Site-specific deformable imaging registration algorithm selection using patient-based simulated deformations

Utility and validation of biomechanical deformable image registration in low-contrast images

PURPOSE

The application of a biomechanical deformable image registration algorithm has been demonstrated to overcome the potential limitations in the use of intensity-based algorithms on low-contrast images that lack prominent features. Because validation of deformable registration is particularly challenging on such images, the dose distribution predicted via a biomechanical algorithm was evaluated using the measured dose from a deformable dosimeter.

METHODS AND MATERIALS

A biomechanical model-based image registration algorithm registered computed tomographic (CT) images of an elastic radiochromic dosimeter between its undeformed and deformed positions. The algorithm aligns the external boundaries of the dosimeter, created from CT contours, and the internal displacements are solved by modeling the physical material properties of the dosimeter. The dosimeter was planned and irradiated in its deformed position, and subsequently, the delivered dose was measured with optical CT in the undeformed position. The predicted dose distribution, created by applying the deformable registration displacement map to the planned distribution, was then compared with the measured optical CT distribution.

RESULTS

Compared with the optical CT distribution, biomechanical image registration predicted the position and size of the deformed dose fields with mean errors of ≤1 mm (maximum, 3 mm). The accuracy did not differ between cross sections with a greater or lesser deformation magnitude despite the homogenous CT intensities throughout the dosimeter. The overall 3-dimensional voxel passing rate of the predicted distribution was γ3%/3mm = 91% compared with optical CT.

CONCLUSIONS

Biomechanical registration accurately predicted the deformed dose distribution measured in a deformable dosimeter, whereas previously, evaluations of a commercial intensity-based algorithm demonstrated substantial errors. The addition of biomechanical algorithms to the collection of adaptive radiation therapy tools would be valuable for dose accumulation, particularly in feature-poor images such as cone beam CT and organs such as the liver.

Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms

PURPOSE

To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation computed tomography (CT) with diagnostic CT coregistration.

MATERIALS AND METHODS

Radiation therapy-simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison.

RESULTS

A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness.

CONCLUSION

Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation.

Quantitative Analysis Tools and Digital Phantoms for Deformable Image Registration Quality Assurance

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R'. The data set, R', T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.

Sensor, signal, and imaging informatics: big data and smart health technologies

OBJECTIVES

This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2014 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2013, with a focus on Big Data and Smart Health Technologies Methods: We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2013, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used.

RESULTS

Big Data are collections of large and complex datasets which have the potential to capture the whole variability of a study population. More and more innovative sensors are emerging, allowing to enrich these big databases. However they become more and more challenging to process (i.e. capture, store, search, share, transfer, exploit) because traditional tools are not adapted anymore.

CONCLUSIONS

This review shows that it is necessary not only to develop new tools specifically designed for Big Data, but also to evaluate their performance on such large datasets.

Validation of nonrigid registration in pretreatment and follow-up PET/CT scans for quantification of tumor residue in lung cancer patients

Nonrigid registrations of pre‐ and postradiotherapy (RT) PET/CT scans of NSCLC patients were performed with different algorithms and validated tracking internal landmarks. Dice overlap ratios (DR) of high FDG‐uptake areas in registered PET/CT scans were then calculated to study patterns of relapse. For 22 patients, pre‐ and post‐RT PET/CT scans were registered first rigidly and then nonrigidly. For three patients, two types (based on Demons or Morphons) of nonrigid registration algorithms each with four different parameter settings were applied and assessed using landmark validation. The two best performing methods were tested on all patients, who were then classified into three groups: large (Group 1), minor (Group 2) or insufficient improvement (Group 3) of registration accuracy. For Group 1 and 2, DRs between high FDG‐uptake areas in pre‐ and post‐RT PET scans were determined. Distances between corresponding landmarks on deformed pre‐RT and post‐RT scans decreased for all registration methods. Differences between Demons and Morphons methods were smaller than 1 mm. For Group 1, landmark distance decreased from 9.5 ± 2.1 mm to 3.8 ± 1.2 mm (mean ± 1 SD, p < 0.001), and for Group 3 from 13.6 ± 3.2 mm to 8.0 ± 2.2 mm (p=0.02). No significant change was observed for Group 2 where distances decreased from 5.6 ± 1.3 mm to 4.5 ± 1.1 mm (p=0.02). DRs of high FDG‐uptake areas improved significantly after nonrigid registration for most patients in Group 1. Landmark validation of nonrigid registration methods for follow‐up CT imaging in NSCLC is necessary. Nonrigid registration significantly improves matching between pre‐ and post‐RT CT scans for a subset of patients, although not in all patients. Hence, the quality of the registration needs to be assessed for each patient individually. Successful nonrigid registration increased the overlap between pre‐ and post‐RT high FDG‐uptake regions.

