Feldkamp and circle-and-line cone-beam reconstruction for 3D micro-CT of vascular networks
Detailed morphometric knowledge of the microvascular network is needed for studies relating structure to haemodynamic function in organs like the lung. Clinical volumetric CT is limited to millimetre-order spatial resolution. Since evidence suggests that small arterioles (50 to 300 micrometres) domi...
Gespeichert in:
Veröffentlicht in: | Physics in medicine & biology 1998-04, Vol.43 (4), p.929-940 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Detailed morphometric knowledge of the microvascular network is needed for studies relating structure to haemodynamic function in organs like the lung. Clinical volumetric CT is limited to millimetre-order spatial resolution. Since evidence suggests that small arterioles (50 to 300 micrometres) dominate pulmonary haemodynamics, we built a micro-CT scanner, capable of imaging excised lungs in 3D with 100 microm resolution, for basic physiology research. The scanner incorporates a micro-focal (3 microm) x-ray source, an xyz theta stage and a CCD-coupled image intensifier detector. We imaged phantoms and contrast-enhanced rat lungs, reconstructing the data with either the Feldkamp or the circle-and-line cone-beam reconstruction algorithm. We present reconstructions using 180 views over 360 degrees for the circular trajectory, augmented with views from a linear scan for the circle-and-line algorithm. Especially for platelike features perpendicular to the rotation axis and remote from the midplane, the circle-and-line algorithm produces superior reconstructions compared with Feldkamp's algorithm. We conclude that the use of nonplanar source trajectories to perform micro-CT on contrast-enhanced, excised lungs can provide data useful for morphometric analysis of vascular trees, currently down to the 130 microm level. |
---|---|
ISSN: | 0031-9155 1361-6560 |
DOI: | 10.1088/0031-9155/43/4/020 |