Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies

Dossun, C.; Niederst, C.; Noël, Georges; Meyer, P.

doi:10.1016/j.ejmp.2022.08.011

Cited by 7 publications

(4 citation statements)

References 127 publications

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“…11 While these algorithms have shown success in registering images such as cervical CTs, 24 thoracic 4DCTs or CTs, 21 , 25 and head and neck MRIs, 26 their performance in registering abdominal MRIs has not been comprehensively evaluated. 27 In this study, we compared these algorithms using metrics recommended by the AAPM task group and other researchers and evaluated their performance at organ boundaries and interior regions, respectively. To address large uncertainties identified in interior regions, we developed a mechanical modeling method to adjust resultant DVFs to facilitate uninterrupted clinical procedures.…”

Section: Discussionmentioning

confidence: 99%

“…The MIM software version 7.06 includes three image or contour‐based DIR algorithms as documented in 11 . While these algorithms have shown success in registering images such as cervical CTs, 24 thoracic 4DCTs or CTs, 21,25 and head and neck MRIs, 26 their performance in registering abdominal MRIs has not been comprehensively evaluated 27 . In this study, we compared these algorithms using metrics recommended by the AAPM task group and other researchers and evaluated their performance at organ boundaries and interior regions, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Zhong,

Kainz,

Paulson

2024

J Applied Clin Med Phys

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PurposeWe evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties.Materials and MethodsThree evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD‐JD), are used to assess the multi‐modality (MM), contour‐consistency (CC), and image‐intensity (II)‐based DIR algorithms in the MIM software package, as well as an in‐house developed, contour matching‐based finite element method (CM‐FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One‐tailed Wilcoxon‐Mann‐Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications.ResultsFor the registrations performed with the MIM‐CC, MIM‐MM, MIM‐II, and CM‐FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p‐values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD‐JDs and greater than 0.87 for MDA and DSC.ConclusionsAmong the three MIM DIR algorithms, MIM‐CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Zhong,

Kainz,

Paulson

2024

J Applied Clin Med Phys

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“…33 Although DIR has been a hot topic of research over the years, it still poses some challenges with different algorithms,image modalities, image quality, and patient sites. 15 For example, a DIR-based autosegmentation has a good performance on sites with small deformation, such as the head and neck 34 ; however, DIR algorithms often fail to yield clinically acceptable results for organs that may display large deformations between planning CT and online MR images. For prostate cancer, as in this study, the DIR method had a good performance for some structures, which had little changes between simulation CT and online MR images, such as that of CTV (87.3%) and femur heads (92.7%).…”

Section: Discussionmentioning

confidence: 99%

Pretreatment information–aided automatic segmentation for online magnetic resonance imaging‐guided prostate radiotherapy

et al. 2023

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BackgroundIt is necessary to contour regions of interest (ROIs) for online magnetic resonance imaging (MRI)‐guided adaptive radiotherapy (MRIgART). These updated contours are used for online replanning to obtain maximum dosimetric benefits. Contouring can be accomplished using deformable image registration (DIR) and deep learning (DL)‐based autosegmentation methods. However, these methods may require considerable manual editing and thus prolong treatment time.PurposeThe present study aimed to improve autosegmentation performance by integrating patients’ pretreatment information in a DL‐based segmentation algorithm. It is expected to improve the efficiency of current MRIgART process.MethodsForty patients with prostate cancer were enrolled retrospectively. The online adaptive MR images, patient‐specific planning computed tomography (CT), and contours in CT were used for segmentation. The deformable registration of planning CT and MR images was performed first to obtain a deformable CT and corresponding contours. A novel DL network, which can integrate such patient‐specific information (deformable CT and corresponding contours) into the segmentation task of MR images was designed. We performed a four‐fold cross‐validation for the DL models. The proposed method was compared with DIR and DL methods on segmentation of prostate cancer. The ROIs included the clinical target volume (CTV), bladder, rectum, left femur head, and right femur head. Dosimetric parameters of automatically generated ROIs were evaluated using a clinical treatment planning system.ResultsThe proposed method enhanced the segmentation accuracy of conventional procedures. Its mean value of the dice similarity coefficient (93.5%) over the five ROIs was higher than both DIR (87.5%) and DL (87.2%). The number of patients (n = 40) that required major editing using DIR, DL, and our method were 12, 18, and 7 (CTV); 17, 4, and 1 (bladder); 8, 11, and 5 (rectum); 2, 4, and 1 (left femur head); and 3, 7, and 1 (right femur head), respectively. The Spearman rank correlation coefficient of dosimetry parameters between the proposed method and ground truth was 0.972 ± 0.040, higher than that of DIR (0.897 ± 0.098) and DL (0.871 ± 0.134).ConclusionThis study proposed a novel method that integrates patient‐specific pretreatment information into DL‐based segmentation algorithm. It outperformed baseline methods, thereby improving the efficiency and segmentation accuracy in adaptive radiotherapy.

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“…Finally, possible research directions were highlighted: 1) hybrid models (classical methods and deep learning), and 2) Boosting MIR performance with priors. Dossun et al (2022) reviewed the performance of deformable IR in radiotherapy treatments in real patients. First, the scope of the paper and the paper selection process were explained.…”

Section: Related Survey Papersmentioning

confidence: 99%

Deep Learning in Medical Image Registration: Introduction and Survey

Hammoudeh,

Dupont

2023

Preprint

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Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

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Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies

Cited by 7 publications

References 127 publications

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Evaluation and mitigation of deformable image registration uncertainties for MRI‐guided adaptive radiotherapy

Pretreatment information–aided automatic segmentation for online magnetic resonance imaging‐guided prostate radiotherapy

Deep Learning in Medical Image Registration: Introduction and Survey

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