1
|
Gong YJ, Li YK, Zhou R, Liang Z, Zhang Y, Cheng T, Zhang ZJ. A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT. Comput Med Imaging Graph 2024; 115:102385. [PMID: 38663077 DOI: 10.1016/j.compmedimag.2024.102385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/18/2023] [Accepted: 04/15/2024] [Indexed: 06/03/2024]
Abstract
Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor's deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).
Collapse
Affiliation(s)
- Ye-Jun Gong
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Yue-Ke Li
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Rongrong Zhou
- Department of Radiation Oncology, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China
| | - Zhan Liang
- Department of Radiation Oncology, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China
| | - Yingying Zhang
- Department of Radiation Oncology, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China
| | - Tingting Cheng
- Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Department of general practice, Xiangya Hospital Central South University, Changsha, Hunan, PR China.
| | - Zi-Jian Zhang
- Department of Radiation Oncology, Xiangya Hospital Central South University, Changsha, Hunan, PR China; Xiangya Lung Cancer Center, Xiangya Hospital Central South University, Changsha, Hunan, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, Hunan, PR China.
| |
Collapse
|
2
|
Lombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, Tijssen RHN, Paganelli C, Liu PZY, Keall PJ, Riboldi M, Kurz C, Landry G, Cusumano D, Fusella M, Placidi L. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects. Radiother Oncol 2024; 190:109970. [PMID: 37898437 DOI: 10.1016/j.radonc.2023.109970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.
Collapse
Affiliation(s)
- Elia Lombardo
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Denis Page
- University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom
| | - Cristina Garibaldi
- IEO, Unit of Radiation Research, European Institute of Oncology IRCCS, Milan, Italy
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Rob H N Tijssen
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Paul Z Y Liu
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Paul J Keall
- Image X Institute, University of Sydney Central Clinical School, Sydney, NSW, Australia; Department of Medical Physics, Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy
| |
Collapse
|
3
|
Palacios MA, Gerganov G, Cobussen P, Tetar SU, Finazzi T, Slotman BJ, Senan S, Haasbeek CJ, Kawrakow I. Accuracy of deformable image registration-based intra-fraction motion management in Magnetic Resonance-guided radiotherapy. Phys Imaging Radiat Oncol 2023; 26:100437. [PMID: 37089906 PMCID: PMC10113900 DOI: 10.1016/j.phro.2023.100437] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023] Open
Abstract
Background and Purpose Intra-fraction motion management is key in Stereotactic Ablative Radiotherapy (SABR) gated delivery. This study assessed the accuracy of automatic tumor segmentation in the delivery of MR-guided radiotherapy (MRgRT) by comparing it to manual delineations performed by experienced observers. Materials and Methods Twenty patients previously treated with MR-guided SABR for thoracic and abdominal tumors were included. Five observers with at least two years of experience in MRgRT manually delineated the gross tumor volume (GTV) for 20 patients on 240 frames of a cine MRI on a sagittal plane. Deformable Image Registration (DIR) based GTV contours were propagated using four different algorithms from a reference frame to subsequent frames.Geometrical analysis based on the Dice Similarity Coefficient (DSC), centroid distance and Hausdorff Distance (HDD) were performed to assess the inter-observer variability and the accuracy of automatic segmentation. A Confidence Value (CV) metric for the reliability of the tumor auto-contouring was also calculated. Results Inter-observer delineation variability resulted in mean DSC of 0.89, HDD of 5.8 mm and centroid distance of 1.7 mm. Tumor auto-contouring by the four DIR algorithms resulted in an excellent agreement with the manual delineations by the experienced observers. Mean DSC for each algorithm across all patients was greater than 0.90, whereas the HDD and centroid distances were below 4.0 mm and 1.5 mm, respectively. The CV showed a strong correlation with the DSC. Conclusions DIR-based auto-contouring in MRgRT exhibited a high level of agreement with the manual contouring performed by experts, allowing accurate gated delivery.
