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Gogola A, Cohen AD, Snitz B, Minhas D, Tudorascu D, Ikonomovic MD, Shaaban CE, Doré V, Matan C, Bourgeat P, Mason NS, Leuzy A, Aizenstein H, Mathis CA, Lopez OL, Lopresti BJ, Villemagne VL. Implementation and Assessment of Tau Thresholds in Non-Demented Individuals as Predictors of Cognitive Decline in Tau Imaging Studies. J Alzheimers Dis 2024; 100:S75-S92. [PMID: 39121123 DOI: 10.3233/jad-240543] [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] [Indexed: 08/11/2024]
Abstract
Background Tau accumulation in Alzheimer's disease is associated with short term clinical progression and faster rates of cognitive decline in individuals with high amyloid-β deposition. Defining an optimal threshold of tau accumulation predictive of cognitive decline remains a challenge. Objective We tested the ability of regional tau PET sensitivity and specificity thresholds to predict longitudinal cognitive decline. We also tested the predictive performance of thresholds in the proposed new NIA-AA biological staging for Alzheimer's disease where multiple levels of tau positivity are used to stage participants. Methods 18F-flortaucipir scans from 301 non-demented participants were processed and sampled. Four cognitive measures were assessed longitudinally. Regional standardized uptake value ratios were split into infra- and suprathreshold groups at baseline using previously derived thresholds. Survival analysis, log rank testing, and Generalized Estimation Equations assessed the relationship between the application of regional sensitivity/specificity thresholds and change in cognitive measures as well as tau threshold performance in predicting cognitive decline within the new NIA-AA biological staging. Results The meta temporal region was best for predicting risk of short-term cognitive decline in suprathreshold, as compared to infrathreshold participants. When applying multiple levels of tau positivity, each subsequent level of tau identified cognitive decline at earlier timepoints. Conclusions When using 18F-flortaucipir, meta temporal suprathreshold classification was associated with increased risk of cognitive decline, suggesting that abnormal tau deposition in the cortex predicts decline. Likewise, the application of multiple levels of tau clearly predicts the distinctive cognitive trajectories in the new NIA-AA biological staging framework.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beth Snitz
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Milos D Ikonomovic
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - C Elizabeth Shaaban
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
- Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Melbourne, VIC, Australia
| | - Cristy Matan
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Pierrick Bourgeat
- Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Melbourne, VIC, Australia
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Antoine Leuzy
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, AZ, USA
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Oscar L Lopez
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
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2
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Bugeja JM, Mehawed G, Roberts MJ, Rukin N, Dowling J, Murray R. Prostate volume analysis in image registration for prostate cancer care: a verification study. Phys Eng Sci Med 2023; 46:1791-1802. [PMID: 37819450 DOI: 10.1007/s13246-023-01342-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/26/2023] [Indexed: 10/13/2023]
Abstract
Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.
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Affiliation(s)
- Jessica M Bugeja
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Herston, Australia.
| | - Georges Mehawed
- Herston Biofabrication Institute, Urology Program, Herston, Australia
- Urology Department, Redcliffe Hospital, Redcliffe, Australia
- School of Medicine, The University of Queensland, Brisbane, Australia
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Matthew J Roberts
- Herston Biofabrication Institute, Urology Program, Herston, Australia
- Urology Department, Redcliffe Hospital, Redcliffe, Australia
- School of Medicine, The University of Queensland, Brisbane, Australia
- Urology Department, Royal Brisbane and Women's Hospital, Herston, Australia
- University of Queensland, University of Queensland Centre for Clinical Research, Herston, Australia
| | - Nicholas Rukin
- Herston Biofabrication Institute, Urology Program, Herston, Australia
- Urology Department, Redcliffe Hospital, Redcliffe, Australia
- School of Medicine, The University of Queensland, Brisbane, Australia
| | - Jason Dowling
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Herston, Australia
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Rebecca Murray
- Herston Biofabrication Institute, Urology Program, Herston, Australia
- Urology Department, Redcliffe Hospital, Redcliffe, Australia
- Australian Institute of Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
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3
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Gogola A, Lopresti BJ, Tudorascu D, Snitz B, Minhas D, Doré V, Ikonomovic MD, Shaaban CE, Matan C, Bourgeat P, Mason NS, Aizenstein H, Mathis CA, Klunk WE, Rowe CC, Lopez OL, Cohen AD, Villemagne VL. Biostatistical Estimation of Tau Threshold Hallmarks (BETTH) Algorithm for Human Tau PET Imaging Studies. J Nucl Med 2023; 64:1798-1805. [PMID: 37709531 PMCID: PMC10626371 DOI: 10.2967/jnumed.123.265941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/03/2023] [Indexed: 09/16/2023] Open
Abstract
A methodology for determining tau PET thresholds is needed to confidently detect early tau deposition. We compared multiple threshold-determining methods in participants who underwent either 18F-flortaucipir or 18F-MK-6240 PET scans. Methods: 18F-flortaucipir (n = 798) and 18F-MK-6240 (n = 216) scans were processed and sampled to obtain regional SUV ratios. Subsamples of the cohorts were based on participant diagnosis, age, amyloid-β status (positive or negative), and neurodegeneration status (positive or negative), creating older-adult (age ≥ 55 y) cognitively unimpaired (amyloid-β-negative, neurodegeneration-negative) and cognitively impaired (mild cognitive impairment/Alzheimer disease, amyloid-β-positive, neurodegeneration-positive) groups, and then were further subsampled via matching to reduce significant differences in diagnostic prevalence, age, and Mini-Mental State Examination score. We used the biostatistical estimation of tau threshold hallmarks (BETTH) algorithm to determine sensitivity and specificity in 6 composite regions. Results: Parametric double receiver operating characteristic analysis yielded the greatest joint sensitivity in 5 of the 6 regions, whereas hierarchic clustering, gaussian mixture modeling, and k-means clustering all yielded perfect joint specificity (2.00) in all regions. Conclusion: When 18F-flortaucipir and 18F-MK-6240 are used, Alzheimer disease-related tau status is best assessed using 2 thresholds, a sensitivity one based on parametric double receiver operating characteristic analysis and a specificity one based on gaussian mixture modeling, delimiting an uncertainty zone indicating participants who may require further evaluation.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania;
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Beth Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Vincent Doré
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Geriatric Research Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; and
| | - C Elizabeth Shaaban
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Cristy Matan
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Pierrick Bourgeat
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
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4
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Zhao Y, Haworth A, Rowshanfarzad P, Ebert MA. Focal Boost in Prostate Cancer Radiotherapy: A Review of Planning Studies and Clinical Trials. Cancers (Basel) 2023; 15:4888. [PMID: 37835581 PMCID: PMC10572027 DOI: 10.3390/cancers15194888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/28/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Focal boost radiotherapy was developed to deliver elevated doses to functional sub-volumes within a target. Such a technique was hypothesized to improve treatment outcomes without increasing toxicity in prostate cancer treatment. PURPOSE To summarize and evaluate the efficacy and variability of focal boost radiotherapy by reviewing focal boost planning studies and clinical trials that have been published in the last ten years. METHODS Published reports of focal boost radiotherapy, that specifically incorporate dose escalation to intra-prostatic lesions (IPLs), were reviewed and summarized. Correlations between acute/late ≥G2 genitourinary (GU) or gastrointestinal (GI) toxicity and clinical factors were determined by a meta-analysis. RESULTS By reviewing and summarizing 34 planning studies and 35 trials, a significant dose escalation to the GTV and thus higher tumor control of focal boost radiotherapy were reported consistently by all reviewed studies. Reviewed trials reported a not significant difference in toxicity between focal boost and conventional radiotherapy. Acute ≥G2 GU and late ≥G2 GI toxicities were reported the most and least prevalent, respectively, and a negative correlation was found between the rate of toxicity and proportion of low-risk or intermediate-risk patients in the cohort. CONCLUSION Focal boost prostate cancer radiotherapy has the potential to be a new standard of care.
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Affiliation(s)
- Yutong Zhao
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA 6009, Australia; (P.R.); (M.A.E.)
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, The University of Sydney, Camperdown, NSW 2050, Australia;
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA 6009, Australia; (P.R.); (M.A.E.)
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA 6000, Australia
| | - Martin A. Ebert
- School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA 6009, Australia; (P.R.); (M.A.E.)
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia
- 5D Clinics, Claremont, WA 6010, Australia
- School of Medicine and Population Health, University of Wisconsin, Madison WI 53706, USA
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Kim H, Kang SW, Kim JH, Nagar H, Sabuncu M, Margolis DJA, Kim CK. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. Eur J Radiol 2023; 165:110887. [PMID: 37245342 DOI: 10.1016/j.ejrad.2023.110887] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 05/30/2023]
Abstract
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.
