Applications of imaging technology in radiation research

Abstract

Imaging research and advances in systems engineering have enabled the transition of medical imaging from a means for accomplishing traditional anatomic visualization (i.e., orthopedic planar film X ray) to a means for noninvasively assessing a variety of functional measures. Perfusion imaging is one of the major highlights in functional imaging. In this work, various methods for measuring perfusion using widely-available commercial imaging modalities and contrast agents, specifically X ray and MR (magnetic resonance), will be described. The first section reviews general methods used for perfusion imaging, and the second section provides modality-specific information, focusing on the contrast mechanisms used to calculate perfusion-related parameters. The goal of these descriptions is to illustrate how perfusion imaging can be applied to radiation biology research.

FIG. 1

Representative signal intensity curve (SIC) showing mathematically straightforward perfusion measurements: (A) maximum enhancement, (B) arrival time, (C) mean transit time, (D) wash-in rate, (E) wash-out rate, (F) brevity of enhancement, (G) blood volume (area under SIC).

FIG. 2

Defects of cerebral perfusion after stroke as visualized using three popular imaging techniques: perfusion CT (PCT), MR diffusion-weighted imaging (DWI), and MR perfusion-weighted imaging (PWI). In the same areas, CT and MR angiography (bottom row) anatomical imaging also show cerebral blood vessel filling defects. PCT shows areas of infarct core (red) and penumbral area (green). DWI images show enhancement in the stroke area due to blood stasis. PWI colormap shows relative MTT where red represents a protracted cerebral transit time, indicating abnormal blood flow. Permissions granted for reproduction from M. Wintermark, et al. (4).

FIG. 3

Representative perfusion color maps calculated from 2D anterior-posterior DSA X ray (2D Perfusion, Philips Healthcare, Best, The Netherlands) before and after carotid artery stenting (treatment area shown by arrow). In this case, the perfusion metric used is time-of-contrast agent arrival. Note how areas of the brain become repurfused and show arrival time information (compare red circles) after treatment. In addition, previously perfused areas show a decrease in contrast arrival time after treatment (more red than blue in the color bar). Both of these features indicate perfusion restoration.

FIG. 4

Patient with innumerable neuroendocrine lesions. Note how the image enhancement as seen on DSA appears similar before and after TACE therapy. However, 2D perfusion analysis (2D Perfusion, Philips Healthcare, Best, The Netherlands) of time-to-peak enhancement show dramatic retardation in blood flow (more blue), especially over the lesions, due to the embolization. (Images courtesy of Jean-François Geschwind, and Nikhil Bhagat, at Johns Hopkins Hospital, Baltimore, MD.)

FIG. 5

Example images from a patient with glioblastoma multiforme: (Panel A) T2-weighted image, (panel B) T2-FLAIR (fluid attenuated inversion recovery) image, (panel C) T1-weighted image post contrast agent injection, (panel D) a single frame from a DCE-MRI acquisition, (panel E) the SIC from a lesion ROI (short arrow) and the vascular input ROI (long arrow), (panel F) a computed K^trans map, (panel G) a single frame from a DSC-MRI acquisition, (panel H) the SIC from a lesion ROI (short arrow) and the vascular input ROI (long arrow), (panel I) a computed rCBV map.

FIG. 6

ASL image creation: Control and labeled images are obtained and a difference image (control – label) is created. As the signal, difference is relatively small, multiple signal averages are obtained and result in a perfusion-weighted image. To convert the perfusion-weighted image to a quantitative rCBF map, additional data is required, including a calculated T1 map, as well as measured or assumed values for α (labeling efficiency); λ (blood-brain partition constant); and δ (labeled blood transit time).