Collapse
|
4
|
Charters JA, Abdulkadir Y, O'Connell D, Yang Y, Lamb JM. Dosimetric evaluation of respiratory gating on a 0.35-T magnetic resonance-guided radiotherapy linac. J Appl Clin Med Phys 2022; 23:e13666. [PMID: 35950272 PMCID: PMC9815517 DOI: 10.1002/acm2.13666] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 01/11/2023] Open
Abstract
PURPOSE The commercial 0.35-T magnetic resonance imaging (MRI)-guided radiotherapy vendor ViewRay recently introduced upgraded real-time imaging frame rates based on compressed sensing techniques. Furthermore, additional motion tracking algorithms were made available. Compressed sensing allows for increased image frame rates but may compromise image quality. To assess the impact of this upgrade on respiratory gating accuracy, we evaluated gated dose distributions pre- and post-upgrade using a motion phantom and radiochromic film. METHODS Seven motion waveforms (four artificial, two patient-derived free-breathing, and one breath-holding) were used to drive an MRI-compatible motion phantom. A treatment plan was developed to deliver a 3-cm diameter spherical dose distribution typical of a stereotactic body radiotherapy plan. Gating was performed using 4-frames per second (fps) imaging pre-upgrade on the "default" tracking algorithm and 8-fps post-upgrade using the "small mobile targets" (SMT) and "large deforming targets" (LDT) tracking algorithms. Radiochromic film was placed in a moving insert within the phantom to measure dose. The planned and delivered dose distributions were compared using the gamma index with 3%/3-mm criteria. Dose-area histograms were produced to calculate the dose to 95% (D95) of the sphere planning target volume (PTV) and two simulated gross tumor volumes formed by contracting the PTV by 3 and 5 mm, respectively. RESULTS Gamma pass rates ranged from 18% to 93% over the 21 combinations of breathing trace and gating conditions examined. D95 ranged from 206 to 514 cGy. On average, the LDT algorithm yielded lower gamma and D95 values than the default and SMT algorithms. CONCLUSION Respiratory gating at 8 fps with the new tracking algorithms provides similar gating performance to the original algorithm with 4 fps, although the LDT algorithm had lower accuracy for our non-deformable target. This indicates that the choice of deformable image registration algorithm should be chosen deliberately based on whether the target is rigid or deforming.
Collapse
Affiliation(s)
- John A. Charters
- Department of Radiation OncologyDavid Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesLos AngelesCaliforniaUSA
| | - Yasin Abdulkadir
- Department of Radiation OncologyDavid Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesLos AngelesCaliforniaUSA
| | - Dylan O'Connell
- Department of Radiation OncologyDavid Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesLos AngelesCaliforniaUSA
| | - Yingli Yang
- Department of Radiation OncologyDavid Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesLos AngelesCaliforniaUSA
| | - James M. Lamb
- Department of Radiation OncologyDavid Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesLos AngelesCaliforniaUSA
| |
Collapse
|
5
|
Reconstruction of Iberian ceramic potteries using generative adversarial networks. Sci Rep 2022; 12:10644. [PMID: 35739184 PMCID: PMC9225991 DOI: 10.1038/s41598-022-14910-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/14/2022] [Indexed: 11/24/2022] Open
Abstract
Several aspects of past culture, including historical trends, are inferred from time-based patterns observed in archaeological artifacts belonging to different periods. The presence and variation of these objects provides important clues about the Neolithic revolution and given their relative abundance in most archaeological sites, ceramic potteries are significantly helpful in this purpose. Nonetheless, most available pottery is fragmented, leading to missing morphological information. Currently, the reassembly of fragmented objects from a collection of thousands of mixed fragments is a daunting and time-consuming task done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction and improve the quality of reconstructed samples, we present IberianGAN, a customized Generative Adversarial Network (GAN) tested on an extensive database with complete and fragmented references. We trained the model with 1072 samples corresponding to Iberian wheel-made pottery profiles belonging to archaeological sites located in the upper valley of the Guadalquivir River (Spain). Furthermore, we provide quantitative and qualitative assessments to measure the quality of the reconstructed samples, along with domain expert evaluation with archaeologists. The resulting framework is a possible way to facilitate pottery reconstruction from partial fragments of an original piece.
Collapse
|
6
|
Tamura Y, Demachi K, Igaki H, Okamoto H, Nakano M. A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning. Cureus 2022; 14:e22826. [PMID: 35382177 PMCID: PMC8976689 DOI: 10.7759/cureus.22826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/03/2022] [Indexed: 11/05/2022] Open
Abstract
Purpose The purpose of this study is to propose algorithms and methods for achieving high accuracy in tracking and interception irradiation technology for tumors that move by respiration using MR-linac (MRIdian®, ViewRay Inc.) and to use deep learning to predict the movement of moving tumors in real time during radiation therapy and reconstruct cine magnetic resonance imaging (cine-MRI) into four-dimensional (4D) movies. Methods In this study, we propose a reconstruction algorithm using 4DCT for treatment planning taken before irradiation as training data in consideration of the actual treatment flow. In the algorithm, two neural networks made before treatment are used to reconstruct 4D movies that predict tumor movement in real time during treatment. Cycle GAN (generative adversarial network) was used to convert MR images to CT images, and long short-term memory was used to convert cine-MRI to 4D movies and predict tumor movement. Results We succeeded in predicting the time including the imaging time of the MR images, the lag until irradiation, and the calculation time in the algorithm. In addition, the conversion and prediction results at each phase of reconstruction were generally good so that they could be clinically applied. Conclusions The reconstruction algorithm proposed in this study enables high-precision radiotherapy while predicting the volume information of the tumor and the actual tumor position, which could not be obtained during radiotherapy.