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Affiliation(s)
- Heejong Kim
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Shin Won Kang
- Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021, United States
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Daniel J A Margolis
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
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White matter microstructure and receptive vocabulary in children with cerebral palsy: The role of interhemispheric connectivity. PLoS One 2023; 18:e0280055. [PMID: 36649231 PMCID: PMC9844879 DOI: 10.1371/journal.pone.0280055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Communication and cognitive impairments are common impediments to participation and social functioning in children with cerebral palsy (CP). Bilateral language networks underlie the function of some high-level language-related cognitive functions. PURPOSE To explore the association between receptive vocabulary and white-matter microstructure in the temporal lobes and the central part of the temporo-temporal bundles in children with CP. MATERIALS AND METHODS 37 children with spastic motor type CP (mean age 9.6 years, 25 male) underwent a receptive vocabulary test (Peabody Picture Vocabulary Test, PPVT-IV) and 3T MRI. Mean fractional anisotropy (FA) and mean diffusivity (MD) were calculated for the temporal lobes and the interhemispheric bundles traversing the splenium of the corpus callosum and the anterior commissure. Associations between microstructure and receptive vocabulary function were explored using univariable linear regression. RESULTS PPVT-IV scores were significantly associated with mean white matter MD in the left temporal lobe, but not the right temporal lobe. There was no association between PPVT-IV and mean white matter FA in the temporal lobes. PPVT-IV scores were not significantly associated with the laterality of these diffusion tensor metrics. Within the corpus callosum, FA, but not MD of the temporo-temporal bundles was significantly associated with the PPVT-IV scores. Within the anterior commissure no equivalent relationship between diffusion metrics and PPVT-IV was found. CONCLUSION Our findings add further understanding to the pathophysiological basis underlying receptive vocabulary skills in children with CP that could extend to other patients with early brain damage. This study highlights the importance of interhemispheric connections for receptive vocabulary.
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A high-performance method of deep learning for prostate MR-only radiotherapy planning using an optimized Pix2Pix architecture. Phys Med 2022; 103:108-118. [DOI: 10.1016/j.ejmp.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 07/25/2022] [Accepted: 10/07/2022] [Indexed: 11/20/2022] Open
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Chourak H, Barateau A, Tahri S, Cadin C, Lafond C, Nunes JC, Boue-Rafle A, Perazzi M, Greer PB, Dowling J, de Crevoisier R, Acosta O. Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods. Front Oncol 2022; 12:968689. [PMID: 36300084 PMCID: PMC9589295 DOI: 10.3389/fonc.2022.968689] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.
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Affiliation(s)
- Hilda Chourak
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
- The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health and Biosecurity, Brisbane, QLD, Australia
- *Correspondence: Hilda Chourak, ; Jason Dowling,
| | - Anaïs Barateau
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Safaa Tahri
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Capucine Cadin
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Caroline Lafond
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Jean-Claude Nunes
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Adrien Boue-Rafle
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Mathias Perazzi
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Peter B. Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia
- Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, NSW, Australia
| | - Jason Dowling
- The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health and Biosecurity, Brisbane, QLD, Australia
- *Correspondence: Hilda Chourak, ; Jason Dowling,
| | - Renaud de Crevoisier
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Oscar Acosta
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
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Han R, Jones CK, Lee J, Zhang X, Wu P, Vagdargi P, Uneri A, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance. Phys Med Biol 2022; 67:10.1088/1361-6560/ac72ef. [PMID: 35609586 PMCID: PMC9801422 DOI: 10.1088/1361-6560/ac72ef] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/24/2022] [Indexed: 01/03/2023]
Abstract
Objective.The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.Approach.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods).Main results.JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods-e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm.Significance.The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.
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Affiliation(s)
- R Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - C K Jones
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America
| | - J Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - X Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P A Helm
- Medtronic Inc., Littleton, MA, United States of America
| | - M Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| | - W S Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
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10
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Li Z, Huang X, Zhang Z, Liu L, Wang F, Li S, Gao S, Xia J. Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function. Quant Imaging Med Surg 2022; 12:3151-3169. [PMID: 35655819 PMCID: PMC9131350 DOI: 10.21037/qims-21-846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 02/23/2022] [Indexed: 12/26/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) images synthesized from computed tomography (CT) data can provide more detailed information on pathological structures than that of CT data alone; thus, the synthesis of MRI has received increased attention especially in medical scenarios where only CT images are available. A novel convolutional neural network (CNN) combined with a contextual loss function was proposed for synthesis of T1- and T2-weighted images (T1WI and T2WI) from CT data. METHODS A total of 5,053 and 5,081 slices of T1WI and T2WI, respectively were selected for the dataset of CT and MRI image pairs. Affine registration, image denoising, and contrast enhancement were done on the aforementioned multi-modality medical image dataset comprising T1WI, T2WI, and CT images of the brain. A deep CNN was then proposed by modifying the ResNet structure to constitute the encoder and decoder of U-Net, called double ResNet-U-Net (DRUNet). Three different loss functions were utilized to optimize the parameters of the proposed models: mean squared error (MSE) loss, binary crossentropy (BCE) loss, and contextual loss. Statistical analysis of the independent-sample t-test was conducted by comparing DRUNets with different loss functions and different network layers. RESULTS DRUNet-101 with contextual loss yielded higher values of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Tenengrad function (i.e., 34.25±2.06, 0.97±0.03, and 17.03±2.75 for T1WI and 33.50±1.08, 0.98±0.05, and 19.76±3.54 for T2WI respectively). The results were statistically significant at P<0.001 with a narrow confidence interval of difference, indicating the superiority of DRUNet-101 with contextual loss. In addition, both image zooming and difference maps presented for the final synthetic MR images visually reflected the robustness of DRUNet-101 with contextual loss. The visualization of convolution filters and feature maps showed that the proposed model can generate synthetic MR images with high-frequency information. CONCLUSIONS The results demonstrated that DRUNet-101 with contextual loss function provided better high-frequency information in synthetic MR images compared with the other two functions. The proposed DRUNet model has a distinct advantage over previous models in terms of PSNR, SSIM, and Tenengrad score. Overall, DRUNet-101 with contextual loss is recommended for synthesizing MR images from CT scans.
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Affiliation(s)
- Zhaotong Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Institute of Medical Humanities, Peking University, Beijing, China
| | - Xinrui Huang
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Zeru Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Institute of Medical Humanities, Peking University, Beijing, China
| | - Liangyou Liu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Institute of Medical Humanities, Peking University, Beijing, China
| | - Fei Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital & Institute, Beijing, China
| | - Sha Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital & Institute, Beijing, China
| | - Song Gao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Jun Xia
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen, China
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11
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Fully Automatic Registration Methods for Chest X-Ray Images. J Med Biol Eng 2021; 41:826-843. [PMID: 34744547 PMCID: PMC8563362 DOI: 10.1007/s40846-021-00666-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 10/14/2021] [Indexed: 11/29/2022]
Abstract
Purpose Image registration is important in medical applications accomplished by improving healthcare technology in recent years. Various studies have been proposed in medical applications, including clinical track of events and updating the treatment plan for radiotherapy and surgery. This study presents a fully automatic registration system for chest X-ray images to generate fusion results for difference analysis. Using the accurate alignment of the proposed system, the fusion result indicates the differences in the thoracic area during the treatment process. Methods The proposed method consists of a data normalization method, a hybrid L-SVM model to detect lungs, ribs and clavicles for object recognition, a landmark matching algorithm, two-stage transformation approaches and a fusion method for difference analysis to highlight the differences in the thoracic area. In evaluation, a preliminary test was performed to compare three transformation models, with a full evaluation process to compare the proposed method with two existing elastic registration methods. Results The results show that the proposed method produces significantly better results than two benchmark methods (P-value \documentclass[12pt]{minimal}
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\begin{document}$$\le$$\end{document}≤ 0.001). The proposed system achieves the lowest mean registration error distance (MRED) (8.99 mm, 23.55 pixel) and the lowest mean registration error ratio (MRER) w.r.t. the length of image diagonal (1.61%) compared to the two benchmark approaches with MRED (15.64 mm, 40.97 pixel) and (180.5 mm, 472.69 pixel) and MRER (2.81%) and (32.51%), respectively. Conclusions The experimental results show that the proposed method is capable of accurately aligning the chest X-ray images acquired at different times, assisting doctors to trace individual health status, evaluate treatment effectiveness and monitor patient recovery progress for thoracic diseases.
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12
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Chen J, Yuan F, Shen Y, Wang J. Multimodality-based knee joint modelling method with bone and cartilage structures for total knee arthroplasty. Int J Med Robot 2021; 17:e2316. [PMID: 34312966 DOI: 10.1002/rcs.2316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/19/2021] [Accepted: 07/22/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We propose a robust and accurate knee joint modelling method with bone and cartilage structures to enable accurate surgical guidance for knee surgery. METHODS A multimodality registration strategy is proposed to fuse magnetic resonance (MR) and computed tomography (CT) images of the femur and tibia separately to remove spatial inconsistency caused by knee bending in CT/MR scans. Automatic segmentation of the femur, tibia and cartilages is carried out with region of interest clustering and intensity analysis based on the multimodal fusion of images. RESULTS Experimental results show that the registration error is 1.13 ± 0.30 mm. The Dice similarity coefficient values of the proposed segmentation method of the femur, tibia, femoral and tibial cartilages are 0.969, 0.966, 0.910 and 0.872, respectively. CONCLUSIONS This study demonstrates the feasibility and effectiveness of multimodality-based registration and segmentation methods for knee joint modelling. The proposed method can provide users with 3D anatomical models of the femur, tibia, and cartilages with few human inputs.
