Deformable planning CT to cone‐beam CT image registration in head‐and‐neck cancer

The aim of this work is to demonstrate the feasibility of using water‐equivalent thickness (WET) and virtual proton depth radiographs (PDRs) of intensity corrected cone‐beam computed tomography (CBCT) to detect anatomical change and patient setup error to trigger adaptive head and neck proton therapy. The planning CT (pCT) and linear accelerator (linac) equipped CBCTs acquired weekly during treatment of a head and neck patient were used in this study. Deformable image registration (DIR) was used to register each CBCT with the pCT and map Hounsfield units (HUs) from the planning CT (pCT) onto the daily CBCT. The deformed pCT is referred as the corrected CBCT (cCBCT). Two dimensional virtual lateral PDRs were generated using a ray‐tracing technique to project the cumulative WET from a virtual source through the cCBCT and the pCT onto a virtual plane. The PDRs were used to identify anatomic regions with large variations in the proton range between the cCBCT and pCT using a threshold of 3 mm relative difference of WET and 3 mm search radius criteria. The relationship between PDR differences and dose distribution is established. Due to weight change and tumor response during treatment, large variations in WETs were observed in the relative PDRs which corresponded spatially with an increase in the number of failing points within the GTV, especially in the pharynx area. Failing points were also evident near the posterior neck due to setup variations. Differences in PDRs correlated spatially to differences in the distal dose distribution in the beam's eye view. Virtual PDRs generated from volumetric data, such as pCTs or CBCTs, are potentially a useful quantitative tool in proton therapy. PDRs and WET analysis may be used to detect anatomical change from baseline during treatment and trigger further analysis in adaptive proton therapy.PACS number(s): 87.55‐x, 87.55.‐D, 87.57.Q‐

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“…6. These results were slightly better than the

2.8 \pm 0.2 mm

1.0 \pm 2.0 mm

, and

1.0 \pm 0.2 mm

errors observed in similar studies that employed different DIR algorithms 7 , 24 , 25 , 26 …”

Section: Resultscontrasting

confidence: 53%

Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

Wang

Yin

Zhang

et al. 2016

J Applied Clin Med Phys

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“…From this, a DTA histogram can be produced. Several different metrics can be derived from this DTA histogram and some of the most commonly reported include the mean‐ and maximum‐DTA 14 , 20 and the 95%‐Hausdorff distance (95%‐HD), 21 , 22 which is defined as the 95th percentile of the DTA histogram. Although these metrics provide a measure of the distance between two structures, they too can be difficult to translate into clinical relevance (19) …”

Section: Introductionmentioning

confidence: 99%

The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy

Beasley

McWilliam

Aitkenhead

et al. 2016

J Applied Clin Med Phys

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Contouring structures in the head and neck is time‐consuming, and automatic segmentation is an important part of an adaptive radiotherapy workflow. Geometric accuracy of automatic segmentation algorithms has been widely reported, but there is no consensus as to which metrics provide clinically meaningful results. This study investigated whether geometric accuracy (as quantified by several commonly used metrics) was associated with dosimetric differences for the parotid and larynx, comparing automatically generated contours against manually drawn ground truth contours. This enabled the suitability of different commonly used metrics to be assessed for measuring automatic segmentation accuracy of the parotid and larynx. Parotid and larynx structures for 10 head and neck patients were outlined by five clinicians to create ground truth structures. An automatic segmentation algorithm was used to create automatically generated normal structures, which were then used to create volumetric‐modulated arc therapy plans. The mean doses to the automatically generated structures were compared with those of the corresponding ground truth structures, and the relative difference in mean dose was calculated for each structure. It was found that this difference did not correlate with the geometric accuracy provided by several metrics, notably the Dice similarity coefficient, which is a commonly used measure of spatial overlap. Surface‐based metrics provided stronger correlation and are, therefore, more suitable for assessing automatic segmentation of the parotid and larynx.PACS number(s): 87.57.nm, 87.55.D, 87.55.Qr

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“…There are scarcer studies, however, focused on the CT-CBCT DIR algorithm comparison with H & N patient data. Though a number of CT-CBCT DIR methods for different clinical sites have been developed [11][12][13][14], it is acknowledged that some DIR algorithms may be more suitable for specific anatomies and image modalities due to the mathematical basis of the algorithms [15]. In this sense, it is beneficial to assess the clinical acceptability of the available DIR algorithms to reduce the time and resources required for contour reviewing and correction in the CBCT-based ART process [9].…”

Section: LI Et Al / Evaluation Of Ct-cbct Dir For Contour Propagamentioning

confidence: 99%

Evaluation of deformable image registration for contour propagation between CT and cone-beam CT images in adaptive head and neck radiotherapy

Zhang

Shi

et al. 2016

THC

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Abstract. Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) to propagate contours between planning computerized tomography (CT) images and treatment CT/Cone-beam CT (CBCT) image to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contours mapping, seven intensity-based DIR strategies are tested on the planning CT and weekly CBCT images from six Head & Neck cancer patients who underwent a 6 ∼ 7 weeks intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e. the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), are employed to measure the agreement between the propagated contours and the physician delineated ground truths. It is found that the performance of all the evaluated DIR algorithms declines as the treatment proceeds. No statistically significant performance difference is observed between different DIR algorithms (p > 0.05), except for the double force demons (DFD) which yields the worst result in terms of DSC and PE. For the metric HD, all the DIR algorithms behaved unsatisfactorily with no statistically significant performance difference (p = 0.273). These findings suggested that special care should be taken when utilizing the intensitybased DIR algorithms involved in this study to deform OAR contours between CT and CBCT, especially for those organs with low contrast.

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Deformable planning CT to cone‐beam CT image registration in head‐and‐neck cancer

Abstract: The authors have implemented and validated a deformable image registration method to register planning CT images to weekly CBCT images in head-and-neck cancer cases. The accuracy of the TRE values suggests that they can be used as a promising tool for automatic target delineation on CBCT.

Cited by 86 publications

References 28 publications

Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy

Evaluation of deformable image registration for contour propagation between CT and cone-beam CT images in adaptive head and neck radiotherapy

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