Incorporating quantitative single photon emission computed tomography into radiation therapy treatment planning for lung cancer: impact of attenuation and scatter correction on the single photon emission computed tomography-weighted mean dose and functional lung segmentation.
OBJECTIVE
To assess the impact of attenuation and scatter corrections on the calculation of single photon emission computed tomography (SPECT)-weighted mean dose (SWMD) and functional volume segmentation as applied to radiation therapy treatment planning for lung cancer.


METHODS
Nine patients with lung cancer underwent a SPECT lung perfusion scan. For each scan, four image sets were reconstructed using the ordered subsets expectation maximization method with attenuation and scatter corrections ranging from none to a most comprehensive combination of attenuation corrections and direct scatter modeling. Functional volumes were segmented in each reconstructed image using 10%, 20%, …, 90% of maximum SPECT intensity as a threshold. Systematic effects of SPECT reconstruction methods on treatment planning using functional volume were studied by calculating size and spatial agreements of functional volumes, and V(20) for functional volume from actual treatment plans. The SWMD was calculated for radiation beams with a variety of possible gantry angles and field sizes.


RESULTS
Functional volume segmentation is sensitive to the particular method of SPECT reconstruction used. Large variations in functional volumes, as high as >50%, were observed in SPECT images reconstructed with different attenuation/scatter corrections. However, SWMD was less sensitive to the type of scatter corrections. SWMD was consistent within 2% for all reconstructions as long as computed tomography-based attenuation correction was used.


CONCLUSIONS
When using perfusion SPECT images during treatment planning optimization/evaluation, the SWMD may be the preferred figure of merit, as it is less affected by reconstruction technique, compared with threshold-based functional volume segmentation.
Yin
Lingshu
L
Vancouver Cancer Centre, Vancouver, BC, Canada. lyin@bccancer.bc.ca
Shcherbinin
Sergey
S
Celler
Anna
A
Thompson
Anna
A
Fua
Tsien-Fei
TF
Liu
Mitchell
M
Duzenli
Cheryl
C
Gill
Brad
B
Sheehan
Finbar
F
Powe
John
J
Worsley
Daniel
D
Marks
Lawrence
L
Moiseenko
Vitali
V
D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging / Q000473:pathology / Q000532:radiotherapy; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D019047:Phantoms, Imaging; D011859:Radiography; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D055752:Small Cell Lung Carcinoma / Q000000981:diagnostic imaging / Q000532:radiotherapy; D012984:Software; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D047368:Tumor Burden