1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Reconstruction of brachytherapy seed positions and orientations from cone-beam CT x-ray projections via a novel iterative forward projection matching method
Rent:
Rent this article for
USD
10.1118/1.3528220
/content/aapm/journal/medphys/38/1/10.1118/1.3528220
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/1/10.1118/1.3528220

Figures

Image of FIG. 1.
FIG. 1.

Elongated line seed of length is characterized by the seed center (black dot) positions and orientation coordinates angle pair in the world coordinates frame, where is the axis of implantation.

Image of FIG. 2.
FIG. 2.

Close-up photographs of (a) an acrylic slab of the phantom containing Model 6711 seeds, where the polar angle was defined as the angle between the implant axis and the major axis of the seed. It was assigned across the slab at different orientation for each seed (see inset). The azimuthal angle was assigned by using the adjustable reference grid drawn for each seed in known orientation. (b) Multiconfiguration precision-machined phantom assembly with all eight replaceable slabs. This phantom was used to create different seed configurations to test the gIFPM algorithm seed localization accuracy in the clinical setting.

Image of FIG. 3.
FIG. 3.

An example case of the image postprocessing of the projection images obtained from the Varian 4030CB digital simulator, (a) raw projection image, (b) filtered image, (c) binary seed-only bitmap image, and (d) blurred grayscale image using the gIFPM algorithm for 76 seed phantom data sets.

Image of FIG. 4.
FIG. 4.

An illustration of the convergence process for a 60 seed simulated implant. (a) Initial estimated seed configuration with straight seeds derived from a patient preplan, (b) computed images after convergence with , (c) computed images after convergence with and using poses (b) as the initial configuration, and (d) the true/synthetic measured images, where the rows represent different gantry angles. The gIFPM algorithm was able to reproduce orientation of each individual seed including overlapping clustered and highly migrated seeds.

Image of FIG. 5.
FIG. 5.

The similarity metric score vs iteration number for the two-step gIFPM algorithm for the four simulated patient cases: 56, 60, 66, and 70 seed configurations. The transition from larger to smaller blurring for the 66 seed configuration is shown by the black arrow. The one-dimensional image-intensity profiles in the inset illustrate the difference in capture range for the two blurring levels.

Image of FIG. 6.
FIG. 6.

Histograms of the seed localization error for the 60 seed simulated patient configuration. (a) Positional error in terms of 3D distance between reconstructed and true location and (b) orientation error. The gIFPM absolute accuracy was for position and and for and angles, respectively.

Image of FIG. 7.
FIG. 7.

Illustration of gIFPM seed reconstruction for simulated case III in Table I for a single projection. In the first row , 66 seeds are present in the simulated implant derived from the preplan but 68 are assumed in the initial seed configure (a) with seed axes parallel to the gantry axis. In the second row , 66 seeds are present both in the initial estimated configuration and in the simulated implant, along with an additional seedlike artifact which is present in the measured images. (a) Initial estimate of the seed configuration, (b) computed images at final convergence, (c) the synthetic measured images corresponding to the “true” seed configuration, and (d) difference between images (b) and (c). The ellipse and arrow in part (d) indicates the extra seed(s) found by gIFPM at convergence.

Image of FIG. 8.
FIG. 8.

The similarity metric score vs iteration number for the two-step gIFPM algorithm for the three example physical phantom seed configurations. The transition from larger to smaller blurring filter for the 50 seed configuration is highlighted by the black arrow.

Image of FIG. 9.
FIG. 9.

Histograms of the seed localization error in 3D space between reconstructed and true pose for the 76 seed phantom configuration for three projection images. (a) Positional error and (b) orientation error. The RMS error was found to be for position. The and angle distributions were found to be and , respectively.

Image of FIG. 10.
FIG. 10.

Superposition of measured (white) and computed (black) line-seed images projected on the detector planes for gantry angles of (a) , (b) −20°, and (c) for 76 seed phantom configuration. While many computed seeds coincided exactly with the measured ones, a few still reveal small discrepancies.

Image of FIG. 11.
FIG. 11.

Seed-by-seed vector difference between gIFPM positions and those obtained from the VARISEED planning system for 76 seed phantom data sets. The 3D RMS error was .

Tables

Generic image for table
TABLE I.

Accuracy of gIFPM reconstructed poses for four simulated implants derived from patient preplans. The RMS value and standard deviation for the positional and orientation coordinates are reported. The maximum displacement (Max. error) of the seed position is also reported.

Generic image for table
TABLE II.

Accuracy of seed poses deduced by the gIFPM algorithm for three seed configurations realized by our physical phantom and imaged on the VCU ACUITY system. The RMS value and standard deviation for the positional and orientation coordinates are reported while using three vs six experimentally acquired projections. The maximum displacement (Max. error) of the seed position is also reported.

Loading

Article metrics loading...

/content/aapm/journal/medphys/38/1/10.1118/1.3528220
2010-12-29
2014-04-20
Loading

Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Reconstruction of brachytherapy seed positions and orientations from cone-beam CT x-ray projections via a novel iterative forward projection matching method
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/1/10.1118/1.3528220
10.1118/1.3528220
SEARCH_EXPAND_ITEM