Collapse
|
7
|
Michael Gach H, Curcuru AN, Kim T, Yang D. Technical Note: Effects of rotating gantry on magnetic field and eddy currents in 0.35 T MRI-guided radiotherapy (MR-IGRT) system. Med Phys 2021; 48:7228-7235. [PMID: 34520081 DOI: 10.1002/mp.15226] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/19/2021] [Accepted: 09/04/2021] [Indexed: 01/03/2023] Open
Abstract
PURPOSE The purpose of this study was to identify the cause of severe image artifacts that occurred during gantry rotation in a 0.35 T MRI-Linac by comparing measurements of eddy currents, center frequency, and field inhomogeneities made with the gantry in motion and stationary. METHODS Gradient and B0 eddy currents were calculated from the free induction decays (FIDs) resulting from selective excitation at a temporal resolution of 200 ms/measurement. B0 eddy currents were also calculated from FIDs acquired with nonselective excitation at a temporal resolution of 100 ms/measurement. Center frequencies and B0 inhomogeneities were measured by acquiring FIDs with a repetition time (TR) of 290 ms. Cartesian and radial 2D true fast imaging with steady-state precession (TrueFISP) pulse sequences used in real-time MRI-guided radiation therapy (MR-IGRT) were acquired. To assess artifact severity, the normalized root mean square error (nRMSE) was calculated between a reference MRI (static gantry) and MRIs acquired during gantry rotation for each serial acquisition. Image artifacts were qualitatively graded as nominal, minor, or severe. Measurements were conducted while the gantry was rotated through its entire range for both clockwise and counterclockwise. Measurements during gantry rotation were compared to measurements with a stationary gantry (every 30°). RESULTS Severe image artifacts were observed 22-35% of the time while the gantry was rotating. Short time constant eddy currents were not affected by gantry rotation. The peak to peak center frequency and FWHM rose by factors of 13.2-14.5 and 1.1-1.6, respectively, for the rotating versus stationary gantry. The magnitude of the center frequency offset and field inhomogeneities depended on the direction of the gantry rotation. CONCLUSIONS Image artifacts during gantry rotation were primarily caused by center frequency variations and field inhomogeneities. Therefore, dynamic B0 compensation techniques should be able to reduce artifacts during gantry rotation.
Collapse
Affiliation(s)
- H Michael Gach
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Austen N Curcuru
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Taeho Kim
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Deshan Yang
- Departments of Radiation Oncology and Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| |
Collapse
|
8
|
de Cesare I, Zamora-Chimal CG, Postiglione L, Khazim M, Pedone E, Shannon B, Fiore G, Perrino G, Napolitano S, di Bernardo D, Savery NJ, Grierson C, di Bernardo M, Marucci L. ChipSeg: An Automatic Tool to Segment Bacterial and Mammalian Cells Cultured in Microfluidic Devices. ACS OMEGA 2021; 6:2473-2476. [PMID: 33553865 PMCID: PMC7859942 DOI: 10.1021/acsomega.0c03906] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/20/2020] [Indexed: 05/14/2023]
Abstract
Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.
Collapse
Affiliation(s)
- Irene de Cesare
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
| | - Criseida G. Zamora-Chimal
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
| | - Lorena Postiglione
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
| | - Mahmoud Khazim
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
- School
of Cellular and Molecular Medicine, University
of Bristol, University Walk, Bristol BS8 1TD, U.K.
| | - Elisa Pedone
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
- School
of Cellular and Molecular Medicine, University
of Bristol, University Walk, Bristol BS8 1TD, U.K.
| | - Barbara Shannon
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
- School
of Biochemistry, University of Bristol, University Walk, Bristol BS8 1TD, U.K.
| | - Gianfranco Fiore
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
| | - Giansimone Perrino
- Telethon
Institute of Genetic and Medicine Via Campi Flegrei 34, 80078 Pozzuoli, Italy
| | - Sara Napolitano
- Telethon
Institute of Genetic and Medicine Via Campi Flegrei 34, 80078 Pozzuoli, Italy
| | - Diego di Bernardo
- Telethon
Institute of Genetic and Medicine Via Campi Flegrei 34, 80078 Pozzuoli, Italy
- Department
of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy
| | - Nigel J. Savery
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
- School
of Biochemistry, University of Bristol, University Walk, Bristol BS8 1TD, U.K.
| | - Claire Grierson
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
- School
of Biological Sciences, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
| | - Mario di Bernardo
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
- Department
of EE and ICT, University of Naples Federico
II, Via Claudio 21, 80125 Naples, Italy
| | - Lucia Marucci
- Department
of Engineering Mathematics, University of
Bristol, Woodland Road, Bristol BS8 1UB, U.K.
