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Li X, Huang W, Holmes JH. Dynamic Contrast-Enhanced (DCE) MRI. Magn Reson Imaging Clin N Am 2024; 32:47-61. [PMID: 38007282 DOI: 10.1016/j.mric.2023.09.001] [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: 11/27/2023]
Abstract
The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - James H Holmes
- Radiology, Biomedical Engineering, and Holden Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA.
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2
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Zhou X, Fan X, Chatterjee A, Yousuf A, Antic T, Oto A, Karczmar GS. Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer. Phys Eng Sci Med 2023; 46:1215-1226. [PMID: 37432557 DOI: 10.1007/s13246-023-01289-6] [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: 02/01/2023] [Accepted: 06/14/2023] [Indexed: 07/12/2023]
Abstract
The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast ([Formula: see text] and [Formula: see text]) and one slow ([Formula: see text] and [Formula: see text]) exchanging compartment, compared with the standard Tofts model parameters (Ktrans and kep). On average, prostate cancer had significantly higher values (p < 0.01) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.001) between Ktrans and [Formula: see text] for cancer, but weak correlation (r = 0.28, p < 0.05) between kep and [Formula: see text]. Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast [Formula: see text] had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM is useful for quantitative analysis of prostate DCE-MRI data and provides new information in the diagnosis of prostate cancer.
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Affiliation(s)
- Xueyan Zhou
- School of Technology, Harbin University, Harbin, China.
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | | | - Ambereen Yousuf
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, 60637, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
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Hirschler L, Sollmann N, Schmitz‐Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda K, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Emblem KE, Smits M, Petr J, Hangel G. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; 57:1655-1675. [PMID: 36866773 PMCID: PMC10946498 DOI: 10.1002/jmri.28662] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Medical Delta FoundationDelftThe Netherlands
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityKrems an der DonauAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Nazmiye Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenThe Netherlands
- Department of NeurologyHaaglanden Medical CenterThe HagueThe Netherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and PsychotherapyInternational Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes‐Bolyai UniversityCluj‐NapocaRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | - Kathleen Schmainda
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftThe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University Hospital, BrnoBrnoCzech Republic
- Faculty of Medicine, Masaryk UniversityBrnoCzech Republic
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | - Marion Smits
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
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Cao J, Pickup S, Rosen M, Zhou R. Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer. Mol Imaging Biol 2023:10.1007/s11307-023-01824-7. [PMID: 37166575 DOI: 10.1007/s11307-023-01824-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 05/12/2023]
Abstract
PURPOSE We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known. PROCEDURES Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment. RESULTS Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining. CONCLUSION Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.
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Affiliation(s)
- Jianbo Cao
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Current address: Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rong Zhou
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Pancreatic Cancer Research Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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5
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Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI. Sci Rep 2022; 12:8737. [PMID: 35610281 PMCID: PMC9130284 DOI: 10.1038/s41598-022-12582-x] [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: 09/24/2021] [Accepted: 04/27/2022] [Indexed: 01/08/2023] Open
Abstract
Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOFSSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test-retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOFSSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOFSSS derived semi-quantitative parameters was good to excellent (ICC = 0.717-0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOFSSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.
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Spees WM, Sukstanskii AL, Bretthorst GL, Neil JJ, Ackerman JJH. Rat Brain Global Ischemia-Induced Diffusion Changes Revisited: Biophysical Modeling of the Water and NAA MR "Diffusion Signal". Magn Reson Med 2022; 88:1333-1346. [PMID: 35452137 DOI: 10.1002/mrm.29262] [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: 10/12/2021] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To assess changes in intracellular diffusion as a mechanism for the reduction in water ADC that accompanies brain injury. Using NAA as a marker of neuronal cytoplasmic diffusion, NAA diffusion was measured before and after global ischemia (immediately postmortem) in the female Sprague-Dawley rat. METHODS Diffusion-weighted PRESS spectra, with diffusion encoding in a single direction, were acquired from large voxels of rat brain gray matter in vivo and postischemia employing either pairs of pulsed half-sine-shaped gradients (in vivo and postischemia, bmax = 19 ms/μm2 ) or sinusoidal oscillating gradients (in vivo only) with frequencies of 99.2-250 Hz. A 2D randomly oriented cylinder (neurite) model gave estimates of longitudinal and transverse diffusivities (DL and DT , respectively). In this model, DL represents the "free" diffusivity of NAA, whereas DT reflects highly restricted diffusion. Using oscillating gradients, the frequency dependence of DT [DT (ω)] gave estimates of the cylinder (axon/dendrite) radius. RESULTS A 10% decrease in DL,NAA followed global ischemia, dropping from 0.391 ± 0.012 μm2 /ms to 0.350 ± 0.009 μm2 /ms. Modeling DT,NAA (ω) provided an estimate of the neurite radius of 1.0 ± 0.6 μm. CONCLUSION Whereas the increase in apparent intraneuronal viscosity suggested by changes in DL,NAA may contribute to the overall reduction in water ADC associated with brain injury, it is not sufficient to be the sole explanation. Estimates of neurite radius based on DT (ω) were consistent with literature values.
