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Honzawa K, Horiguchi H, Terauchi R, Iida Y, Katagiri S, Gunji H, Nakano T. RHOMBUS DEFORMATION OF RETINAL LATERAL DISPLACEMENT AFTER EPIRETINAL MEMBRANE REMOVAL REVEALED BY DIFFEOMORPHIC IMAGE REGISTRATION. Retina 2023; 43:1132-1142. [PMID: 36893431 DOI: 10.1097/iae.0000000000003775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
PURPOSE To establish an analysis method using diffeomorphic image registration and evaluate microvascular displacement through epiretinal membrane (ERM) removal. METHODS Medical records of eyes that underwent vitreous surgery for ERM were reviewed. Postoperative optical coherence tomography angiography (OCTA) images were converted to the corresponding preoperative images according to a configured algorithm using diffeomorphism. RESULTS Thirty-seven eyes with ERM were examined. Measured changes in the foveal avascular zone (FAZ) area showed a significant negative correlation with central foveal thickness (CFT). The average amplitude of microvascular displacement calculated for each pixel was 69 ± 27 µ m in the nasal area, which was relatively smaller than that in other areas. The vector map, which included both the amplitude and the vector of microvasculature displacement, showed a unique vector flow pattern called the rhombus deformation sign in 17 eyes. Eyes with this deformation sign showed less surgery-induced changes in the FAZ area and CFT and a milder ERM stage than those without this sign. CONCLUSION The authors calculated and visualized microvascular displacement using diffeomorphism. The authors found a unique pattern (rhombus deformation) of retinal lateral displacement through ERM removal, which was significantly associated with the severity of ERM.
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Affiliation(s)
- Koki Honzawa
- Department of Ophthalmology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan
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Haghighi B, D Ellingwood N, Yin Y, Hoffman EA, Lin CL. A GPU-based symmetric non-rigid image registration method in human lung. Med Biol Eng Comput 2018; 56:355-371. [PMID: 28762017 PMCID: PMC5794656 DOI: 10.1007/s11517-017-1690-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 07/16/2017] [Indexed: 11/26/2022]
Abstract
Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD. The GPU version demonstrated an average of 43 times speedup and ~5.2 times speedup over the single-threaded and 12-threaded CPU versions, respectively. Run times with the GPU were as fast as 2 min. The symmetric method improved the inverse consistency, aiding the use of image registration in the QCT-based evaluation of the lung.
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Affiliation(s)
- Babak Haghighi
- Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IO, 52242, USA
- IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IO, 52242, USA
| | - Nathan D Ellingwood
- IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IO, 52242, USA
| | - Youbing Yin
- Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IO, 52242, USA
| | - Eric A Hoffman
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IO, 52242, USA
- Department of Internal Medicine, The University of Iowa, Iowa City, IO, 52242, USA
- Department of Radiology, The University of Iowa, Iowa City, IO, 52242, USA
| | - Ching-Long Lin
- Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IO, 52242, USA.
- IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IO, 52242, USA.
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Ciardo D, Jereczek-Fossa BA, Petralia G, Timon G, Zerini D, Cambria R, Rondi E, Cattani F, Bazani A, Ricotti R, Garioni M, Maestri D, Marvaso G, Romanelli P, Riboldi M, Baroni G, Orecchia R. Multimodal image registration for the identification of dominant intraprostatic lesion in high-precision radiotherapy treatments. Br J Radiol 2017; 90:20170021. [PMID: 28830203 DOI: 10.1259/bjr.20170021] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The integration of CT and multiparametric MRI (mpMRI) is a challenging task in high-precision radiotherapy for prostate cancer. A simple methodology for multimodal deformable image registration (DIR) of prostate cancer patients is presented. METHODS CT and mpMRI of 10 patients were considered. Organs at risk and prostate were contoured on both scans. The dominant intraprostatic lesion was additionally delineated on MRI. After a preliminary rigid image registration, the voxel intensity of all the segmented structures in both scans except the prostate was increased by a specific amount (a constant additional value, A), in order to enhance the contrast of the main organs influencing its position and shape. 70 couples of scans were obtained by varying A from 0 to 800 and they were subsequently non-rigidly registered. Quantities derived from image analysis and contour statistics were considered for the tuning of the best performing A. RESULTS A = 200 resulted the minimum enhancement value required to obtain statistically significant superior registration results. Mean centre of mass distance between corresponding structures decreases from 7.4 mm in rigid registration to 5.3 mm in DIR without enhancement (DIR-0) and to 2.7 mm in DIR with A = 200 (DIR-200). Mean contour distance was 2.5, 1.9 and 0.67 mm in rigid registration, DIR-0 and DIR-200, respectively. In DIR-200 mean contours overlap increases of +13 and +24% with respect to DIR-0 and rigid registration, respectively. CONCLUSION Contour propagation according to the vector field resulting from DIR-200 allows the delineation of dominant intraprostatic lesion on CT scan and its use for high-precision radiotherapy treatment planning. Advances in knowledge: We investigated the application of a B-spline, mutual information-based multimodal DIR coupled with a simple, patient-unspecific but efficient contrast enhancement procedure in the pelvic body area, thus obtaining a robust and accurate methodology to transfer the functional information deriving from mpMRI onto a planning CT reference volume.
