Correction for ‘artificial’ electron disequilibrium due to cone-beam CT density errors: implications for on-line adaptive stereotactic body radiation therapy of lung

Abstract

Cone-beam computed tomography (CBCT) has rapidly become a clinically useful imaging modality for image-guided radiation therapy. Unfortunately, CBCT images of the thorax are susceptible to artefacts due to scattered photons, beam hardening, lag in data acquisition, and respiratory motion during a slow scan. These limitations cause dose errors when CBCT image data are used directly in dose computations for on-line, dose adaptive radiation therapy (DART). The purpose of this work is to assess the magnitude of errors in CBCT numbers (HU), and determine the resultant effects on derived tissue density and computed dose accuracy for stereotactic body radiation therapy (SBRT) of lung cancer. Planning CT (PCT) images of three lung patients were acquired using a Philips multi-slice helical CT simulator, while CBCT images were obtained with a Varian On-Board Imaging system. To account for erroneous CBCT data, three practical correction techniques were tested: (1) conversion of CBCT numbers to electron density using phantoms, (2) replacement of individual CBCT pixel values with bulk CT numbers, averaged from PCT images for tissue regions, and (3) limited replacement of CBCT lung pixels values (LCT) likely to produce artificial lateral electron disequilibrium. For each corrected CBCT data set, lung SBRT dose distributions were computed for a 6 MV volume modulated arc therapy (VMAT) technique within the Philips Pinnacle treatment planning system. The reference prescription dose was set such that 95% of the planning target volume (PTV) received at least 54 Gy (i.e. D95). Further, we used the relative depth dose factor as an a priori index to predict the effects of incorrect low tissue density on computed lung dose in regions of severe electron disequilibrium. CT number profiles from co-registered CBCT and PCT patient lung images revealed many reduced lung pixel values in CBCT data, with some pixels corresponding to vacuum (-1000 HU). Similarly, CBCT data in a plastic lung phantom were reduced by 200 HU compared with known CT number values. For the three patients, dose results using the CBCT number data registered with PCT showed a prescription dose reduction ranging from 4 to 13% (D95 = 47 Gy). Therefore, accurate determination of lung density, especially for very low lung density (<0.2 g cm(-3)) is essential, but difficult to achieve using the CBCT data. Applying corrective techniques (1) and (2) to CBCT patient data produced unacceptable dose differences. For one typical VMAT SBRT patient, the D95 for the corrected CBCT and BCT image-based plans differed by -4% (D95 = 52 Gy) and 9% (D95 = 59 Gy) compared to the co-registered PCT image-based plan. However, corrective technique (3) produced negligible dose differences comparing LCT and PCT image-based plans. With regard to implementing on-line DART, dose errors must be minimized because they affect re-optimization decisions, and prevent accurate accumulation of the dose distribution.

Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

“…For conventional linear accelerator (linac)-based radiation therapy a major challenge on the way towards CBCT-based ART is insufficient cone-beam computed tomography (CBCT) image quality [7][8][9]. A CBCT possesses image artifacts due to detector scatter, patient specific scatter, image lag and beam hardening, making dose calculations prone to errors.…”

“…A CBCT possesses image artifacts due to detector scatter, patient specific scatter, image lag and beam hardening, making dose calculations prone to errors. Many calibration approaches for accurate dose calculations on CBCT currently exist [10]: Patient-or population-specific CT number to electron density calibration (CT-ED-calibration) [11], bulk density override [12], image processing algorithms that further improve the image quality [13][14][15] or deformable image registration (DIR) [7,8] between the CBCT and the treatment planning CT (pCT) to create a deformed CT or a deformed dose in order to assess anatomical and dosimetric changes [16,17]. Recently, deep learning (DL) approaches gained popularity since they can be custom tailored to individual treatment components like image processing and can substantially accelerate time-consuming workflow steps in ART [18,19].…”

On-line adaptive MR guided radiotherapy for locally advanced pancreatic cancer: Clinical and dosimetric considerations

“…The overall management of SABR is limited by the difficulty to accurately identify the therapy volumes. This is related to limited soft-tissue contrast provided by computed tomography (CT), cone beam computed tomography (CBCT) or mega voltage computed tomography (MVCT) images, generally used in abdominal radiotherapy (RT) [18,19], especially for pre-treatment positioning. Motion management solutions in conventional radiotherapy often require larger planning target volume (PTV) margins or the use of the internal target volume (ITV), limiting the possibility to perform any kind of dose escalation protocols, since toxicity to surrounding organs remains a limiting factor.…”

Value of Three-Dimensional Imaging Systems for Image-Guided Carbon Ion Radiotherapy

“…Currently, ART is typically based on CBCT or CT-on-rails; the former is dominant because of its wide installation. However, because of the abovementioned limitations, CBCT is not considered a good imaging modality for online ART in some sites such as the head and neck, abdomen, pelvis [55,56], and even in the lungs [57]. However, a recent report argued that CBCT can help provide similar clinical indicators to those using rescan CT for lung cancer patients [58].…”