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Affiliation(s)
- Jiahe Chen
- School of Mechanical Engineering and Automation, Beihang University, Beijing, China
| | - Fuzhen Yuan
- Knee Surgery Department of the Institute of Sports Medicine, Peking University Third Hospital, Beijing, China
| | - Yu Shen
- School of Mechanical Engineering and Automation, Beihang University, Beijing, China
| | - Junchen Wang
- School of Mechanical Engineering and Automation, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
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13
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Polfliet M, Hendriks MS, Guyader JM, Ten Hove I, Mast H, Vandemeulebroucke J, van der Lugt A, Wolvius EB, Klein S. Registration of magnetic resonance and computed tomography images in patients with oral squamous cell carcinoma for three-dimensional virtual planning of mandibular resection and reconstruction. Int J Oral Maxillofac Surg 2021; 50:1386-1393. [PMID: 33551174 DOI: 10.1016/j.ijom.2021.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/29/2020] [Accepted: 01/04/2021] [Indexed: 12/26/2022]
Abstract
The aim of this study was to evaluate and present an automated method for registration of magnetic resonance imaging (MRI) and computed tomography (CT) or cone beam CT (CBCT) images of the mandibular region for patients with oral squamous cell carcinoma (OSCC). Registered MRI and (CB)CT could facilitate the three-dimensional virtual planning of surgical guides employed for resection and reconstruction in patients with OSCC with mandibular invasion. MRI and (CB)CT images were collected retrospectively from 19 patients. MRI images were aligned with (CB)CT images employing a rigid registration approach (stage 1), a rigid registration approach using a mandibular mask (stage 2), and two non-rigid registration approaches (stage 3). Registration accuracy was quantified by the mean target registration error (mTRE), calculated over a set of landmarks annotated by two observers. Stage 2 achieved the best registration result, with an mTRE of 2.5±0.7mm, which was comparable to the inter- and intra-observer variabilities of landmark placement in MRI. Stage 2 was significantly better aligned compared to all approaches in stage 3. In conclusion, this study demonstrated that rigid registration with the use of a mask is an appropriate image registration method for aligning MRI and (CB)CT images of the mandibular region in patients with OSCC.
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Affiliation(s)
- M Polfliet
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium; imec, Leuven, Belgium; Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M S Hendriks
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - J-M Guyader
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands; LabISEN - Yncréa Ouest, Brest, France
| | - I Ten Hove
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - H Mast
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - J Vandemeulebroucke
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium; imec, Leuven, Belgium
| | - A van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - E B Wolvius
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - S Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
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14
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Qu Y, Li X, Yan Z, Zhao L, Zhang L, Liu C, Xie S, Li K, Metaxas D, Wu W, Hao Y, Dai K, Zhang S, Tao X, Ai S. Surgical planning of pelvic tumor using multi-view CNN with relation-context representation learning. Med Image Anal 2021; 69:101954. [PMID: 33550006 DOI: 10.1016/j.media.2020.101954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 11/21/2020] [Accepted: 12/28/2020] [Indexed: 12/20/2022]
Abstract
Limb salvage surgery of malignant pelvic tumors is the most challenging procedure in musculoskeletal oncology due to the complex anatomy of the pelvic bones and soft tissues. It is crucial to accurately resect the pelvic tumors with appropriate margins in this procedure. However, there is still a lack of efficient and repetitive image planning methods for tumor identification and segmentation in many hospitals. In this paper, we present a novel deep learning-based method to accurately segment pelvic bone tumors in MRI. Our method uses a multi-view fusion network to extract pseudo-3D information from two scans in different directions and improves the feature representation by learning a relational context. In this way, it can fully utilize spatial information in thick MRI scans and reduce over-fitting when learning from a small dataset. Our proposed method was evaluated on two independent datasets collected from 90 and 15 patients, respectively. The segmentation accuracy of our method was superior to several comparing methods and comparable to the expert annotation, while the average time consumed decreased about 100 times from 1820.3 seconds to 19.2 seconds. In addition, we incorporate our method into an efficient workflow to improve the surgical planning process. Our workflow took only 15 minutes to complete surgical planning in a phantom study, which is a dramatic acceleration compared with the 2-day time span in a traditional workflow.
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Affiliation(s)
- Yang Qu
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Xiaomin Li
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Zhennan Yan
- SenseBrain Technology, Princeton, NJ 08540, USA
| | - Liang Zhao
- SenseTime Research, Shanghai 200233, China
| | - Lichi Zhang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030 China
| | - Chang Liu
- SenseTime Research, Shanghai 200233, China
| | | | - Kang Li
- Department of Orthopaedics, Rutgers New Jersey Medical School, Newark, NJ 07103, USA
| | - Dimitris Metaxas
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA
| | - Wen Wu
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Yongqiang Hao
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Kerong Dai
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education, Shanghai 200240, China
| | - Shaoting Zhang
- SenseTime Research, Shanghai 200233, China; Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
| | - Songtao Ai
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
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15
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Fu Y, Wang T, Lei Y, Patel P, Jani AB, Curran WJ, Liu T, Yang X. Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks. Med Phys 2020; 48:253-263. [PMID: 33164219 DOI: 10.1002/mp.14584] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 10/23/2020] [Accepted: 11/02/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and dose monitoring in prostate radiotherapy. METHODS AND MATERIALS We proposed a novel registration framework which considers biomechanical constraint when deforming the MR to CBCT. The registration framework consists of two segmentation convolutional neural networks (CNN) for MR and CBCT prostate segmentation, and a three-dimensional (3D) point cloud (PC) matching network. Image intensity-based rigid registration was first performed to initialize the alignment between MR and CBCT prostate. The aligned prostates were then meshed into tetrahedron elements to generate volumetric PC representation of the prostate shapes. The 3D PC matching network was developed to predict a PC motion vector field which can deform the MRI prostate PC to match the CBCT prostate PC. To regularize the network's motion prediction with biomechanical constraints, finite element (FE) modeling-generated motion fields were used to train the network. MRI and CBCT images of 50 patients with intraprostatic fiducial markers were used in this study. Registration results were evaluated using three metrics including dice similarity coefficient (DSC), mean surface distance (MSD), and target registration error (TRE). In addition to spatial registration accuracy, Jacobian determinant and strain tensors were calculated to assess the physical fidelity of the deformation field. RESULTS The mean and standard deviation of our method were 0.93 ± 0.01, 1.66 ± 0.10 mm, and 2.68 ± 1.91 mm for DSC, MSD, and TRE, respectively. The mean TRE of the proposed method was reduced by 29.1%, 14.3%, and 11.6% as compared to image intensity-based rigid registration, coherent point drifting (CPD) nonrigid surface registration, and modality-independent neighborhood descriptor (MIND) registration, respectively. CONCLUSION We developed a new framework to accurately register the prostate on MRI to CBCT images for external beam radiotherapy. The proposed method could be used to aid DIL delineation on CBCT, treatment planning, dose escalation to DIL, and dose monitoring.
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Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Tonghe Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA.,Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Yang Lei
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Pretesh Patel
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA.,Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Ashesh B Jani
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA.,Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA.,Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA.,Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA.,Winship Cancer Institute, Emory University, Atlanta, GA, USA
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16
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Min H, McClymont D, Chandra SS, Crozier S, Bradley AP. Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI. Biomed Phys Eng Express 2020; 6. [PMID: 35045404 DOI: 10.1088/2057-1976/abc45c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/23/2020] [Indexed: 11/11/2022]
Abstract
Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is0.72±0.15for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.
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Affiliation(s)
- Hang Min
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Darryl McClymont
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Australia
| | - Andrew P Bradley
- Science and Engineering Faculty, Queensland University of Technology, Australia
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17
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Neubert A, Bourgeat P, Wood J, Engstrom C, Chandra SS, Crozier S, Fripp J. Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative. Med Phys 2020; 47:4939-4948. [PMID: 32745260 DOI: 10.1002/mp.14421] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/07/2020] [Accepted: 07/24/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE High resolution three-dimensional (3D) magnetic resonance (MR) images are well suited for automated cartilage segmentation in the human knee joint. However, volumetric scans such as 3D Double-Echo Steady-State (DESS) images are not routinely acquired in clinical practice which limits opportunities for reliable cartilage segmentation using (fully) automated algorithms. In this work, a method for generating synthetic 3D MR (syn3D-DESS) images with better contrast and higher spatial resolution from routine, low resolution, two-dimensional (2D) Turbo-Spin Echo (TSE) clinical knee scans is proposed. METHODS A UNet convolutional neural network is employed for synthesizing enhanced artificial MR images suitable for automated knee cartilage segmentation. Training of the model was performed on a large, publically available dataset from the OAI, consisting of 578 MR examinations of knee joints from 102 healthy individuals and patients with knee osteoarthritis. RESULTS The generated synthetic images have higher spatial resolution and better tissue contrast than the original 2D TSE, which allow high quality automated 3D segmentations of the cartilage. The proposed approach was evaluated on a separate set of MR images from 88 subjects with manual cartilage segmentations. It provided a significant improvement in automated segmentation of knee cartilages when using the syn3D-DESS images compared to the original 2D TSE images. CONCLUSION The proposed method can successfully synthesize 3D DESS images from 2D TSE images to provide images suitable for automated cartilage segmentation.
