3D image is rendered. MIP is most commonly used in display and analysis of angiographic images and draws from only a small percentage of the entire volume dataset for rendering. Its shortcomings include lack of depth cues to assess spatial relationships and increased background “noise” due to display of only voxels with the highest intensity values.

Volume rendering is a technique that utilizes the entire volume dataset and assigns an opacity and color to each voxel which corresponds to tissue characteristics. It can display surfaces with shading and other parts of the volume simultaneously [23]. The highest image quality volume rendering method from 3D scalar data is by volume ray casting. In volume ray casting, a “ray” is projected through the volume, and the amount of intersection between the ray and a voxel determines the contribution to the voxel value. Weighted measures of the voxel values are calculated for each pixel and scales to a range of gray-scale values from which an image is constructed. As volume rendering incorporates the whole dataset, it requires complex algorithms and powerful computer processing equipment but has the capacity to display multiple tissues in addition to giving information on spatial relationship within the volume.

The goal of medical image registration is to find the ideal transformation that optimally aligns the anatomic structures of interest from the acquired image datasets. Correspondence is established between features in image datasets using a transformational model such that mapping of coordinates between these images corresponds to the same anatomical point. Registration allows clinicians to integrate information from different imaging modalities or time points. The term “fusion” refers to the step subsequent to registration that creates an integrated display of the data involved. However, it is often used to refer to the entire process of co-registration and co-display of the registered datasets. Applications of image registration are diverse and include surgical guidance, radiation planning, computer-aided diagnosis, and fusion of anatomic and functional imaging [24–27].

Image registration was first accomplished by physicians using “cognitive registration,” referring to the basic interpretations of imaging data and applying it to physical space based on cognitive estimations. Rapid technologic progress and research in the field has harnessed increased computational power to employ complex algorithms to deliver more accurate registration systems. Image registration is accomplished by incorporating information from one imaging modality and then applying it to a complementary modality and/or physical space to relate contrasting information from the different types of images. For example, when a suspicious lesion is only visualized on multiparametric MR imaging, MR/transrectal ultrasound fusion-guided biopsy can be performed whereby co-registration allows the real-time imaging advantage of US and resolution of MR to complement each other [13].

Current image registration techniques employ a combination of software-based algorithms and manual corrections to determine the spatial correspondence within 3D space. There are a number of algorithms that may be applied for registration, the common element being the ability to maximize a measure of similarity between a transformed “floating image” and a fixed “reference image” [28]. Intrinsic registration modalities rely solely on patient-derived imaging data and include intensity-based and geometric transformation-based models.

Geometric transformations require segmentation and include both landmark-based registration and surface-based registration techniques. Geometric transformation takes two forms, which are rigid and nonrigid or deformable algorithms. Rigid body registration is based on the notion that one image requires only translation and/or a rotation to achieve reasonable correspondence with a similar image, without change in the size or shape. Nonrigid or elastic registration is the more complex of the two types of transformations, where one image is elastically deformed to fit the second imaging and is used to describe changes in the shape, organ shift, or patient motion. Elastic registration employs a landmark-based approach in which corresponding landmarks (points, lines, surfaces, volumes) are forced (warped) to exactly match the other, potentially resulting in registration errors which may be compounded with each additional elastic transformation.

Tracking refers to the transformation of image, patient, and instrument coordinates into a common reference system to identify its precise location within a 3D space. The clinical application of tracking or surgical navigation transcends medical specialties and has been exploited for percutaneous needle-based procedures (biopsy, catheter drainage, and ablation), endovascular interventions, minimally invasive surgical procedures, and even endoscopic procedures such as guidance for bronchoscopic biopsy of peripherally located lesions [41–43].

Optical tracking systems, the first to be widely adopted for clinical applications, operate via laser or infrared light emitting diodes and are tracked by reflection of light that is captured by an infrared camera. Alternatively, diodes placed on surgical instruments themselves may transmit signals back to a charge-coupled device camera. These systems represent the most accurate way of tracking instruments with spatial inaccuracy reported to be less than 1–3 mm [44, 45]. A major limiting factor in the use of optical tracking systems is the requirement of maintaining a “line of sight” (or unimpeded pathway) between the optical markers and the tracker camera [46]. As such, if an instrument tip is outside of the direct field, it is not visualized, but optically tracking the back of a rigid instrument allows for compensation by extrapolating the tips position. However, the line of sight requirement precludes the tracking of internal portions of an instrument, or if they are flexible or deformable [47].

Mechanical localizers are the earliest iterations that were employed for endonasal and neurosurgical therapies [48–51]. They consist of hardware with several degrees of freedom with encoders located at each joint, the entire device which is solely controlled by the operator. The exact location and orientation is calculated based on the feedback from geometric models derived from each encoder. These systems, though exhibiting a high degree of accuracy, can only track one object and may be large and cumbersome for use in a small operative field. A benefit of this system is that an instrument can be guided by the operator to a desired location within a physical space, fixed in a definite position and its location recorded [52].

The introduction of electromagnetic (EM) tracking systems was a proposed answer to the limitations of prior methods [53]. Electromagnetic tracking systems are based on the ability to localize a sensor which emits a weak electrical current in a weak pulsed electromagnetic field. An EM field generator is placed over the area of interest, creating a weak and differential electromagnetic field with a defined volume. The electric current emitted by sensor coils from skin fiducials, attached to or inside of instruments, is detected; and the strength of the signal based on its location within the EM field is triangulated to define its point in space [54]. Coils may be either 5 or 6 DOF (degrees of freedom). A single 5 DOF coil, though smaller, cannot determine the rotation component, which can be overcome by adding two or more 5 DOF coils, and most systems can track multiple coils within the geometry of the EM field. The reported accuracy is less than that of optical tracking systems (typically ~ 3 mm); however, “line of sight” tracking is not required allowing for tracking objects within the body within the EM field. Additionally, rapid technologic improvements now reportedly provide submillimeter tracking. A number of factors contribute to the utility of an individual EM tracking system. The refresh rate, working volume created by the field generator, and resilience must be considered [55]. A rapid refresh rate is needed when quantitative feedback is need such as during needle tracking, and images should be updated so as to not impede the physician performing the procedure. The size of the EM field created by the generator results in a fixed, arbitrary working volume in which the sensors must operate. As a result, it is imperative that the anatomy of interest and working area of a procedure be within the field. Signal intensity is dependent on proximity from the EM field generator; hence, the closer the coil is, the stronger the signal that is detected. Ferromagnetic materials from instruments and imaging equipment can cause interference resulting in distortions that may influence the accuracy of the system [56, 57]. A resilient system that employs direct current-driven tracking can minimize eddy currents created by metallic objects close to the operative field, and metal immune systems are now available.

Augmented reality (AR) for surgical navigation uses a 3D image acquired preoperatively, rendering to a 2D live display captured intraoperatively. AR allows a surgeon to appreciate the objects and anatomical structures in the surgical field that are beyond the normal field of view. It can, for example, provide guidance for surgical resection of tumors deep to the visualized surface, or to appreciate vascular anatomy prior to being directly encountered [58].

In urologic surgery, it has successfully been used to assimilate information from imaging and visual information from the operative field during laparoscopic partial nephrectomy, adrenalectomy, and radical prostatectomy [59, 60

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