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Affiliation(s)
- Delia Ciardo
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy.,2 Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Giuseppe Petralia
- 3 Division of Radiology, European Institute of Oncology, Milan, Italy
| | - Giorgia Timon
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy
| | - Dario Zerini
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy
| | - Raffaella Cambria
- 4 Unit of Medical Physics, European Institute of Oncology, Milan, Italy
| | - Elena Rondi
- 4 Unit of Medical Physics, European Institute of Oncology, Milan, Italy
| | - Federica Cattani
- 4 Unit of Medical Physics, European Institute of Oncology, Milan, Italy
| | - Alessia Bazani
- 4 Unit of Medical Physics, European Institute of Oncology, Milan, Italy
| | - Rosalinda Ricotti
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy
| | - Maria Garioni
- 4 Unit of Medical Physics, European Institute of Oncology, Milan, Italy
| | - Davide Maestri
- 4 Unit of Medical Physics, European Institute of Oncology, Milan, Italy
| | - Giulia Marvaso
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy
| | - Paola Romanelli
- 1 Division of Radiation Oncology, European Institute of Oncology, Milan, Italy
| | - Marco Riboldi
- 5 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Guido Baroni
- 5 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.,6 Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica (CNAO Foundation), Pave, Italy
| | - Roberto Orecchia
- 2 Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy.,7 Department of Medical Imaging and Radiation Sciences, European Institute of Oncology, Milan, Italy
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Peroni M, Golland P, Sharp GC, Baroni G. Stopping Criteria for Log-Domain Diffeomorphic Demons Registration: An Experimental Survey for Radiotherapy Application. Technol Cancer Res Treat 2014; 15:77-90. [PMID: 24000996 DOI: 10.7785/tcrtexpress.2013.600269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2013] [Accepted: 06/26/2013] [Indexed: 11/06/2022] Open
Abstract
A crucial issue in deformable image registration is achieving a robust registration algorithm at a reasonable computational cost. Given the iterative nature of the optimization procedure an algorithm must automatically detect convergence, and stop the iterative process when most appropriate. This paper ranks the performances of three stopping criteria and six stopping value computation strategies for a Log-Domain Demons Deformable registration method simulating both a coarse and a fine registration. The analyzed stopping criteria are: (a) velocity field update magnitude, (b) mean squared error, and (c) harmonic energy. Each stoping condition is formulated so that the user defines a threshold ∊, which quantifies the residual error that is acceptable for the particular problem and calculation strategy. In this work, we did not aim at assigning a value to e, but to give insights in how to evaluate and to set the threshold on a given exit strategy in a very popular registration scheme. Experiments on phantom and patient data demonstrate that comparing the optimization metric minimum over the most recent three iterations with the minimum over the fourth to sixth most recent iterations can be an appropriate algorithm stopping strategy. The harmonic energy was found to provide best trade-off between robustness and speed of convergence for the analyzed registration method at coarse registration, but was outperformed by mean squared error when all the original pixel information is used. This suggests the need of developing mathematically sound new convergence criteria in which both image and vector field information could be used to detect the actual convergence, which could be especially useful when considering multi-resolution registrations. Further work should be also dedicated to study same strategies performances in other deformable registration methods and body districts.