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Affiliation(s)
- Aleš Neubert
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
| | - Jason Wood
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
| | - Craig Engstrom
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, Australia
| | - Jurgen Fripp
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia
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Fazlollahi A, Raniga P, Bourgeat P, Yates P, Bush AI, Salvado O, Ayton S. Restricted Effect of Cerebral Microbleeds on Regional Magnetic Susceptibility. J Alzheimers Dis 2020; 76:571-577. [DOI: 10.3233/jad-200076] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | | | | | - Paul Yates
- Department of Aged Care, Austin Health, Heidelberg, Victoria, Australia
| | - Ashley I. Bush
- University of Melbourne, Parkville, Victoria, Australia
- Melbourne Dementia Research Centre, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | | | - Scott Ayton
- University of Melbourne, Parkville, Victoria, Australia
- Melbourne Dementia Research Centre, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
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19
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Pagnozzi AM, Pannek K, Fripp J, Fiori S, Boyd RN, Rose S. Understanding the impact of bilateral brain injury in children with unilateral cerebral palsy. Hum Brain Mapp 2020; 41:2794-2807. [PMID: 32134174 PMCID: PMC7294067 DOI: 10.1002/hbm.24978] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 01/27/2020] [Accepted: 02/23/2020] [Indexed: 11/29/2022] Open
Abstract
The presence of bilateral brain injury in patients with unilateral cerebral palsy (CP) may impact neuroplasticity in the ipsilateral hemisphere; however, this pattern of injury is typically under‐analyzed due to the lack of methods robust to severe injury. In this study, injury‐robust methods have been applied to structural brain magnetic resonance imaging (MRI) data of a cohort of 91 children with unilateral CP (37 with unilateral and 54 with bilateral brain injury, 4–17 years) and 44 typically developing controls (5–17 years), to determine how brain structure is associated with concurrent motor function, and if these associations differ between patients with unilateral or bilateral injury. Regression models were used to associate these measures with two clinical scores of hand function, with patient age, gender, brain injury laterality, and interaction effects included. Significant associations with brain structure and motor function were observed (Pearson's r = .494–.716), implicating several regions of the motor pathway, and demonstrating an accurate prediction of hand function from MRI, regardless of the extent of brain injury. Reduced brain volumes were observed in patients with bilateral injury, including volumes of the thalamus and corpus callosum splenium, compared to those with unilateral injury, and the healthy controls. Increases in cortical thickness in several cortical regions were observed in cohorts with unilateral and bilateral injury compared to controls, potentially suggesting neuroplasticity might be occurring in the inferior frontal gyrus and the precuneus. These findings identify prospective useful target regions for transcranial magnetic stimulation intervention.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Kerstin Pannek
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | | | - Roslyn N Boyd
- Queensland Cerebral Palsy and Rehabilitation Research Centre, Faculty of Medicine, Centre for Children's Health Research, The University of Queensland, Brisbane, Australia
| | - Stephen Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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20
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Wang T, Zhou J, Tian S, Wang Y, Patel P, Jani AB, Langen KM, Curran WJ, Liu T, Yang X. A planning study of focal dose escalations to multiparametric MRI-defined dominant intraprostatic lesions in prostate proton radiation therapy. Br J Radiol 2020; 93:20190845. [PMID: 31904261 PMCID: PMC7066949 DOI: 10.1259/bjr.20190845] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 12/05/2019] [Accepted: 12/23/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVES The purpose of this study is to investigate the dosimetric effect and clinical impact of delivering a focal radiotherapy boost dose to multiparametric MRI (mp-MRI)-defined dominant intraprostatic lesions (DILs) in prostate cancer using proton therapy. METHODS We retrospectively investigated 36 patients with pre-treatment mp-MRI and CT images who were treated using pencil beam scanning (PBS) proton radiation therapy to the whole prostate. DILs were contoured on co-registered mp-MRIs. Simultaneous integrated boost (SIB) plans using intensity-modulated proton therapy (IMPT) were created based on conventional whole-prostate-irradiation for each patient and optimized with additional DIL coverage goals and urethral constraints. DIL dose coverage and organ-at-risk (OAR) sparing were compared between conventional and SIB plans. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were estimated to evaluate the clinical impact of the SIB plans. RESULTS Optimized SIB plans significantly escalated the dose to DILs while meeting OAR constraints. SIB plans were able to achieve 125, 150 and 175% of prescription dose coverage in 74, 54 and 17% of 36 patients, respectively. This was modeled to result in an increase in DIL TCP by 7.3-13.3% depending on α / β and DIL risk level. CONCLUSION The proposed mp-MRI-guided DIL boost using proton radiation therapy is feasible without violating OAR constraints and demonstrates a potential clinical benefit by improving DIL TCP. This retrospective study suggested the use of IMPT-based DIL SIB may represent a strategy to improve tumor control. ADVANCES IN KNOWLEDGE This study investigated the planning of mp-MRI-guided DIL boost in prostate proton radiation therapy and estimated its clinical impact with respect to TCP and NTCP.
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Affiliation(s)
- Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Sibo Tian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Yinan Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Ashesh B. Jani
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Katja M. Langen
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta 30322, Georgia
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21
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Ranzini MBM, Henckel J, Ebner M, Cardoso MJ, Isaac A, Vercauteren T, Ourselin S, Hart A, Modat M. Automated postoperative muscle assessment of hip arthroplasty patients using multimodal imaging joint segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 183:105062. [PMID: 31522089 DOI: 10.1016/j.cmpb.2019.105062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 08/15/2019] [Accepted: 09/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition and assess implant failure. In this work, we combine CT and MRI for joint bone and muscle segmentation and we propose a novel Intramuscular Fat Fraction estimation method for the quantification of muscle atrophy. METHODS Our multimodal framework is able to segment healthy and pathological musculoskeletal structures as well as implants, and develops into three steps. First, input images are pre-processed to improve the low quality of clinically acquired images and to reduce the noise associated with metal artefact. Subsequently, CT and MRI are non-linearly aligned using a novel approach which imposes rigidity constraints on bony structures to ensure realistic deformation. Finally, taking advantage of a multimodal atlas we created for this task, a multi-atlas based segmentation delineates pelvic bones, abductor muscles and implants on both modalities jointly. From the obtained segmentation, a multimodal estimation of the Intramuscular Fat Fraction can be automatically derived. RESULTS Evaluation of the segmentation in a leave-one-out cross-validation study on 22 hip sides resulted in an average Dice score of 0.90 for skeletal and 0.84 for muscular structures. Our multimodal Intramuscular Fat Fraction was benchmarked on 27 different cases against a standard radiological score, showing stronger association than a single modality approach in a one-way ANOVA F-test analysis. CONCLUSIONS The proposed framework represents a promising tool to support image analysis in hip arthroplasty, being robust to the presence of implants and associated image artefacts. By allowing for the automated extraction of a muscle atrophy imaging biomarker, it could quantitatively inform the decision-making process about patient's management.
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Affiliation(s)
- Marta B M Ranzini
- Centre for Medical Imaging Computing, University College London, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Medical Physics and Biomedical Engineering Department, University College London, London WC1E 6BT, United Kingdom.