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Affiliation(s)
- William M Spees
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Alex L Sukstanskii
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - G Larry Bretthorst
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Jeffrey J Neil
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri.,Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.,Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri
| | - Joseph J H Ackerman
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri.,Department of Chemistry, Washington University, St. Louis, Missouri.,Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri.,Alvin J Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
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Liu F, Zhao M, Lu S, Kang L. Study on the Value of DCE-MRI in Differentiating Glossitis and Tongue Cancer and the Intratumour Heterogeneity. Cancer Manag Res 2021; 13:6925-6934. [PMID: 34526818 PMCID: PMC8436779 DOI: 10.2147/cmar.s315418] [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: 04/21/2021] [Accepted: 08/11/2021] [Indexed: 12/02/2022] Open
Abstract
Objective DCE-MRI is an imaging technique that reflects the blood perfusion status of the tissue’s microcirculation. The purpose of this article is to explore the clinical value of dynamic contrast-enhanced (DCE)-MRI in distinguishing benign and malignant tongue lesions and the internal heterogeneity of a tumour. Methods The patients were divided into a tongue cancer group (22 patients) and a glossitis group (7 patients) based on the pathology results. All of the patients underwent DCE-MRI examination. Results The results of this study showed that the volume transfer constant (Ktrans), rate constant (Kep), contrast enhancement ratio (CER) and initial area under the gadolinium contrast agent concentration time curve (IAUGG) values of the tongue cancer group were significantly higher than those of the glossitis group, and the difference was statistically significant (P < 0.05). However, the extravascular extracellular volume fraction (Ve), fractional plasma volume (fPV), maximum slope (MaxSlope), and bolus arrival time (BAT) values measured by DCE-MRI in the tongue cancer group were not significantly different from those in the glossitis group (P > 0.05). The results of this study showed that the Ktrans, Kep, and IAUGG values measured by DCE-MRI had a good ability to distinguish tongue inflammation from tumours and Ktrans threshold of 0.484 has the best discriminative ability among them. The mean Ktrans values of stage I–II lesions were significantly higher than that of stage III–IV lesion (p = 0.045). Conclusion DCE-MRI is effective in distinguishing between benign and malignant tongue lesions and the internal heterogeneity of the tumour; it is worth following up in a larger study. Clinical Registration Number Research registry 6393.
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Affiliation(s)
- Fenghai Liu
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061001, People's Republic of China
| | - Meng Zhao
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061001, People's Republic of China
| | - Shan Lu
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061001, People's Republic of China
| | - Liqing Kang
- Department of Magnetic Resonance Imaging, Cangzhou Central Hospital, Cangzhou, Hebei Province, 061001, People's Republic of China
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Callewaert B, Jones EAV, Himmelreich U, Gsell W. Non-Invasive Evaluation of Cerebral Microvasculature Using Pre-Clinical MRI: Principles, Advantages and Limitations. Diagnostics (Basel) 2021; 11:diagnostics11060926. [PMID: 34064194 PMCID: PMC8224283 DOI: 10.3390/diagnostics11060926] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 12/11/2022] Open
Abstract
Alterations to the cerebral microcirculation have been recognized to play a crucial role in the development of neurodegenerative disorders. However, the exact role of the microvascular alterations in the pathophysiological mechanisms often remains poorly understood. The early detection of changes in microcirculation and cerebral blood flow (CBF) can be used to get a better understanding of underlying disease mechanisms. This could be an important step towards the development of new treatment approaches. Animal models allow for the study of the disease mechanism at several stages of development, before the onset of clinical symptoms, and the verification with invasive imaging techniques. Specifically, pre-clinical magnetic resonance imaging (MRI) is an important tool for the development and validation of MRI sequences under clinically relevant conditions. This article reviews MRI strategies providing indirect non-invasive measurements of microvascular changes in the rodent brain that can be used for early detection and characterization of neurodegenerative disorders. The perfusion MRI techniques: Dynamic Contrast Enhanced (DCE), Dynamic Susceptibility Contrast Enhanced (DSC) and Arterial Spin Labeling (ASL), will be discussed, followed by less established imaging strategies used to analyze the cerebral microcirculation: Intravoxel Incoherent Motion (IVIM), Vascular Space Occupancy (VASO), Steady-State Susceptibility Contrast (SSC), Vessel size imaging, SAGE-based DSC, Phase Contrast Flow (PC) Quantitative Susceptibility Mapping (QSM) and quantitative Blood-Oxygenation-Level-Dependent (qBOLD). We will emphasize the advantages and limitations of each strategy, in particular on applications for high-field MRI in the rodent's brain.
