Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors

In image‐guided radiotherapy (IGRT) of prostate cancer, delineation of the clinical target volume (CTV) often relies on magnetic resonance (MR) because of its good soft‐tissue visualization. Registration of MR and computed tomography (CT) is required in order to add this accurate delineation to the dose planning CT. An automatic approach for local MR‐CT registration of the prostate has previously been developed using a voxel property‐based registration as an alternative to a manual landmark‐based registration. The aim of this study is to compare the two registration approaches and to investigate the clinical potential for replacing the manual registration with the automatic registration. Registrations and analysis were performed for 30 prostate cancer patients treated with IGRT using a Ni‐Ti prostate stent as a fiducial marker. The comparison included computing translational and rotational differences between the approaches, visual inspection, and computing the overlap of the CTV. The computed mean translational difference was 1.65, 1.60, and 1.80 mm and the computed mean rotational difference was 1.51°, 3.93°, and 2.09° in the superior/inferior, anterior/posterior, and medial/lateral direction, respectively. The sensitivity of overlap was 87%. The results demonstrate that the automatic registration approach performs registrations comparable to the manual registration.PACS number(s): 87.57.nj, 87.61.‐c, 87.57.Q‐, 87.56.J‐

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of manual and automatic MR‐CT registration for radiotherapy of prostate cancer

Korsager

Carl

Østergaard

2016

“…They compared two metrics: mutual information and mean‐squared intensity difference. Castadot et al11 compared 12 DIR algorithms as applied to adaptive radiation therapy of head and neck cancer using the Dice similarity index12 and the correlation coefficient. We chose the gamma index because of our familiarity with the method (through dose comparison) and it has the feature of defining pass/fail criteria for all pixels in the 3‐D image and a pass/fail map can be generated (i.e., the gamma map).…”

Section: Introductionmentioning

confidence: 99%

Using gamma index to flag changes in anatomy during image‐guided radiation therapy of head and neck cancer

Schaly

Kempe²,

Venkatesan

et al. 2017

During radiation therapy of head and neck cancer, the decision to consider replanning a treatment because of anatomical changes has significant resource implications. We developed an algorithm that compares cone‐beam computed tomography (CBCT) image pairs and provides an automatic alert as to when remedial action may be required. Retrospective CBCT data from ten head and neck cancer patients that were replanned during their treatment was used to train the algorithm on when to recommend a repeat CT simulation (re‐CT). An additional 20 patients (replanned and not replanned) were used to validate the predictive power of the algorithm. CBCT images were compared in 3D using the gamma index, combining Hounsfield Unit (HU) difference with distance‐to‐agreement (DTA), where the CBCT study acquired on the first fraction is used as the reference. We defined the match quality parameter (MQP x) as a difference between the x th percentiles of the failed‐pixel histograms calculated from the reference gamma comparison and subsequent comparisons, where the reference gamma comparison is taken from the first two CBCT images acquired during treatment. The decision to consider re‐CT was based on three consecutive MQP values being less than or equal to a threshold value, such that re‐CT recommendations were within ±3 fractions of the actual re‐CT order date for the training cases. Receiver‐operator characteristic analysis showed that the best trade‐off in sensitivity and specificity was achieved using gamma criteria of 3 mm DTA and 30 HU difference, and the 80th percentile of the failed‐pixel histogram. A sensitivity of 82% and 100% was achieved in the training and validation cases, respectively, with a false positive rate of ~30%. We have demonstrated that gamma analysis of CBCT‐acquired anatomy can be used to flag patients for possible replanning in a manner consistent with local clinical practice guidelines.

“…( 11 , 12 , 13 , 14 ) It has become common practice to compare algorithms by evaluating similarity measures such as the sum of square intensity differences (SSD), correlation coefficient (CC), and mutual information (MI) upon completion of image registration. ( 13 , 15 , 16 , 17 ) It is assumed that there is a direct correlation between enhanced similarity values and registration accuracy. Although these methods may be sufficient for relative comparison, they provide little information on the absolute accuracy of registration.…”

Section: Introductionmentioning

confidence: 99%

“…Various measures of volume overlap evaluated using radiation oncologist delineated prostate contours have been used for validation of deformable registration of

{normalT}_{2}

‐weighted pelvic images with and without inflated endorectal coils. ( 29 , 30 , 31 ) Volumetric‐based methods have also been used when validating deformable registration of pelvic CT ( 1 , 2 ) and head and neck CT ( 16 ) images.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a commercially available automatic deformable registration system

Fallone

Rivest

Riauka

et al. 2010

In recent years, a number of approaches have been applied to the problem of deformable registration validation. However, the challenge of assessing a commercial deformable registration system – in particular, an automatic registration system in which the deformable transformation is not readily accessible – has not been addressed. Using a collection of novel and established methods, we have developed a comprehensive, four‐component protocol for the validation of automatic deformable image registration systems over a range of IGRT applications. The protocol, which was applied to the Reveal‐MVS system, initially consists of a phantom study for determination of the system's general tendencies, while relative comparison of different registration settings is achieved through postregistration similarity measure evaluation. Synthetic transformations and contour‐based metrics are used for absolute verification of the system's intra‐modality and inter‐modality capabilities, respectively. Results suggest that the commercial system is more apt to account for global deformations than local variations when performing deformable image registration. Although the protocol was used to assess the capabilities of the Reveal‐MVS system, it can readily be applied to other commercial systems. The protocol is by no means static or definitive, and can be further expanded to investigate other potential deformable registration applications.PACS numbers: 87.19.xj, 87.56.Da, 87.57.nj