| | - Johann Henckel
- Royal National Orthopaedic Hospital NHS Foundation Trust, London, UK
| | - Michael Ebner
- Centre for Medical Imaging Computing, University College London, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Medical Physics and Biomedical Engineering Department, University College London, London WC1E 6BT, United Kingdom
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Medical Physics and Biomedical Engineering Department, University College London, London WC1E 6BT, United Kingdom
| | - Amanda Isaac
- School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Radiology Department, Guys & St Thomas Hospitals NHS Foundation Trust, London SE1 7EH, UK
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Medical Physics and Biomedical Engineering Department, University College London, London WC1E 6BT, United Kingdom
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Medical Physics and Biomedical Engineering Department, University College London, London WC1E 6BT, United Kingdom
| | - Alister Hart
- Royal National Orthopaedic Hospital NHS Foundation Trust, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London SE1 7EH, United Kingdom; Medical Physics and Biomedical Engineering Department, University College London, London WC1E 6BT, United Kingdom
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Largent A, Barateau A, Nunes JC, Mylona E, Castelli J, Lafond C, Greer PB, Dowling JA, Baxter J, Saint-Jalmes H, Acosta O, de Crevoisier R. Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning. Int J Radiat Oncol Biol Phys 2019; 105:1137-1150. [DOI: 10.1016/j.ijrobp.2019.08.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 08/16/2019] [Accepted: 08/22/2019] [Indexed: 12/25/2022]
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23
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Buckley JG, Rai R, Liney GP, Dowling JA, Holloway LC, Metcalfe PE, Keall PJ. Anatomical deformation due to horizontal rotation: towards gantry-free radiation therapy. ACTA ACUST UNITED AC 2019; 64:175014. [DOI: 10.1088/1361-6560/ab324c] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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24
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Similarity clustering‐based atlas selection for pelvic
CT
image segmentation. Med Phys 2019; 46:2243-2250. [DOI: 10.1002/mp.13494] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 01/29/2019] [Accepted: 03/02/2019] [Indexed: 11/07/2022] Open
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25
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Largent A, Barateau A, Nunes JC, Lafond C, Greer PB, Dowling JA, Saint-Jalmes H, Acosta O, de Crevoisier R. Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods. Int J Radiat Oncol Biol Phys 2018; 103:479-490. [PMID: 30336265 DOI: 10.1016/j.ijrobp.2018.10.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/15/2018] [Accepted: 10/01/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE Methods have been recently developed to generate pseudo-computed tomography (pCT) for dose calculation in magnetic resonance imaging (MRI)-only radiation therapy. This study aimed to propose an original nonlocal mean patch-based method (PBM) and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiation therapy. MATERIALS AND METHODS Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer. In addition to the planning computed tomography (CT) scans, T2-weighted MRI scans were acquired. pCTs were generated from MRIs using 3 methods: an original nonlocal mean PBM, ABM, and BDM. The PBM was performed using feature extraction and approximate nearest neighbor search in a training cohort. The PBM accuracy was evaluated in a validation cohort by using imaging and dosimetric endpoints. Imaging endpoints included mean absolute error and mean error between Hounsfield units of the pCT and the reference CT (CTref). Dosimetric endpoints were based on dose-volume histograms calculated from the CTref and the pCTs for various volumes of interest and on 3-dimensional gamma analyses. The PBM uncertainties were compared with those of the ABM and BDM. RESULTS The mean absolute error and mean error obtained from the PBM were 41.1 and -1.1 Hounsfield units. The PBM dose-volume histogram differences were 0.7% for prostate planning target volume V95%, 0.5% for rectum V70Gy, and 0.2% for bladder V50Gy. Compared with ABM and BDM, PBM provided significantly lower dose uncertainties for the prostate planning target volume (70-78 Gy), the rectum (8.5-29 Gy, 40-48 Gy, and 61-73 Gy), and the bladder (12-78 Gy). The PBM mean gamma pass rate (99.5%) was significantly higher than that of ABM (94.9%) or BDM (96.1%). CONCLUSIONS The proposed PBM provides low uncertainties with dose planned on CTref. These uncertainties were smaller than those of ABM and BDM and are unlikely to be clinically significant.
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Affiliation(s)
- Axel Largent
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
| | - Anaïs Barateau
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Jean-Claude Nunes
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Caroline Lafond
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Peter B Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, Australia
| | - Jason A Dowling
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia
| | - Hervé Saint-Jalmes
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Oscar Acosta
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
| | - Renaud de Crevoisier
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France
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26
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Dubey A, Iliopoulos AS, Sun X, Yin FF, Ren L. Iterative inversion of deformation vector fields with feedback control. Med Phys 2018; 45:3147-3160. [PMID: 29757473 DOI: 10.1002/mp.12962] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/13/2018] [Accepted: 04/22/2018] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four-dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. METHOD We introduce a framework of fixed-point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. RESULTS The NTDC-adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis-based predictions. CONCLUSION Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control. Adaptive control is necessary and highly effective in the presence of nonsmall NTDCs. The adaptive iterations or the spectral measures, or both, may potentially be incorporated into deformable image registration methods.
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Affiliation(s)
- Abhishek Dubey
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | | | - Xiaobai Sun
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, 27710, USA.,Medical Physics Program, Duke Kunshan University, Kunshan, Jiangsu, 215316, China
| | - Lei Ren
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, 27710, USA
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Abstract
Over the past decade, the application of magnetic resonance imaging (MRI) has increased, and there is growing evidence to suggest that improvements in the accuracy of target delineation in MRI-guided radiation therapy may improve clinical outcomes in a variety of cancer types. However, some considerations should be recognized including patient motion during image acquisition and geometric accuracy of images. Moreover, MR-compatible immobilization devices need to be used when acquiring images in the treatment position while minimizing patient motion during the scan time. Finally, synthetic CT images (i.e. electron density maps) and digitally reconstructed radiograph images should be generated from MRI images for dose calculation and image guidance prior to treatment. A short review of the concepts and techniques that have been developed for implementation of MRI-only workflows in radiation therapy is provided in this document.
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Affiliation(s)
- Amir M. Owrangi
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Peter B. Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, 2308, Australia
- Department of Radiation Oncology, Calvary Mater Hospital, Newcastle, NSW, 2298, Australia
| | - Carri K. Glide-Hurst
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan
- Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan
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28
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Glodeck D, Hesser J, Zheng L. Potential of metric homotopy between intensity and geometry information for multi-modal 3D registration. Z Med Phys 2018; 28:325-334. [PMID: 29439849 DOI: 10.1016/j.zemedi.2018.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 12/08/2017] [Accepted: 01/17/2018] [Indexed: 10/18/2022]
Abstract
This paper focuses on a novel strategy increasing robustness with respect to local optima when using Mutual Information (MI) in multi-modal image registration. This is realized by integrating additional geometry information in the cost function. Hereby, the main innovation is a generalization of multi-metric registration approaches by means of a metric homotopy. Particularly we realize a method which automatically determines the parameters of the metric homotopy. To construct the cost function independent of the choice of the optimizer, the weighting is defined as a function of one of the metrics instead of optimizer steps. In addition, a differentiable cost function is developed. In comparison to the commonly used technique to process an intensity based registration on different resolutions, the proposed method is three times faster with unchanged accuracy. It is also shown that in the presence of large landmark errors the proposed method outperforms an approach in accuracy in which both similarity functionals are applied one after the other. The method is evaluated on 3D multi-modal human brain data sets from the Retrospective Image Registration Evaluation Project (RIRE). The evaluation is performed using the evaluation website of the RIRE project to make the registration results of the proposed method easily comparable to other methods. Therefore, the presented results are also available online on the RIRE project page.
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Affiliation(s)
- Daniel Glodeck
- Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Germany.
| | - Jürgen Hesser
- Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Germany; Interdisziplinary center for scientific computing (IWR), Heidelberg University, Germany.
| | - Lei Zheng
- Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Germany.
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29
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Ghose S, Greer PB, Sun J, Pichler P, Rivest-Henault D, Mitra J, Richardson H, Wratten C, Martin J, Arm J, Best L, Dowling JA. Regression and statistical shape model based substitute CT generation for MRI alone external beam radiation therapy from standard clinical MRI sequences. Phys Med Biol 2017; 62:8566-8580. [PMID: 28976369 DOI: 10.1088/1361-6560/aa9104] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
In MR only radiation therapy planning, generation of the tissue specific HU map directly from the MRI would eliminate the need of CT image acquisition and may improve radiation therapy planning. The aim of this work is to generate and validate substitute CT (sCT) scans generated from standard T2 weighted MR pelvic scans in prostate radiation therapy dose planning. A Siemens Skyra 3T MRI scanner with laser bridge, flat couch and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole pelvis MRI (1.6 mm 3D isotropic T2w SPACE sequence) was acquired. Patients received a routine planning CT scan. Co-registered whole pelvis CT and T2w MRI pairs were used as training images. Advanced tissue specific non-linear regression models to predict HU for the fat, muscle, bladder and air were created from co-registered CT-MRI image pairs. On a test case T2w MRI, the bones and bladder were automatically segmented using a novel statistical shape and appearance model, while other soft tissues were separated using an Expectation-Maximization based clustering model. The CT bone in the training database that was most 'similar' to the segmented bone was then transformed with deformable registration to create the sCT component of the test case T2w MRI bone tissue. Predictions for the bone, air and soft tissue from the separate regression models were successively combined to generate a whole pelvis sCT. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same IMRT dose plan was found to be [Formula: see text] (mean ± standard deviation) for 39 patients. The 3D Gamma pass rate was [Formula: see text] (2 mm/2%). The novel hybrid model is computationally efficient, generating an sCT in 20 min from standard T2w images for prostate cancer radiation therapy dose planning and DRR generation.
