Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

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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“…The entire process is encompassed by the deformation vector field (DVF), which aggregates the individual vectors into a single map and specifies the coordinate transformation between the two datasets. The DVF facilitates the transfer of information and allows the user to perform a number of useful functions such as contour propagation (1) or dose accumulation (2) . Initially, these functions were primarily limited to academic centers where many of the deformation algorithms were developed.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

Pukala

Johnson

Shah

et al. 2016

J Applied Clin Med Phys

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Benchmarking is a process in which standardized tests are used to assess system performance. The data produced in the process are important for comparative purposes, particularly when considering the implementation and quality assurance of DIR algorithms. In this work, five commercial DIR algorithms (MIM, Velocity, RayStation, Pinnacle, and Eclipse) were benchmarked using a set of 10 virtual phantoms. The phantoms were previously developed based on CT data collected from real head and neck patients. Each phantom includes a start of treatment CT dataset, an end of treatment CT dataset, and the ground‐truth deformation vector field (DVF) which links them together. These virtual phantoms were imported into the commercial systems and registered through a deformable process. The resulting DVFs were compared to the ground‐truth DVF to determine the target registration error (TRE) at every voxel within the image set. Real treatment plans were also recalculated on each end of treatment CT dataset and the dose transferred according to both the ground‐truth and test DVFs. Dosimetric changes were assessed, and TRE was correlated with changes in the DVH of individual structures. In the first part of the study, results show mean TRE on the order of 0.5 mm to 3 mm for all phantoms and ROIs. In certain instances, however, misregistrations were encountered which produced mean and max errors up to 6.8 mm and 22 mm, respectively. In the second part of the study, dosimetric error was found to be strongly correlated with TRE in the brainstem, but weakly correlated with TRE in the spinal cord. Several interesting cases were assessed which highlight the interplay between the direction and magnitude of TRE and the dose distribution, including the slope of dosimetric gradients and the distance to critical structures. This information can be used to help clinicians better implement and test their algorithms, and also understand the strengths and weaknesses of a dose adaptive approach.PACS number(s): 87.57.nj, 87.55.dk, 87.55.Qr

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“…This is of particular benefit to adaptive radiotherapy (ART), and there has therefore been much interest in automatic segmentation, with several algorithms having been assessed for accuracy 7 , 11 , 12 , 13 …”

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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Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

Cited by 169 publications

References 27 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

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

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

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