- BrisSynBio,
Life Sciences Building, University of Bristol, Tyndall Avenue, Bristol BS8 1TQ, U.K.
- School
of Cellular and Molecular Medicine, University
of Bristol, University Walk, Bristol BS8 1TD, U.K.
| |
Collapse
|
9
|
Time Analysis of Online Adaptive Magnetic Resonance-Guided Radiation Therapy Workflow According to Anatomical Sites. Pract Radiat Oncol 2020; 11:e11-e21. [PMID: 32739438 DOI: 10.1016/j.prro.2020.07.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 06/26/2020] [Accepted: 07/18/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE To document time analysis of detailed workflow steps for the online adaptive magnetic resonance-guided radiation therapy treatments (MRgRT) with the ViewRay MRIdian system and to identify the barriers to and solutions for shorter treatment times. METHODS AND MATERIALS A total of 154 patients were treated with the ViewRay MRIdian system between September 2018 and October 2019. The time process of MRgRT workflow steps of 962 fractions for 166 treatment sites was analyzed in terms of patient and online adaptive treatment (ART) characteristics. RESULTS Overall, 774 of 962 fractions were treated with online ART, and 83.2% of adaptive fractions were completed in less than 60 minutes. Sixty-three percent, 50.3%, and 4.2% of fractions were completed in less than 50 minutes, 45 minutes, and 30 minutes, respectively. Eight-point-three percent and 3% of fractions were completed in more than 70 minutes and 80 minutes, respectively. The median time (tmed) for ART workflow steps were as follows: (1) setup tmed: 5.0 minutes, (2) low-resolution scanning tmed: 1 minute, (3) high-resolution scanning tmed: 3 minutes, (4) online contouring tmed: 9 minutes, (5) reoptimization with online quality assurance tmed: 5 minutes, (6) real targeting tmed: 3 minutes, (7) beam delivery with gating tmed: 17 minutes, and (8) net total treatment time tmed: 45 minutes. The shortest and longest tmean rates of net total treatment time were 41.59 minutes and 64.43 minutes for upper-lung-lobe-located thoracic tumors and ultracentrally located thoracic tumors, respectively. CONCLUSIONS To our knowledge, this is the first broad treatment-time analysis for online ART in the literature. Although treatment times are long due to human- and technology-related limitations, benefits offered by MRgRT might be clinically important. In the future, implementation of artificial intelligence segmentation, an increase in dose rate, and faster multileaf collimator and gantry speeds may lead to achieving shorter MRgRT treatments.
Collapse
|
10
|
Ginn JS, Ruan D, Low DA, Lamb JM. An image regression motion prediction technique for MRI-guided radiotherapy evaluated in single-plane cine imaging. Med Phys 2019; 47:404-413. [PMID: 31808161 DOI: 10.1002/mp.13948] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 11/29/2019] [Accepted: 11/29/2019] [Indexed: 12/31/2022] Open
Abstract
PURPOSE To develop and evaluate a novel motion prediction method for magnetic resonance image (MRI)-guided radiotherapy applications. This method, which we deem "image regression," predicts future tissue motion based on a weighted combination of previously observed motion states. Motion predictions are derived from a sliding window of recent motion states which are defined by a temporal sequence of images. A key advantage of this method compared to other motion prediction methods is that its computational complexity scales weekly with the number of spatial points predicted. Applications of gating latency reduction and improvement in deformable registration-based target tracking are demonstrated. METHODS The image regression (IR) motion prediction method was developed and evaluated using 26.9 h of real-time imaging acquired from eight healthy volunteers and 13 patients using a 0.35 T MRI-guided radiotherapy system. Motion predictions were performed 0.25-0.33 s into the future using a weighted sum of previously observed motion states with image similarity-derived weights. The set of previously observed motion states were continuously updated to incorporate the changes in breathing patterns. The accuracy of the predicted radiotherapy gating decision, beam-on positive predictive value (PPV), and predicted vs ground-truth target centroid position errors are reported. The IR technique was compared against no prediction, linear extrapolation, and an established autoregressive linear prediction algorithm. The usage of IR to initialize the deformable registration and enhance the target tracking was demonstrated in the healthy volunteer studies. Deformable registration with IR initialization was compared to the initialization performed by current clinical software: no initialization, previous image registration initialization and linear motion extrapolation initialization. RESULTS The average IR-predicted radiation beam gating decision accuracy was 95.8%, with a PPV of 95.7%, and median and 95th percentile centroid position errors of 0.63 and 2.08 mm, respectively. Compared to the autoregressive linear prediction method, gating accuracy was 1.15% greater, PPV was 1.61% greater, and median and 95th percentile centroid distances were 0.21 and 0.23 mm smaller. The IR-initialized registration on average converged within 0.50 mm of the ground-truth position in fewer than 10 iterations whereas the next best initialization method required more than 25 iterations. CONCLUSIONS Image regression motion prediction has the potential to reduce the gating latencies and improve the speed and accuracy of deformable registration-based target tracking in MRI-guided radiotherapy.