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Affiliation(s)
- Soumya Ghose
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
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Automated T2-mapping of the Menisci From Magnetic Resonance Images in Patients with Acute Knee Injury. Acad Radiol 2017; 24:1295-1304. [PMID: 28551397 DOI: 10.1016/j.acra.2017.03.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 12/23/2016] [Accepted: 03/30/2017] [Indexed: 12/27/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the accuracy of an automated method for segmentation and T2 mapping of the medial meniscus (MM) and lateral meniscus (LM) in clinical magnetic resonance images from patients with acute knee injury. MATERIALS AND METHODS Eighty patients scheduled for surgery of an anterior cruciate ligament or meniscal injury underwent magnetic resonance imaging of the knee (multiplanar two-dimensional [2D] turbo spin echo [TSE] or three-dimensional [3D]-TSE examinations, T2 mapping). Each meniscus was automatically segmented from the 2D-TSE (composite volume) or 3D-TSE images, auto-partitioned into anterior, mid, and posterior regions, and co-registered onto the T2 maps. The Dice similarity index (spatial overlap) was calculated between automated and manual segmentations of 2D-TSE (15 patients), 3D-TSE (16 patients), and corresponding T2 maps (31 patients). Pearson and intraclass correlation coefficients (ICC) were calculated between automated and manual T2 values. T2 values were compared (Wilcoxon rank sum tests) between torn and non-torn menisci for the subset of patients with both manual and automated segmentations to compare statistical outcomes of both methods. RESULTS The Dice similarity index values for the 2D-TSE, 3D-TSE, and T2 map volumes, respectively, were 76.4%, 84.3%, and 75.2% for the MM and 76.4%, 85.1%, and 76.1% for the LM. There were strong correlations between automated and manual T2 values (rMM = 0.95, ICCMM = 0.94; rLM = 0.97, ICCLM = 0.97). For both the manual and the automated methods, T2 values were significantly higher in torn than in non-torn MM for the full meniscus and its subregions (P < .05). Non-torn LM had higher T2 values than non-torn MM (P < .05). CONCLUSIONS The present automated method offers a promising alternative to manual T2 mapping analyses of the menisci and a considerable advance for integration into clinical workflows.
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31
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Ayton S, Fazlollahi A, Bourgeat P, Raniga P, Ng A, Lim YY, Diouf I, Farquharson S, Fripp J, Ames D, Doecke J, Desmond P, Ordidge R, Masters CL, Rowe CC, Maruff P, Villemagne VL, Salvado O, Bush AI. Cerebral quantitative susceptibility mapping predicts amyloid-β-related cognitive decline. Brain 2017; 140:2112-2119. [PMID: 28899019 DOI: 10.1093/brain/awx137] [Citation(s) in RCA: 188] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 05/07/2017] [Indexed: 11/14/2022] Open
Abstract
See Derry and Kent (doi:10.1093/awx167) for a scientific commentary on this article.The large variance in cognitive deterioration in subjects who test positive for amyloid-β by positron emission tomography indicates that convergent pathologies, such as iron accumulation, might combine with amyloid-β to accelerate Alzheimer's disease progression. Here, we applied quantitative susceptibility mapping, a relatively new magnetic resonance imaging method sensitive to tissue iron, to assess the relationship between iron, amyloid-β load, and cognitive decline in 117 subjects who underwent baseline magnetic resonance imaging and amyloid-β positron emission tomography from the Australian Imaging, Biomarkers and Lifestyle study (AIBL). Cognitive function data were collected every 18 months for up to 6 years from 100 volunteers who were either cognitively normal (n = 64) or diagnosed with mild cognitive impairment (n = 17) or Alzheimer's disease (n = 19). Among participants with amyloid pathology (n = 45), higher hippocampal quantitative susceptibility mapping levels predicted accelerated deterioration in composite cognition tests for episodic memory [β(standard error) = -0.169 (0.034), P = 9.2 × 10-7], executive function [β(standard error) = -0.139 (0.048), P = 0.004), and attention [β(standard error) = -0.074 (0.029), P = 0.012]. Deteriorating performance in a composite of language tests was predicted by higher quantitative susceptibility mapping levels in temporal lobe [β(standard error) = -0.104 (0.05), P = 0.036] and frontal lobe [β(standard error) = -0.154 (0.055), P = 0.006]. These findings indicate that brain iron might combine with amyloid-β to accelerate clinical progression and that quantitative susceptibility mapping could be used in combination with amyloid-β positron emission tomography to stratify individuals at risk of decline.
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Affiliation(s)
- Scott Ayton
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia
| | - Amir Fazlollahi
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
| | - Pierrick Bourgeat
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
| | - Parnesh Raniga
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia
| | - Amanda Ng
- Department of Anatomy and Neuroscience, The University of Melbourne, Parkville, Australia
| | - Yen Ying Lim
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia
| | - Ibrahima Diouf
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia
| | - Shawna Farquharson
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,Department of Anatomy and Neuroscience, The University of Melbourne, Parkville, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
| | - David Ames
- National Ageing Research Institute, Parkville, Victoria, Australia.,University of Melbourne Academic Unit for the Psychiatry of Old Age, Parkville, Australia
| | - James Doecke
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
| | - Patricia Desmond
- Department of Medicine and Radiology, Royal Melbourne Hospital, University of Melbourne, Parkville, Australia
| | - Roger Ordidge
- Department of Anatomy and Neuroscience, The University of Melbourne, Parkville, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
| | - Christopher C Rowe
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,Austin Health, Heidelberg, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,Cogstate Ltd, Melbourne, Australia
| | - Victor L Villemagne
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,Austin Health, Heidelberg, Australia
| | | | - Olivier Salvado
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
| | - Ashley I Bush
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.,Cooperative Research Centre for Mental Health, Parkville, Australia
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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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Pagnozzi AM, Dowson N, Doecke J, Fiori S, Bradley AP, Boyd RN, Rose S. Identifying relevant biomarkers of brain injury from structural MRI: Validation using automated approaches in children with unilateral cerebral palsy. PLoS One 2017; 12:e0181605. [PMID: 28763455 PMCID: PMC5538741 DOI: 10.1371/journal.pone.0181605] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 05/02/2017] [Indexed: 11/19/2022] Open
Abstract
Previous studies have proposed that the early elucidation of brain injury from structural Magnetic Resonance Images (sMRI) is critical for the clinical assessment of children with cerebral palsy (CP). Although distinct aetiologies, including cortical maldevelopments, white and grey matter lesions and ventricular enlargement, have been categorised, these injuries are commonly only assessed in a qualitative fashion. As a result, sMRI remains relatively underexploited for clinical assessments, despite its widespread use. In this study, several automated and validated techniques to automatically quantify these three classes of injury were generated in a large cohort of children (n = 139) aged 5–17, including 95 children diagnosed with unilateral CP. Using a feature selection approach on a training data set (n = 97) to find severity of injury biomarkers predictive of clinical function (motor, cognitive, communicative and visual function), cortical shape and regional lesion burden were most often chosen associated with clinical function. Validating the best models on the unseen test data (n = 42), correlation values ranged between 0.545 and 0.795 (p<0.008), indicating significant associations with clinical function. The measured prevalence of injury, including ventricular enlargement (70%), white and grey matter lesions (55%) and cortical malformations (30%), were similar to the prevalence observed in other cohorts of children with unilateral CP. These findings support the early characterisation of injury from sMRI into previously defined aetiologies as part of standard clinical assessment. Furthermore, the strong and significant association between quantifications of injury observed on structural MRI and multiple clinical scores accord with empirically established structure-function relationships.
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Affiliation(s)
- Alex M. Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
- The School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
- * E-mail:
| | - Nicholas Dowson
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - James Doecke
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | | | - Andrew P. Bradley
- The School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Roslyn N. Boyd
- Queensland Cerebral Palsy and Rehabilitation Research Centre, School of Medicine and Science, Centre for Children’s Health Research, The University of Queensland, Brisbane, Australia
| | - Stephen Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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Largent A, Nunes JC, Lafond C, Périchon N, Castelli J, Rolland Y, Acosta O, de Crevoisier R. [MRI-based radiotherapy planning]. Cancer Radiother 2017; 21:788-798. [PMID: 28690126 DOI: 10.1016/j.canrad.2017.02.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/09/2017] [Accepted: 02/27/2017] [Indexed: 12/11/2022]
Abstract
MRI-based radiotherapy planning is a topical subject due to the introduction of a new generation of treatment machines combining a linear accelerator and a MRI. One of the issues for introducing MRI in this task is the lack of information to provide tissue density information required for dose calculation. To cope with this issue, two strategies may be distinguished from the literature. Either a synthetic CT scan is generated from the MRI to plan the dose, or a dose is generated from the MRI based on physical underpinnings. Within the first group, three approaches appear: bulk density mapping assign a homogeneous density to different volumes of interest manually defined on a patient MRI; machine learning-based approaches model local relationship between CT and MRI image intensities from multiple data, then applying the model to a new MRI; atlas-based approaches use a co-registered training data set (CT-MRI) which are registered to a new MRI to create a pseudo CT from spatial correspondences in a final fusion step. Within the second group, physics-based approaches aim at computing the dose directly from the hydrogen contained within the tissues, quantified by MRI. Excepting the physics approach, all these methods generate a synthetic CT called "pseudo CT", on which radiotherapy planning will be finally realized. This literature review shows that atlas- and machine learning-based approaches appear more accurate dosimetrically. Bulk density approaches are not appropriate for bone localization. The fastest methods are machine learning and the slowest are atlas-based approaches. The less automatized are bulk density assignation methods. The physical approaches appear very promising methods. Finally, the validation of these methods is crucial for a clinical practice, in particular in the perspective of adaptive radiotherapy delivered by a linear accelerator combined with an MRI scanner.