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Affiliation(s)
- M Peroni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italia
| | - P Golland
- Computer Science and Artificial Intelligence Laboratory, Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - G C Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA Harvard Medical School, Boston, MA
| | - G Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italia Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
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Bert C, Graeff C, Riboldi M, Nill S, Baroni G, Knopf AC. Advances in 4D treatment planning for scanned particle beam therapy - report of dedicated workshops. Technol Cancer Res Treat 2014; 13:485-95. [PMID: 24354749 PMCID: PMC4527425 DOI: 10.7785/tcrtexpress.2013.600274] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 09/27/2013] [Accepted: 10/01/2013] [Indexed: 11/25/2022] Open
Abstract
We report on recent progress in the field of mobile tumor treatment with scanned particle beams, as discussed in the latest editions of the 4D treatment planning workshop. The workshop series started in 2009, with about 20 people from 4 research institutes involved, all actively working on particle therapy delivery and development. The first workshop resulted in a summary of recommendations for the treatment of mobile targets, along with a list of requirements to apply these guidelines clinically. The increased interest in the treatment of mobile tumors led to a continuously growing number of attendees: the 2012 edition counted more than 60 participants from 20 institutions and commercial vendors. The focus of research discussions among workshop participants progressively moved from 4D treatment planning to complete 4D treatments, aiming at effective and safe treatment delivery. Current research perspectives on 4D treatments include all critical aspects of time resolved delivery, such as in-room imaging, motion detection, beam application, and quality assurance techniques. This was motivated by the start of first clinical treatments of hepato cellular tumors with a scanned particle beam, relying on gating or abdominal compression for motion mitigation. Up to date research activities emphasize significant efforts in investigating advanced motion mitigation techniques, with a specific interest in the development of dedicated tools for experimental validation. Potential improvements will be made possible in the near future through 4D optimized treatment plans that require upgrades of the currently established therapy control systems for time resolved delivery. But since also these novel optimization techniques rely on the validity of the 4DCT, research focusing on alternative 4D imaging technique, such as MRI based 4DCT generation will continue.
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Affiliation(s)
- Christoph Bert
- University Clinic Erlangen, Radiation Oncology, Universitatsstrasse 27, 91054 Erlangen, Germany.
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Kumarasiri A, Siddiqui F, Liu C, Yechieli R, Shah M, Pradhan D, Zhong H, Chetty IJ, Kim J. Deformable image registration based automatic CT-to-CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting. Med Phys 2014; 41:121712. [DOI: 10.1118/1.4901409] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Gruslys A, Acosta-Cabronero J, Nestor PJ, Williams GB, Ansorge RE. A new fast accurate nonlinear medical image registration program including surface preserving regularization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2118-2127. [PMID: 24968094 DOI: 10.1109/tmi.2014.2332370] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Recently inexpensive graphical processing units (GPUs) have become established as a viable alternative to traditional CPUs for many medical image processing applications. GPUs offer the potential of very significant improvements in performance at low cost and with low power consumption. One way in which GPU programs differ from traditional CPU programs is that increasingly elaborate calculations per voxel may not impact of the overall processing time because memory accesses can dominate execution time. This paper presents a new GPU based elastic image registration program named Ezys. The Ezys image registration algorithm belongs to the wide class of diffeomorphic demons but uses surface preserving image smoothing and regularization filters designed for a GPU that would be computationally expensive on a CPU. We describe the methods used in Ezys and present results from two important neuroscience applications. Firstly inter-subject registration for transfer of anatomical labels and secondly longitudinal intra-subject registration to quantify atrophy in individual subjects. Both experiments showed that Ezys registration compares favorably with other popular elastic image registration programs. We believe Ezys is a useful tool for neuroscience and other applications, and also demonstrates the value of developing of novel image processing filters specifically designed for GPUs.
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Sharp G, Fritscher KD, Pekar V, Peroni M, Shusharina N, Veeraraghavan H, Yang J. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys 2014; 41:050902. [PMID: 24784366 PMCID: PMC4000389 DOI: 10.1118/1.4871620] [Citation(s) in RCA: 223] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Revised: 04/01/2014] [Accepted: 04/03/2014] [Indexed: 12/25/2022] Open
Abstract
Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.
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Affiliation(s)
- Gregory Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Karl D Fritscher
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114
| | | | - Marta Peroni
- Center for Proton Therapy, Paul Scherrer Institut, 5232 Villigen-PSI, Switzerland
| | - Nadya Shusharina
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065
| | - Jinzhong Yang
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, Texas 77030
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