Collapse
Affiliation(s)
- John S Ginn
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Dan Ruan
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel A Low
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| |
Collapse
|
11
|
Knowles BR, Friedrich F, Fischer C, Paech D, Ladd ME. Beyond T2 and 3T: New MRI techniques for clinicians. Clin Transl Radiat Oncol 2019; 18:87-97. [PMID: 31341982 PMCID: PMC6630188 DOI: 10.1016/j.ctro.2019.04.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 04/11/2019] [Accepted: 04/11/2019] [Indexed: 12/12/2022] Open
Abstract
Technological advances in Magnetic Resonance Imaging (MRI) in terms of field strength and hybrid MR systems have led to improvements in tumor imaging in terms of anatomy and functionality. This review paper discusses the applications of such advances in the field of radiation oncology with regards to treatment planning, therapy guidance and monitoring tumor response and predicting outcome.
Collapse
Affiliation(s)
- Benjamin R. Knowles
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Florian Friedrich
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Carola Fischer
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mark E. Ladd
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| |
Collapse
|
12
|
Bertholet J, Knopf A, Eiben B, McClelland J, Grimwood A, Harris E, Menten M, Poulsen P, Nguyen DT, Keall P, Oelfke U. Real-time intrafraction motion monitoring in external beam radiotherapy. Phys Med Biol 2019; 64:15TR01. [PMID: 31226704 PMCID: PMC7655120 DOI: 10.1088/1361-6560/ab2ba8] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/10/2019] [Accepted: 06/21/2019] [Indexed: 12/25/2022]
Abstract
Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.
Collapse
Affiliation(s)
- Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
- Author to whom any correspondence should be
addressed
| | - Antje Knopf
- Department of Radiation Oncology,
University Medical Center
Groningen, University of Groningen, The
Netherlands
| | - Björn Eiben
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Jamie McClelland
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Alexander Grimwood
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Emma Harris
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Martin Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Per Poulsen
- Department of Oncology, Aarhus University Hospital, Aarhus,
Denmark
| | - Doan Trang Nguyen
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
- School of Biomedical Engineering,
University of Technology
Sydney, Sydney, Australia
| | - Paul Keall
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| |
Collapse
|
13
|
Menten MJ, Fast MF, Wetscherek A, Rank CM, Kachelrieß M, Collins DJ, Nill S, Oelfke U. The impact of 2D cine MR imaging parameters on automated tumor and organ localization for MR-guided real-time adaptive radiotherapy. Phys Med Biol 2018; 63:235005. [PMID: 30465542 PMCID: PMC6372137 DOI: 10.1088/1361-6560/aae74d] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/22/2018] [Accepted: 10/10/2018] [Indexed: 12/25/2022]
Abstract
2D cine MR imaging may be utilized to monitor rapidly moving tumors and organs-at-risk for real-time adaptive radiotherapy. This study systematically investigates the impact of geometric imaging parameters on the ability of 2D cine MR imaging to guide template-matching-driven autocontouring of lung tumors and abdominal organs. Abdominal 4D MR images were acquired of six healthy volunteers and thoracic 4D MR images were obtained of eight lung cancer patients. At each breathing phase of the images, the left kidney and gallbladder or lung tumor, respectively, were outlined as volumes of interest. These images and contours were used to create artificial 2D cine MR images, while simultaneously serving as 3D ground truth. We explored the impact of five different imaging parameters (pixel size, slice thickness, imaging plane orientation, number and relative alignment of images as well as strategies to create training images). For each possible combination of imaging parameters, we generated artificial 2D cine MR images as training and test images. A template-matching algorithm used the training images to determine the tumor or organ position in the test images. Subsequently, a 3D base contour was shifted to the determined position and compared to the ground truth via centroid distance and Dice similarity coefficient. The median centroid distance between adapted and ground truth contours was 1.56 mm for the kidney, 3.81 mm for the gallbladder and 1.03 mm for the lung tumor (median Dice similarity coefficient: 0.95, 0.72 and 0.93). We observed that a decrease in image resolution led to a modest decrease in localization accuracy, especially for the small gallbladder. However, for all volumes of interest localization accuracy varied substantially more between subjects than due to the different imaging parameters. Automated tumor and organ localization using 2D cine MR imaging and template-matching-based autocontouring is robust against variation of geometric imaging parameters. Future work and optimization efforts of 2D cine MR imaging for real-time adaptive radiotherapy is needed to characterize the influence of sequence- and anatomical site-specific imaging contrast.
