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Adapting liver motion models using a navigator channel technique
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10.1118/1.3077923
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    Affiliations:
    1 Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 3E2, Canada
    2 Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 3E2, Canada
    3 Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 3E2, Canada and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 3E2, Canada
    4 Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 3E2, Canada, Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 3E2, Canada, and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 3E2, Canada
    5 Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 3E2, Canada, Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 3E2, Canada, and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 3E2, Canada
    a) Author to whom correspondence should be addressed. Electronic mail: thao-nguyen.nguyen@rmp.uhn.on.ca; Also at Ontario Cancer Institute, Suite 8-207, Princess Margaret Hospital, 610 University Ave., Toronto, Ontario M5G 2M9, Canada. Telephone: 416-946-4501 (x4214).
    Med. Phys. 36, 1061 (2009); http://dx.doi.org/10.1118/1.3077923
/content/aapm/journal/medphys/36/4/10.1118/1.3077923
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/36/4/10.1118/1.3077923

Figures

Image of FIG. 1.
FIG. 1.

FEM mesh models of the population and five example patient exhale livers. The COM was calculated and the COM node number was highlighted on each liver showing the consistent COM node representation.

Image of FIG. 2.
FIG. 2.

A patient example of the liver population model deforming into the patient specific exhale and inhale liver using MORFEUS deformable algorithm to generate the liver population motion model. [(a) and (b)] The liver population exhale model (black mesh) is deformed into the patient’s exhale model (black solid volume). [(c) and (d)] The resulting population-patient exhale liver model (light mesh) is deformed into the patient’s inhale liver (light solid volume). This process generated patient specific deformation maps from the exhale to inhale respiratory states using a common mesh representation for statistical and verification analysis.

Image of FIG. 3.
FIG. 3.

A flowchart outlining the rigid and deformable registration techniques used to deform the population liver model into the patient specific exhale and then subsequently inhale liver model to generate patient specific liver motion models for validation of the proposed method.

Image of FIG. 4.
FIG. 4.

Deformation color map of the liver population exhale to inhale liver motion model.

Image of FIG. 5.
FIG. 5.

A schematic diagram displaying the navigator channel technique process. The motion adaptation technique utilizes the population motion model information, motion information obtained from patient specific images, and the relative nodal position to adapt the population motion model into a navigator-adapted patient specific motion. Here, the adapted motion is applied to a patient exhale FEM model for illustrative purpose.

Image of FIG. 6.
FIG. 6.

Sagittal images of a patient example showing the SI-adapted motion shifting the inhale navigator channel on the image before calculating the AP patient specific motion. (a) and (c) show the navigator channel on the patient exhale sagittal image. (b) shows the initial position of the navigator channel on the inhale sagittal image. (c) shows the shifted position of the navigator channel on the inhale sagittal image. Note that the navigator channel on images (c) and (d) are on the same anatomic position of the liver, which will ensure accurate assessment of the motion at this position.

Image of FIG. 7.
FIG. 7.

An illustration depicting the various accuracy metrics used to validate the two methods used in the navigator channel technique. To validate (1) the motion detection method, (i) the navigator channel technique was compared to (ii) MORFEUS’ motion at spatially corresponding location. An illustration of an example of a navigator channel placed on the superior dome of the liver at exhale (solid line) and at inhale (dashed line) liver boundaries. Intensity plots of the two respiratory liver edges along the navigator channel show the calculated motion as the shift between the two gradients. The navigator channel motion is compared to the node motion calculated from the population-patient FEM model generated using MORFEUS. The right box (2) shows a representation of (i) navigator channel adaptation technique and the three comparison methods used for accuracy verification. (ii) shows an axial slice image of contours of the liver, which are used to generate a FEM mesh to calculate a deformation map across the liver volume. (iii) shows an illustration of the exhale (blue and solid) and inhale (red and dashed) livers with a representative spherical tumor from which the tumor COM is calculated and their displacement is determined. (iv) shows an illustration of an enhanced vessel bifurcation from the exhale (blue and solid line) and the inhale (red and dashed) from which the point of bifurcation from both is used to calculate the bifurcation motion.

Image of FIG. 8.
FIG. 8.

A bar graph illustrating the absolute average accuracy of the navigator channel adaptation technique using the two tumor COM methods: MORFEUS (dark gray) and image derived (light gray).

Image of FIG. 9.
FIG. 9.

A bar graph illustrating the absolute average accuracies across the contrast enhanced subset of patients , which were verified using all three verification methods. A star is drawn showing the average of all three verification methods in each of the cardinal directions.

Tables

Generic image for table
TABLE I.

Characteristics of the patients and treatment protocols.

Generic image for table
TABLE II.

The average absolute accuracy of patient motion detection using the navigator channel at the four liver boundaries compared to the corresponding locations in the full deformable registration algorithm. (MORFEUS motion—navigator channel motion).

Generic image for table
TABLE III.

The average absolute accuracy of patient motion prediction of the adapted motion model compared to the three different verification metrics: (1) MORFEUS deformable algorithm, (2) tumor COM, and (3) vessel bifurcations. (Comparison method motion—adapted motion). There are 13 patient data sets in total, which were all tested using the MORFEUS deformable registration algorithm. A subset of the patients had contrast administration, where bifurcations and tumors were identifiable. An additional subset of patients had visible tumor shadows in their images, which allowed for tumor COM comparison as well as the contrast patients. The remaining patients where the inhale tumor was not visible in the CT images derived the tumor COM motion using the MORFEUS deformable algorithm.

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/content/aapm/journal/medphys/36/4/10.1118/1.3077923
2009-03-05
2014-04-19
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Adapting liver motion models using a navigator channel technique
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/36/4/10.1118/1.3077923
10.1118/1.3077923
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