Di Maso Lianna D, Huang Jessie, Bassetti Michael F, DeWerd Larry A, Miller Jessica R
Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53716, USA.
Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, 53716, USA.
J Appl Clin Med Phys. 2018 Sep;19(5):676-683. doi: 10.1002/acm2.12435. Epub 2018 Aug 17.
Tumor delineation using conventional CT images can be a challenge for pancreatic adenocarcinoma where contrast between the tumor and surrounding healthy tissue is low. This work investigates the ability of a split-filter dual-energy CT (DECT) system to improve pancreatic tumor contrast and contrast-to-noise ratio (CNR) for radiation therapy treatment planning.
Multiphasic scans of 20 pancreatic tumors were acquired using a split-filter DECT technique with iodinated contrast medium, OMNIPAQUE . Analysis was performed on the pancreatic and portal venous phases for several types of DECT images. Pancreatic gross target volume (GTV) contrast and CNR were calculated and analyzed from mixed 120 kVp-equivalent images and virtual monoenergetic images (VMI) at 57 and 40 keV. The role of iterative reconstruction on DECT images was also investigated. Paired t-tests were used to assess the difference in GTV contrast and CNR among the different images.
The VMIs at 40 keV had a 110% greater image noise compared to the mixed 120 kVp-equivalent images (P < 0.0001). VMIs at 40 keV increased GTV contrast from 15.9 ± 19.9 HU to 93.7 ± 49.6 HU and CNR from 1.37 ± 2.05 to 3.86 ± 2.78 in comparison to the mixed 120 kVp-equivalent images. The iterative reconstruction algorithm investigated decreased noise in the VMIs by about 20% and improved CNR by about 30%.
Pancreatic tumor contrast and CNR were significantly improved using VMIs reconstructed from the split-filter DECT technique, and the use of iterative reconstruction further improved CNR. This gain in tumor contrast may lead to more accurate tumor delineation for radiation therapy treatment planning.
对于胰腺腺癌，利用传统CT图像进行肿瘤勾画可能具有挑战性，因为肿瘤与周围健康组织之间的对比度较低。本研究探讨了分体式滤过器双能CT（DECT）系统在改善胰腺肿瘤对比度和对比噪声比（CNR）以用于放射治疗计划方面的能力。
使用分体式滤过器DECT技术并采用碘化造影剂欧乃派克对20例胰腺肿瘤进行多期扫描。对几种类型的DECT图像在胰腺期和门静脉期进行分析。从混合的等效120 kVp图像以及57 keV和40 keV的虚拟单能图像（VMI）中计算并分析胰腺大体肿瘤体积（GTV）对比度和CNR。还研究了迭代重建在DECT图像上的作用。采用配对t检验评估不同图像之间GTV对比度和CNR的差异。
与混合的等效120 kVp图像相比，40 keV的VMI图像噪声高110%（P < 0.0001）。与混合的等效120 kVp图像相比，40 keV的VMI使GTV对比度从15.9±19.9 HU提高到93.7±49.6 HU，CNR从1.37±2.05提高到3.86±2.78。所研究的迭代重建算法使VMI中的噪声降低了约20%，并使CNR提高了约30%。
利用分体式滤过器DECT技术重建的VMI显著提高了胰腺肿瘤对比度和CNR，而迭代重建的应用进一步提高了CNR。肿瘤对比度的这种提高可能会使放射治疗计划中的肿瘤勾画更加准确。