Collapse
Affiliation(s)
- Martin J Menten
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Martin F Fast
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Andreas Wetscherek
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Christopher M Rank
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marc Kachelrieß
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David J Collins
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Simeon Nill
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Uwe Oelfke
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
14
|
Zhang J, Markova S, Garcia A, Huang K, Nie X, Choi W, Lu W, Wu A, Rimner A, Li G. Evaluation of automatic contour propagation in T2-weighted 4DMRI for normal-tissue motion assessment using internal organ-at-risk volume (IRV). J Appl Clin Med Phys 2018; 19:598-608. [PMID: 30112797 PMCID: PMC6123161 DOI: 10.1002/acm2.12431] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/19/2018] [Accepted: 07/01/2018] [Indexed: 12/25/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion. Methods and Materials T2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm. Results Visual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI. Conclusion Automatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.
Collapse
Affiliation(s)
- Jingjing Zhang
- Department of Radiation Oncology, Zhongshan Hospital of Sun Yat-Sen University, Zhongshan, China.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Svetlana Markova
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alejandro Garcia
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kirk Huang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Xingyu Nie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wookjin Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Abraham Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
15
|
Kashani R, Olsen JR. Magnetic Resonance Imaging for Target Delineation and Daily Treatment Modification. Semin Radiat Oncol 2018; 28:178-184. [DOI: 10.1016/j.semradonc.2018.02.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|
16
|
Chen T, Zhang M, Jabbour S, Wang H, Barbee D, Das IJ, Yue N. Principal component analysis-based imaging angle determination for 3D motion monitoring using single-slice on-board imaging. Med Phys 2018; 45:2377-2387. [DOI: 10.1002/mp.12904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 03/13/2018] [Accepted: 03/22/2018] [Indexed: 01/07/2023] Open
Affiliation(s)
- Ting Chen
- Department of Radiation Oncology; Laura and Isaac Perlmutter Cancer Center New York University Langone Health; New York NY 10016 USA
- Department of Radiation Oncology; Rutgers Cancer Institute of New Jersey; New Brunswick NJ 08901 USA
| | - Miao Zhang
- Department of Radiation Oncology; Rutgers Cancer Institute of New Jersey; New Brunswick NJ 08901 USA
- Department of Medical Physics; Memorial Sloan Kettering Cancer Center; New York NY 10065 USA
| | - Salma Jabbour
- Department of Radiation Oncology; Rutgers Cancer Institute of New Jersey; New Brunswick NJ 08901 USA
| | - Hesheng Wang
- Department of Radiation Oncology; Laura and Isaac Perlmutter Cancer Center New York University Langone Health; New York NY 10016 USA
| | - David Barbee
- Department of Radiation Oncology; Laura and Isaac Perlmutter Cancer Center New York University Langone Health; New York NY 10016 USA
| | - Indra J. Das
- Department of Radiation Oncology; Laura and Isaac Perlmutter Cancer Center New York University Langone Health; New York NY 10016 USA
| | - Ning Yue
- Department of Radiation Oncology; Rutgers Cancer Institute of New Jersey; New Brunswick NJ 08901 USA
| |
Collapse
|
17
|
Pathmanathan AU, van As NJ, Kerkmeijer LGW, Christodouleas J, Lawton CAF, Vesprini D, van der Heide UA, Frank SJ, Nill S, Oelfke U, van Herk M, Li XA, Mittauer K, Ritter M, Choudhury A, Tree AC. Magnetic Resonance Imaging-Guided Adaptive Radiation Therapy: A "Game Changer" for Prostate Treatment? Int J Radiat Oncol Biol Phys 2018; 100:361-373. [PMID: 29353654 DOI: 10.1016/j.ijrobp.2017.10.020] [Citation(s) in RCA: 118] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/09/2017] [Accepted: 10/12/2017] [Indexed: 01/25/2023]
Abstract
Radiation therapy to the prostate involves increasingly sophisticated delivery techniques and changing fractionation schedules. With a low estimated α/β ratio, a larger dose per fraction would be beneficial, with moderate fractionation schedules rapidly becoming a standard of care. The integration of a magnetic resonance imaging (MRI) scanner and linear accelerator allows for accurate soft tissue tracking with the capacity to replan for the anatomy of the day. Extreme hypofractionation schedules become a possibility using the potentially automated steps of autosegmentation, MRI-only workflow, and real-time adaptive planning. The present report reviews the steps involved in hypofractionated adaptive MRI-guided prostate radiation therapy and addresses the challenges for implementation.