PACS number: 87.57.Q‐, 87.57.C‐, 87.57.N‐, 87.57.‐s, 87.55.‐x, 87.55.D‐, 87.55.dh, 87.57.uk, 87.57.nj

Surface-constrained nonrigid registration for dose monitoring in prostate cancer radiotherapy

This paper addresses the issue of cumulative dose estimation from cone beam computed tomography (CBCT) images in prostate cancer radiotherapy. It focuses on the dose received by the surfaces of the main organs at risk, namely the bladder and rectum. We have proposed both a surface-constrained dose accumulation approach and its extensive evaluation. Our approach relied on the nonrigid registration (NRR) of daily acquired CBCT images on the planning CT image. This proposed NRR method was based on a Demons-like algorithm, implemented in combination with mutual information metric. It allowed for different levels of geometrical constraints to be considered, ensuring a better point to point correspondence, especially when large deformations occurred, or in high dose gradient areas. The three following implementations: 1) full iconic NRR; 2) iconic NRR constrained with landmarks (LCNRR); 3) NRR constrained with full delineation of organs (DBNRR). To obtain reference data, we designed a numerical phantom based on finite-element modeling and image simulation. The methods were assessed on both the numerical phantom and real patient data in order to quantify uncertainties in terms of dose accumulation. The LCNRR method appeared to constitute a good compromise for dose monitoring in clinical practice.

Mapping of dose distribution from IMRT onto MRI-guided high dose rate brachytherapy using deformable image registration for cervical cancer treatments: preliminary study with commercially available software

PURPOSE

For patients undergoing external beam radiation therapy (EBRT) and brachytherapy, recommendations for target doses and constraints are based on calculation of the equivalent dose in 2 Gy fractions (EQD2) from each phase. At present, the EBRT dose distribution is assumed to be uniform throughout the pelvis. We performed a preliminary study to determine whether deformable dose distribution mapping from the EBRT onto magnetic resonance (MR) images for the brachytherapy would yield differences in doses for organs at risk (OARs) and high-risk clinical target volume (HR-CTV).

MATERIAL AND METHODS

Nine cervical cancer patients were treated to a total dose of 45 Gy in 25 fractions using intensity-modulated radiation therapy (IMRT), followed by MRI-based 3D high dose rate (HDR) brachytherapy. Retrospectively, the IMRT planning CT images were fused with the MR image for each fraction of brachytherapy using deformable image registration. The deformed IMRT dose onto MR images were converted to EQD2 and compared to the uniform dose assumption.

RESULTS

For all patients, the EQD2 from the EBRT phase was significantly higher with deformable registration than with the conventional uniform dose distribution assumption. The mean EQD2 ± SD for HR-CTV D90 was 45.7 ± 0.7 Gy vs. 44.3 Gy for deformable vs. uniform dose distribution, respectively (p < 0.001). The dose to 2 cc of the bladder, rectum, and sigmoid was 46.4 ± 1.2 Gy, 46.2 ± 1.0 Gy, and 48.0 ± 2.5 Gy, respectively with deformable dose distribution, and was significantly higher than with uniform dose distribution (43.2 Gy for all OAR, p < 0.001).

CONCLUSIONS

This study reveals that deformed EBRT dose distribution to HR-CTV and OARs in MR images for brachytherapy is technically feasible, and achieves differences compared to a uniform dose distribution. Therefore, the assumption that EBRT contributes the same dose value may need to be carefully investigated further based on deformable image registration.

Using patient-specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy

The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B‐spline‐based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast‐Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM‐DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0~3.1mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B‐spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient‐specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient‐dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy.

PACS numbers: 87.10.Kn, 87.55.km, 87.55.Qr, 87.57.nj