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Affiliation(s)
- A Largent
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - J-C Nunes
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - C Lafond
- Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France
| | - N Périchon
- Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France
| | - J Castelli
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - Y Rolland
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Département d'imagerie médicale, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France
| | - O Acosta
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France
| | - R de Crevoisier
- Laboratoire traitement du signal et de l'image, campus de Beaulieu, université de Rennes 1, 263, avenue du Général-Leclerc, 35042 Rennes, France; Département de radiothérapie, centre régional de lutte contre le cancer Eugène-Marquis, avenue de la Bataille-Flandres-Dunkerque, 35042 Rennes, France; Inserm, UMR 1099, 263, avenue du Général-Leclerc, 35042 Rennes, France.
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Ghose S, Dowling JA, Rai R, Liney GP. Substitute CT generation from a single ultra short time echo MRI sequence: preliminary study. Phys Med Biol 2017; 62:2950-2960. [PMID: 28306546 DOI: 10.1088/1361-6560/aa508a] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In MR guided radiation therapy planning both MR and CT images for a patient are acquired and co-registered to obtain a tissue specific HU map. Generation of the HU map directly from the MRI would eliminate the CT acquisition and may improve radiation therapy planning. In this preliminary study of substitute CT (sCT) generation, two porcine leg phantoms were scanned using a 3D ultrashort echo time (PETRA) sequence and co-registered to corresponding CT images to build tissue specific regression models. The model was created from one co-registered CT-PETRA pair to generate the sCT for the other PETRA image. An expectation maximization based clustering was performed on the co-registered PETRA image to identify the soft tissues, dense bone and air class membership probabilities. A tissue specific non linear regression model was built from one registered CT-PETRA pair dataset to predict the sCT of the second PETRA image in a two-fold cross validation schema. A complete substitute CT is generated in 3 min. The mean absolute HU error for air was 0.3 HU, bone was 95 HU, fat was 30 HU and for muscle it was 10 HU. The mean surface reconstruction error for the bone was 1.3 mm. The PETRA sequence enabled a low mean absolute surface distance for the bone and a low HU error for other classes. The sCT generated from a single PETRA sequence shows promise for the generation of fast sCT for MRI based radiation therapy planning.
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Affiliation(s)
- Soumya Ghose
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
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36
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Shiradkar R, Podder TK, Algohary A, Viswanath S, Ellis RJ, Madabhushi A. Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI. Radiat Oncol 2016; 11:148. [PMID: 27829431 PMCID: PMC5103611 DOI: 10.1186/s13014-016-0718-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 10/17/2016] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. METHODS The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. RESULTS Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text]) which is the current clinical standard, radiomics based focal ([Formula: see text]), and whole gland with a radiomics based focal boost ([Formula: see text]). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text]. A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text]. CONCLUSIONS A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.
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Affiliation(s)
- Rakesh Shiradkar
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106 USA
| | - Tarun K Podder
- Department of Radiation Oncology, Case School of Medicine, Cleveland, 44106 USA
| | - Ahmad Algohary
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106 USA
| | - Satish Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106 USA
| | - Rodney J. Ellis
- Department of Radiation Oncology, Case School of Medicine, Cleveland, 44106 USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106 USA
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37
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Walker A, Metcalfe P, Liney G, Batumalai V, Dundas K, Glide‐Hurst C, Delaney GP, Boxer M, Yap ML, Dowling J, Rivest‐Henault D, Pogson E, Holloway L. MRI geometric distortion: Impact on tangential whole-breast IMRT. J Appl Clin Med Phys 2016; 17:7-19. [PMID: 28297426 PMCID: PMC5495026 DOI: 10.1120/jacmp.v17i5.6242] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Accepted: 03/21/2016] [Indexed: 12/04/2022] Open
Abstract
The purpose of this study was to determine the impact of magnetic resonance imaging (MRI) geometric distortions when using MRI for target delineation and planning for whole-breast, intensity-modulated radiotherapy (IMRT). Residual system distortions and combined systematic and patient-induced distortions are considered. This retrospective study investigated 18 patients who underwent whole-breast external beam radiotherapy, where both CT and MRIs were acquired for treatment planning. Distortion phantoms were imaged on two MRI systems, dedicated to radiotherapy planning (a wide, closed-bore 3T and an open-bore 1T). Patient scans were acquired on the 3T system. To simulate MRI-based planning, distortion maps representing residual system distortions were generated via deformable registration between phantom CT and MRIs. Patient CT images and structures were altered to match the residual system distortion measured by the phantoms on each scanner. The patient CTs were also registered to the corresponding patient MRI scans, to assess patient and residual system effects. Tangential IMRT plans were generated and optimized on each resulting CT dataset, then propagated to the original patient CT space. The resulting dose distributions were then evaluated with respect to the standard clinically acceptable DVH and visual assessment criteria. Maximum residual systematic distortion was measured to be 7.9 mm (95%<4.7mm) and 11.9 mm (95%<4.6mm) for the 3T and 1T scanners, respectively, which did not result in clinically unacceptable plans. Eight of the plans accounting for patient and systematic distortions were deemed clinically unacceptable when assessed on the original CT. For these plans, the mean difference in PTV V95 (volume receiving 95% prescription dose) was 0.13±2.51% and -0.73±1.93% for right- and left-sided patients, respectively. Residual system distortions alone had minimal impact on the dosimetry for the two scanners investigated. The combination of MRI systematic and patient-related distortions can result in unacceptable dosimetry for whole-breast IMRT, a potential issue when considering MRI-only radiotherapy treatment planning. PACS number(s): 87.61.-c, 87.57.cp, 87.57.nj, 87.55.D.
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Affiliation(s)
- Amy Walker
- Centre for Medical Radiation Physics, University of WollongongWollongongNSWAustralia
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
| | - Peter Metcalfe
- Centre for Medical Radiation Physics, University of WollongongWollongongNSWAustralia
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
| | - Gary Liney
- Centre for Medical Radiation Physics, University of WollongongWollongongNSWAustralia
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
- Institute of Medical Physics, School of Physics, University of SydneySydneyNSWAustralia
| | - Vikneswary Batumalai
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
- South Western Clinical School, University of New South WalesSydneyNSWAustralia
| | - Kylie Dundas
- Centre for Medical Radiation Physics, University of WollongongWollongongNSWAustralia
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
| | - Carri Glide‐Hurst
- Department of Radiation OncologyHenry Ford Health SystemDetroitMIUSA
| | - Geoff P Delaney
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- South Western Clinical School, University of New South WalesSydneyNSWAustralia
- Collaboration for Cancer Outcomes Research and Evaluation, Liverpool HospitalLiverpoolNSWAustralia
- School of Medicine, University of Western SydneySydneyNSWAustralia
| | - Miriam Boxer
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- South Western Clinical School, University of New South WalesSydneyNSWAustralia
| | - Mei Ling Yap
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
- South Western Clinical School, University of New South WalesSydneyNSWAustralia
- Collaboration for Cancer Outcomes Research and Evaluation, Liverpool HospitalLiverpoolNSWAustralia
- School of Medicine, University of Western SydneySydneyNSWAustralia
| | - Jason Dowling
- Commonwealth Scientific and Industrial Research Organisation Computational Informatics, Australian E‐Health Research CentreBrisbaneAustralia
| | - David Rivest‐Henault
- Commonwealth Scientific and Industrial Research Organisation Computational Informatics, Australian E‐Health Research CentreBrisbaneAustralia
| | - Elise Pogson
- Centre for Medical Radiation Physics, University of WollongongWollongongNSWAustralia
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
| | - Lois Holloway
- Centre for Medical Radiation Physics, University of WollongongWollongongNSWAustralia
- Liverpool and Macarthur Cancer Therapy CentresNSWAustralia
- Ingham Institute for Applied Medical Research, Liverpool HospitalSydneyNSWAustralia
- South Western Clinical School, University of New South WalesSydneyNSWAustralia
- Institute of Medical Physics, School of Physics, University of SydneySydneyNSWAustralia
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Pagnozzi AM, Shen K, Doecke JD, Boyd RN, Bradley AP, Rose S, Dowson N. Using ventricular modeling to robustly probe significant deep gray matter pathologies: Application to cerebral palsy. Hum Brain Mapp 2016; 37:3795-3809. [PMID: 27257958 DOI: 10.1002/hbm.23276] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 04/27/2016] [Accepted: 05/18/2016] [Indexed: 11/11/2022] Open
Abstract