Collapse
Affiliation(s)
- Angela U Pathmanathan
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | - Nicholas J van As
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | | | | | | | - Danny Vesprini
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Steven J Frank
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Simeon Nill
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | - Uwe Oelfke
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| | - Marcel van Herk
- Manchester Cancer Research Centre, University of Manchester, Manchester Academic Health Science Centre, The Christie National Health Service Foundation Trust, Manchester, United Kingdom; National Institute of Health Research, Manchester Biomedical Research Centre, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - X Allen Li
- Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kathryn Mittauer
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Mark Ritter
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Ananya Choudhury
- Manchester Cancer Research Centre, University of Manchester, Manchester Academic Health Science Centre, The Christie National Health Service Foundation Trust, Manchester, United Kingdom; National Institute of Health Research, Manchester Biomedical Research Centre, Central Manchester University Hospitals National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
| | - Alison C Tree
- The Institute of Cancer Research, London, United Kingdom; The Royal Marsden National Health Service Foundation Trust, London, United Kingdom
| |
Collapse
|
18
|
Hann A, Bettac L, Haenle MM, Graeter T, Berger AW, Dreyhaupt J, Schmalstieg D, Zoller WG, Egger J. Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound. Sci Rep 2017; 7:12779. [PMID: 28986569 PMCID: PMC5630585 DOI: 10.1038/s41598-017-12925-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 09/20/2017] [Indexed: 12/19/2022] Open
Abstract
Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assistance in this process are often assessed using a small number of lesions performed by examiners who already know the algorithm. In this work, we present the application of an algorithm for the segmentation of liver metastases due to pancreatic cancer using a set of 105 different images of metastases. The algorithm and the two examiners had never assessed the images before. The examiners first performed a manual segmentation and, after five weeks, a semiautomatic segmentation using the algorithm. They were satisfied in up to 90% of the cases with the semiautomatic segmentation results. Using the algorithm was significantly faster and resulted in a median Dice similarity score of over 80%. Estimation of the inter-operator variability by using the intra class correlation coefficient was good with 0.8. In conclusion, the algorithm facilitates fast and accurate segmentation of liver metastases, comparable to the current gold standard of manual segmentation.
Collapse
Affiliation(s)
- Alexander Hann
- Department of Internal Medicine I, Ulm University, Ulm, Germany. .,Department of Internal Medicine and Gastroenterology, Katharinenhospital, Kriegsbergstraße 60, 70174, Stuttgart, Germany.
| | - Lucas Bettac
- Department of Internal Medicine I, Ulm University, Ulm, Germany
| | - Mark M Haenle
- Department of Internal Medicine I, Ulm University, Ulm, Germany
| | - Tilmann Graeter
- Department of Diagnostic and Interventional Radiology, Ulm University, Ulm, Germany
| | | | - Jens Dreyhaupt
- Institute of Epidemiology & Medical Biometry, Ulm University, Ulm, Germany
| | - Dieter Schmalstieg
- Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria
| | - Wolfram G Zoller
- Department of Internal Medicine and Gastroenterology, Katharinenhospital, Kriegsbergstraße 60, 70174, Stuttgart, Germany
| | - Jan Egger
- Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria.,BioTechMed, Krenngasse 37/1, 8010, Graz, Austria
| |
Collapse
|
19
|
Ono T, Nakamura M, Hirose Y, Kitsuda K, Ono Y, Ishigaki T, Hiraoka M. Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis. J Appl Clin Med Phys 2017; 18:36-42. [PMID: 28661100 PMCID: PMC7663969 DOI: 10.1002/acm2.12121] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 05/18/2017] [Accepted: 05/19/2017] [Indexed: 12/25/2022] Open
Abstract
To estimate the lung tumor position from multiple anatomical features on four‐dimensional computed tomography (4D‐CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D‐CT scanning. The three‐dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D‐CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root‐mean‐square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D‐CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV.
