Assessment of 18F PET signals for automatic target volume definition in radiotherapy treatment planning.
BACKGROUND
Positron emission tomography (PET) alone or in combination with computer tomography (PET/CT) is increasingly used in target volume assessment. A standardized way of converting PET signals into target volumes is not available at present.


METHODS
Assuming a uniform signal emission from a tumour and surrounding normal tissues, a model-based method was developed to determine a relative threshold level (Th(rel)) for gross tumour volume delineation. Two phantoms consisting of cylindrical and spherical sources of diameter ranging from 4.5 to 43 mm in a tank and (18)F activities ranging from 0.001 to 0.15 MBq/ml for tank and sources, respectively, were used for PET/CT imaging. A Th(rel) was calculated that best corresponded to the physical diameter of the cylindrical sources. Software (SW) was generated to automatically delineate volumes based on this threshold. The SW was validated for in vitro and in vivo PET signals.


RESULTS
The Th(rel) best representing the source diameter was 41+/-2.5% (95% confidence level) of the background-subtracted signal. The mean deviation for sources of diameter > or =12.5 mm was < or =1.5 mm. The Th(rel) was constant for diameters > or =12.5 mm. For source diameters <12.5 mm, the 41% level over-estimated the real source diameter by a factor depending on the diameter. In an in vitro set-up the SW was capable of segmenting solitary PET volumes to within 1.4 mm (1SD). For non-homogeneous signals in a clinical set-up minimal manual intervention is presently required to separate target from non-target signals. The SW may slightly underestimate target volumes when compared with CT-based volumes, but works well as a first approximation. The volume can be manually adapted to give the ultimate target volume.


CONCLUSIONS
SW-based automatic delineation of the volume of (18)F activity is feasible and highly reproducible. Volumes can be subsequently modified by the clinician if necessary. This approach will increase the efficiency of the planning process.
Davis
J Bernard
JB
Radiation Oncology, University Hospital, Zurich, Switzerland.
Reiner
Beatrice
B
Huser
Marius
M
Burger
Cyrill
C
Székely
Gábor
G
Ciernik
I Frank
IF
D001331:Automation; D019788:Fluorodeoxyglucose F18 / Q000494:pharmacology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D009369:Neoplasms / Q000000981:diagnostic imaging*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D018787:Radiation Oncology / Q000379:methods*; D019275:Radiopharmaceuticals / Q000494:pharmacology*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18