Understanding the relationships between the structure and function of the brain largely relies on the qualitative assessment of Magnetic Resonance Images (MRIs) by expert clinicians. Automated analysis systems can support these assessments by providing quantitative measures of brain injury. However, the assessment of deep gray matter structures, which are critical to motor and executive function, remains difficult as a result of large anatomical injuries commonly observed in children with Cerebral Palsy (CP). Hence, this article proposes a robust surrogate marker of the extent of deep gray matter injury based on impingement due to local ventricular enlargement on surrounding anatomy. Local enlargement was computed using a statistical shape model of the lateral ventricles constructed from 44 healthy subjects. Measures of injury on 95 age-matched CP patients were used to train a regression model to predict six clinical measures of function. The robustness of identifying ventricular enlargement was demonstrated by an area under the curve of 0.91 when tested against a dichotomised expert clinical assessment. The measures also showed strong and significant relationships for multiple clinical scores, including: motor function (r2 = 0.62, P < 0.005), executive function (r2 = 0.55, P < 0.005), and communication (r2 = 0.50, P < 0.005), especially compared to using volumes obtained from standard anatomical segmentation approaches. The lack of reliance on accurate anatomical segmentations and its resulting robustness to large anatomical variations is a key feature of the proposed automated approach. This coupled with its strong correlation with clinically meaningful scores, signifies the potential utility to repeatedly assess MRIs for clinicians diagnosing children with CP. Hum Brain Mapp 37:3795-3809, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia. .,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
| | - Kaikai Shen
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - James D Doecke
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Roslyn N Boyd
- Queensland Cerebral Palsy and Rehabilitation Research Centre, School of Medicine, The University of Queensland, Brisbane, Australia
| | - Andrew P Bradley
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Stephen Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Nicholas Dowson
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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Pagnozzi AM, Dowson N, Fiori S, Doecke J, Bradley AP, Boyd RN, Rose S. Alterations in regional shape on ipsilateral and contralateral cortex contrast in children with unilateral cerebral palsy and are predictive of multiple outcomes. Hum Brain Mapp 2016; 37:3588-603. [PMID: 27259165 DOI: 10.1002/hbm.23262] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 05/04/2016] [Accepted: 05/06/2016] [Indexed: 11/07/2022] Open
Abstract
Congenital brain lesions result in a wide range of cerebral tissue alterations observed in children with cerebral palsy (CP) that are associated with a range of functional impairments. The relationship between injury severity and functional outcomes, however, remains poorly understood. This research investigates the differences in cortical shape between children with congenital brain lesions and typically developing children (TDC) and investigates the correlations between cortical shape and functional outcome in a large cohort of patients diagnosed with unilateral CP. Using 139 structural magnetic resonance images, including 95 patients with clinically diagnosed CP and 44 TDC, cortical segmentations were obtained using a modified expectation maximization algorithm. Three shape characteristics (cortical thickness, curvature, and sulcal depth) were computed within a number of cortical regions. Significant differences in these shape measures compared to the TDC were observed on both the injured hemisphere of children with CP (P < 0.004), as well as on the apparently uninjured hemisphere, illustrating potential compensatory mechanisms in these children. Furthermore, these shape measures were significantly correlated with several functional outcomes, including motor, cognition, vision, and communication (P < 0.012), with three out of these four models performing well on test set validation. This study highlights that cortical neuroplastic effects may be quantified using MR imaging, allowing morphological changes to be studied longitudinally, including any influence of treatment. Ultimately, such approaches could be used for the long term prediction of outcomes and the tailoring of treatment to individuals. Hum Brain Mapp 37:3588-3603, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.,The School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Nicholas Dowson
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | | | - James Doecke
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Andrew P Bradley
- The School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Roslyn N Boyd
- School of Medicine, The University of Queensland, Queensland Cerebral Palsy and Rehabilitation Research Centre, Brisbane, Australia
| | - Stephen Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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40
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Pagnozzi AM, Dowson N, Doecke J, Fiori S, Bradley AP, Boyd RN, Rose S. Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy. NEUROIMAGE-CLINICAL 2016; 11:751-759. [PMID: 27330975 PMCID: PMC4908311 DOI: 10.1016/j.nicl.2016.05.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 04/28/2016] [Accepted: 05/27/2016] [Indexed: 11/25/2022]
Abstract
White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs) of children with cerebral palsy (CP). Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established. In this study, we perform automatic lesion segmentation on a large cohort of data (107 children with unilateral CP and 18 healthy children) with a new, validated method for segmenting both white matter (WM) and grey matter (GM) lesions. The method has better accuracy (94%) than the best current methods (73%), and only requires standard structural MRI sequences. Anatomical lesion burdens most predictive of clinical scores of motor, cognitive, visual and communicative function were identified using the Least Absolute Shrinkage and Selection operator (LASSO). The improved segmentations enabled identification of significant correlations between regional lesion burden and clinical performance, which conform to known structure-function relationships. Model performance was validated in an independent test set, with significant correlations observed for both WM and GM regional lesion burden with motor function (p < 0.008), and between WM and GM lesions alone with cognitive and visual function respectively (p < 0.008). The significant correlation of GM lesions with functional outcome highlights the serious implications GM lesions, in addition to WM lesions, have for prognosis, and the utility of structural MRI alone for quantifying lesion burden and planning therapy interventions.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia; The University of Queensland, School of Information, Technology and Electrical Engineering, St Lucia, Brisbane, Australia.
| | - Nicholas Dowson
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - James Doecke
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | | | - Andrew P Bradley
- The University of Queensland, School of Information, Technology and Electrical Engineering, St Lucia, Brisbane, Australia
| | - Roslyn N Boyd
- The University of Queensland, Queensland Cerebral Palsy and Rehabilitation Research Centre, Centre for Children's Health Research, Faculty of Medicine and Science, Brisbane, Australia
| | - Stephen Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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Dowling JA, Sun J, Pichler P, Rivest-Hénault D, Ghose S, Richardson H, Wratten C, Martin J, Arm J, Best L, Chandra SS, Fripp J, Menk FW, Greer PB. Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences. Int J Radiat Oncol Biol Phys 2015; 93:1144-53. [PMID: 26581150 DOI: 10.1016/j.ijrobp.2015.08.045] [Citation(s) in RCA: 119] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Revised: 08/05/2015] [Accepted: 08/25/2015] [Indexed: 11/28/2022]
Abstract
PURPOSE To validate automatic substitute computed tomography CT (sCT) scans generated from standard T2-weighted (T2w) magnetic resonance (MR) pelvic scans for MR-Sim prostate treatment planning. PATIENTS AND METHODS A Siemens Skyra 3T MR imaging (MRI) scanner with laser bridge, flat couch, and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole-pelvis MRI scan (1.6 mm 3-dimensional isotropic T2w SPACE [Sampling Perfection with Application optimized Contrasts using different flip angle Evolution] sequence) was acquired. Three additional small field of view scans were acquired: T2w, T2*w, and T1w flip angle 80° for gold fiducials. Patients received a routine planning CT scan. Manual contouring of the prostate, rectum, bladder, and bones was performed independently on the CT and MR scans. Three experienced observers contoured each organ on MRI, allowing interobserver quantification. To generate a training database, each patient CT scan was coregistered to their whole-pelvis T2w using symmetric rigid registration and structure-guided deformable registration. A new multi-atlas local weighted voting method was used to generate automatic contours and sCT results. RESULTS The mean error in Hounsfield units between the sCT and corresponding patient CT (within the body contour) was 0.6 ± 14.7 (mean ± 1 SD), with a mean absolute error of 40.5 ± 8.2 Hounsfield units. Automatic contouring results were very close to the expert interobserver level (Dice similarity coefficient): prostate 0.80 ± 0.08, bladder 0.86 ± 0.12, rectum 0.84 ± 0.06, bones 0.91 ± 0.03, and body 1.00 ± 0.003. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same dose prescription was found to be 0.3% ± 0.8%. The 3-dimensional γ pass rate was 1.00 ± 0.00 (2 mm/2%). CONCLUSIONS The MR-Sim setup and automatic sCT generation methods using standard MR sequences generates realistic contours and electron densities for prostate cancer radiation therapy dose planning and digitally reconstructed radiograph generation.
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Affiliation(s)
- Jason A Dowling
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia; University of Newcastle, Callaghan, New South Wales, Australia.
| | - Jidi Sun
- University of Newcastle, Callaghan, New South Wales, Australia
| | - Peter Pichler
- Calvary Mater Newcastle Hospital, Waratah, New South Wales, Australia
| | | | - Soumya Ghose
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia
| | - Haylea Richardson
- Calvary Mater Newcastle Hospital, Waratah, New South Wales, Australia
| | - Chris Wratten
- University of Newcastle, Callaghan, New South Wales, Australia; Calvary Mater Newcastle Hospital, Waratah, New South Wales, Australia
| | - Jarad Martin
- University of Newcastle, Callaghan, New South Wales, Australia; Calvary Mater Newcastle Hospital, Waratah, New South Wales, Australia
| | - Jameen Arm
- Calvary Mater Newcastle Hospital, Waratah, New South Wales, Australia
| | - Leah Best
- Department of Radiology, Hunter New England Health, New Lambton, New South Wales, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia
| | | | - Peter B Greer
- University of Newcastle, Callaghan, New South Wales, Australia; Calvary Mater Newcastle Hospital, Waratah, New South Wales, Australia
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