Collapse
Affiliation(s)
- Tomohiro Ono
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Kenji Kitsuda
- Division of Radiology, Osaka Red Cross Hospital, Osaka, Japan
| | - Yuka Ono
- Department of Radiation Oncology, Osaka Red Cross Hospital, Osaka, Japan
| | - Takashi Ishigaki
- Department of Radiation Oncology, Osaka Red Cross Hospital, Osaka, Japan
| | - Masahiro Hiraoka
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| |
Collapse
|
20
|
Feng Y, Dong F, Xia X, Hu CH, Fan Q, Hu Y, Gao M, Mutic S. An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images. Med Phys 2017; 44:3752-3760. [PMID: 28513858 DOI: 10.1002/mp.12350] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 04/24/2017] [Accepted: 05/10/2017] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. METHODS To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. RESULTS Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. CONCLUSION Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures.
Collapse
Affiliation(s)
- Yuan Feng
- Center for Molecular Imaging and Nuclear Medicine, School of Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, Jiangsu, 215123, China.,School of Mechanical and Electronic Engineering, Soochow University, Suzhou, Jiangsu, 215021, China.,School of Computer Science and Engineering, Soochow University, Suzhou, Jiangsu, 215021, China
| | - Fenglin Dong
- Department of Ultrasounds, the First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, China
| | - Xiaolong Xia
- Center for Molecular Imaging and Nuclear Medicine, School of Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, Jiangsu, 215123, China
| | - Chun-Hong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, China
| | - Qianmin Fan
- Department of Ultrasounds, the First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, China
| | - Yanle Hu
- Department of Radiation Oncology, Mayo Clinic in Arizona, Phoenix, AZ, USA
| | - Mingyuan Gao
- Center for Molecular Imaging and Nuclear Medicine, School of Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, Jiangsu, 215123, China
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University, St. Louis, MO, USA
| |
Collapse
|
21
|
Raudaschl PF, Zaffino P, Sharp GC, Spadea MF, Chen A, Dawant BM, Albrecht T, Gass T, Langguth C, Lüthi M, Jung F, Knapp O, Wesarg S, Mannion-Haworth R, Bowes M, Ashman A, Guillard G, Brett A, Vincent G, Orbes-Arteaga M, Cárdenas-Peña D, Castellanos-Dominguez G, Aghdasi N, Li Y, Berens A, Moe K, Hannaford B, Schubert R, Fritscher KD. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015. Med Phys 2017; 44:2020-2036. [PMID: 28273355 DOI: 10.1002/mp.12197] [Citation(s) in RCA: 136] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 10/13/2016] [Accepted: 02/22/2017] [Indexed: 01/28/2023] Open
Abstract
PURPOSE Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. METHODS In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. RESULTS This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. CONCLUSIONS The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.
Collapse
Affiliation(s)
- Patrik F Raudaschl
- Department of Biomedical Computer Science and Mechatronics, Institute for Biomedical Image Analysis, UMIT, Hall, Tyrol, 6060, Austria
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, 88100, Italy
| | - Gregory C Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, 88100, Italy
| | - Antong Chen
- Merck and Co., Inc., West Point, PA, 19422, USA
| | - Benoit M Dawant
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA
| | | | - Tobias Gass
- Varian Medical Systems, Baden, 5404, Switzerland
| | | | | | | | | | | | | | - Mike Bowes
- Imorphics Ltd., Kilburn House, Manchester Science Park, Manchester, M15 6SE, UK
| | - Annaliese Ashman
- Imorphics Ltd., Kilburn House, Manchester Science Park, Manchester, M15 6SE, UK
| | - Gwenael Guillard
- Imorphics Ltd., Kilburn House, Manchester Science Park, Manchester, M15 6SE, UK
| | - Alan Brett
- Imorphics Ltd., Kilburn House, Manchester Science Park, Manchester, M15 6SE, UK
| | - Graham Vincent
- Imorphics Ltd., Kilburn House, Manchester Science Park, Manchester, M15 6SE, UK
| | | | - David Cárdenas-Peña
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Colombia
| | | | - Nava Aghdasi
- University of Washington, Seattle, WA, 98105, USA
| | - Yangming Li
- University of Washington, Seattle, WA, 98105, USA
| | | | - Kris Moe
- University of Washington, Seattle, WA, 98105, USA
| | | | - Rainer Schubert
- Department of Biomedical Computer Science and Mechatronics, Institute for Biomedical Image Analysis, UMIT, Hall, Tyrol, 6060, Austria
| | - Karl D Fritscher
- Department of Biomedical Computer Science and Mechatronics, Institute for Biomedical Image Analysis, UMIT, Hall, Tyrol, 6060, Austria
| |
Collapse
|