Perspective volume rendering of CT and MR images: applications for endoscopic imaging.

Rubin, Geoffrey D.; Beaulieu, Christopher F.; Argiro, Vincent; Ringl, Helmut; Norbash, Alexander; Feller, John F.; Dake, Michael D.; Jeffrey, R. Brooke; Napel, Sandy

doi:10.1148/radiology.199.2.8668772

Cited by 377 publications

(135 citation statements)

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“…The largest operator classified as small, s, was determined from the smallest airway 3D region growing can segment continually. A value of 3 was used for s since airways with cross sections larger than B 3 4 are hardly influenced by partial volume effects. The largest operator, M, is based on the size of the largest airway on the image.…”

Section: Resultsmentioning

confidence: 99%

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Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

et al. 2002

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Rationale and Objectives. The segmentation of airways from CT images is a critical first step for numerous virtual bronchoscopic (VB) applications. Automatic or semiautomatic methods are necessary, since manual segmentation is prohibitively time consuming. The methods must be robust and operate within a reasonable time frame to be useful for clinical VB use. The authors developed an integrated airway segmentation system and demonstrated its effectiveness on a series of human images. Materials and Methods.The authors' airway segmentation system draws on two segmentation algorithms: (a) an adaptive region-growing algorithm and (b) a new hybrid algorithm that uses both region growing and mathematical morphology. Images from an ongoing VB study were segmented by means of both the adaptive region-growing and the new hybrid methods. The segmentation volume, branch number estimate, and segmentation quality were determined for each case.Results. The results demonstrate the need for an integrated segmentation system, since no single method is superior for all clinically relevant cases. The region-growing algorithm is the fastest and provides acceptable segmentations for most VB applications, but the hybrid method provides superior airway edge localization, making it better suited for quantitative applications. In addition, the authors show that prefiltering the image data before airway segmentation increases the robustness of both regiongrowing and hybrid methods. Conclusion.The combination of these two algorithms with the prefiltering options allowed the successful segmentation of all test images. The times required for all segmentations were acceptable, and the results were suitable for the authors' VB application needs.Key Words. Bronchi, CT; bronchoscopy; computed tomography (CT), image processing; computed tomography (CT), threedimensional; trachea, CT. © AUR, 2002New multidetector spiral CT scanners can produce threedimensional (3D) volumetric images of the human airway tree consisting of hundreds of two-dimensional (2D) sections (1,2). A typical 3D image can include 400 or more 512 ϫ 512 0.6-mm sections. Such images provide an excellent basis for virtual bronchoscopy (VB) applications (3-15) and quantitative airway analysis (8,16 -20). New VB methods also allow for live guided nodule and lymph node biopsies (13,15). A critical first step in these VB applications is the segmentation of the airway tree. Manual interactive segmentation has been applied in some cases, but routine manual analysis is impractical for the large 3D images arising from the new scanners (21,22). A variety of semiautomatic airway segmentation techniques have been proposed, but none have been conclusively proved adequate for very large, high-resolution, 3D CT chest images (4,20 -30).

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Section: Resultsmentioning

confidence: 99%

“…A typical 3D image can include 400 or more 512 ϫ 512 0.6-mm sections. Such images provide an excellent basis for virtual bronchoscopy (VB) applications (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) and quantitative airway analysis (8,16 -20). New VB methods also allow for live guided nodule and lymph node biopsies (13,15).…”

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confidence: 99%

Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

et al. 2002

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“…The development of virtual bronchoscopy (VB) has led to interest in introducing CT-based computer-graphics techniques into the procedure of lung-cancer staging [9][10][11][12][13][14]. In VB, the 3D CT image serves as a high-resolution digital-image replica of the chest.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of techniques are surface and volume rendering, multi-planar reformatted (MPR) slice viewing, projection imaging, thinslab viewing, and endoluminal (virtual endoscopic) rendering [10,12,14,[23][24][25][26]. But, in reality, the visualization actually used in clinical practice is limited to previewing transverse-plane 2D slice images, occasional coronal and sagittal MPR sections, gray-scale windowing and thresholding to highlight structures of interest, and simple annotation and report generation.…”

Section: Introductionmentioning

confidence: 99%

3D CT-Video Fusion for Image-Guided Bronchoscopy

Higgins¹,

Helferty²,

Lü³

et al. 2008

Computerized Medical Imaging and Graphics

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Bronchoscopic biopsy of the central-chest lymph nodes is an important step for lung-cancer staging. Before bronchoscopy, the physician first visually assesses a patient's three-dimensional (3D) computed tomography (CT) chest scan to identify suspect lymph-node sites. Next, during bronchoscopy, the physician guides the bronchoscope to each desired lymph-node site. Unfortunately, the physician has no link between the 3D CT image data and the live video stream provided during bronchoscopy. Thus, the physician must essentially perform biopsy blindly, and the skill levels between different physicians differ greatly. We describe an approach that enables synergistic fusion between the 3D CT data and the bronchoscopic video. Both the integrated planning and guidance system and the internal CT-video registration and fusion methods are described. Phantom, animal, and human studies illustrate the efficacy of the methods.

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“…True axial images from MR imaging [9,39] or nonhelical axial CT data sets can also be reconstructed into 3-D models, but this requires longer acquisition times that can result in further loss of anatomical detail.…”

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Virtual endoscopic environments in modern neurosurgical practice

Levy¹,

Chen²,

Amar³

et al. 1999

FOC

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Modern radiographic techniques have allowed the creation of high-definition planar images that can provide important anatomical as well as physiological data. Planar imaging sets can be reformatted into three-dimensional (3-D) data sets that can then be manipulated to demonstrate important anatomical or gross pathological features. Three-dimensional data sets have been used with success in modern image-guided or frameless stereotactic surgery. Another potential application is so-called "virtual endoscopy" or "scopeless endoscopy," in which a 3-D anatomical data set is reformatted into a volume-rendered image that can then be viewed. By reformatting images in this way, a "surgeon's-eye" view can be obtained, which can aid in presurgical planning and diagnosis. The use of virtual endoscopy has the potential to increase our understanding of the appropriate anatomy and the anatomical relationships most apparent during neurosurgical approaches. In so doing, virtual endoscopy may serve as an important means of planning for therapeutic interventions. On the other hand, one must always be cognizant of the technical limitations of these studies regardless of the quality of the reconstructed images. Prospective, correlative, clinical studies in which the anatomical advantages of virtual-based endoscopy are evaluated in large cadaver or patient series must be performed. Until then, the only potential ways to compensate for errors that exist in the algorithms and reconstructions of 3-D endoscopic images are based on the surgeon's understanding of the clinical state of the patient and prior experience with the anatomy in the region of question.

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Perspective volume rendering of CT and MR images: applications for endoscopic imaging.

Abstract: PVR is a novel way to present volumetric data and may enable noninvasive diagnostic endoscopy and provide an alternate method to analyze volumetric imaging data for primary diagnosis.

Cited by 377 publications

References 0 publications

Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

3D CT-Video Fusion for Image-Guided Bronchoscopy

Virtual endoscopic environments in modern neurosurgical practice

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