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Affiliation(s)
- Bram Callewaert
- Biomedical MRI Group, University of Leuven, Herestraat 49, bus 505, 3000 Leuven, Belgium; (B.C.); (W.G.)
- CMVB, Center for Molecular and Vascular Biology, University of Leuven, Herestraat 49, bus 911, 3000 Leuven, Belgium;
| | - Elizabeth A. V. Jones
- CMVB, Center for Molecular and Vascular Biology, University of Leuven, Herestraat 49, bus 911, 3000 Leuven, Belgium;
- CARIM, Maastricht University, Universiteitssingel 50, 6200 MD Maastricht, The Netherlands
| | - Uwe Himmelreich
- Biomedical MRI Group, University of Leuven, Herestraat 49, bus 505, 3000 Leuven, Belgium; (B.C.); (W.G.)
- Correspondence:
| | - Willy Gsell
- Biomedical MRI Group, University of Leuven, Herestraat 49, bus 505, 3000 Leuven, Belgium; (B.C.); (W.G.)
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9
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Julie L, Ikram D, Mailyn PL, Augustin L, Afef B, Joevin S, Bentoumi I, Cuenod CA, Daniel B. A free time point model for dynamic contrast enhanced exploration. Magn Reson Imaging 2021; 80:39-49. [PMID: 33905829 DOI: 10.1016/j.mri.2021.04.005] [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: 01/20/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023]
Abstract
Dynamic-Contrast-Enhanced (DCE) Imaging has been widely studied to characterize microcirculatory disorders associated with various diseases. Although numerous studies have demonstrated its diagnostic interest, the physiological interpretation using pharmacokinetic models often remains debatable. Indeed, to be interpretable, a model must provide, at first instance, an accurate description of the DCE data. However, the evaluation and optimization of this accuracy remain rather limited in DCE. Here we established a non-linear Free-Time-Point-Hermite (FTPH) data-description model designed to fit DCE data accurately. Its performance was evaluated on data generated using two contrasting pharmacokinetic microcirculatory hypotheses (MH). The accuracy of data description of the models was evaluated by calculating the mean squared error (QE) from initial and assessed tissue impulse responses. Then, FTPH assessments were provided to blinded observers to evaluate if these assessments allowed observers to identify MH in their data. Regardless of the initial pharmacokinetic model used for data generation, QE was lower than 3% for the noise-free datasets and increased up to 10% for a signal-to-noise-ratio (SNR) of 20. Under SNR = 20, the sensitivity and specificity of the MH identification were over 80%. The performance of the FTPH model was higher than that of the B-Spline model used as a reference. The accuracy of the FTPH model regardless of the initial MH provided an opportunity to have a reference to check the accuracy of other pharmacokinetic models.
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Affiliation(s)
- Levebvre Julie
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | - Djebali Ikram
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | | | | | | | - Sourdon Joevin
- Université de Paris, PARCC, INSERM, Paris F-75015, France.
| | - Isma Bentoumi
- Université de Paris, PARCC, INSERM, Paris F-75015, France
| | - Charles-André Cuenod
- Université de Paris, PARCC, INSERM, Paris F-75015, France; Service Radiologie, AP-HP, Hôpital Européen Georges Pompidou, F-75015, France.
| | - Balvay Daniel
- Université de Paris, PARCC, INSERM, Paris F-75015, France; Université de Paris, Plateforme d'Imageries du Vivant, F-75015, France.
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10
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Liu S, Yu X, Yang S, Hu P, Hu Y, Chen X, Li Y, Zhang Z, Li C, Lu Q. Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer. Front Oncol 2021; 11:610338. [PMID: 33842316 PMCID: PMC8033032 DOI: 10.3389/fonc.2021.610338] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 03/11/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning. Methods The clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves. Results The radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801. Conclusion The radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.
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Affiliation(s)
- Siye Liu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Songhua Yang
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Pingsheng Hu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Yingbin Hu
- Department of Intestinal Oncology Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoyan Chen
- Department of Pathology, Hunan Cancer Hospital, Changsha, China
| | - Yilin Li
- Department of Pathology, Hunan Cancer Hospital, Changsha, China
| | - Zhe Zhang
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Cheng Li
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Qiang Lu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
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11
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Nijkamp J, Kallehauge J. Editorial for "Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging". J Magn Reson Imaging 2021; 53:1911-1912. [PMID: 33559246 DOI: 10.1002/jmri.27545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Jasper Nijkamp
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Jesper Kallehauge
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
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12
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Fuentes D, Thompson E, Jacobsen M, Crouch AC, Layman RR, Riviere B, Cressman E. Imaging-based characterization of convective tissue properties. Int J Hyperthermia 2021; 37:155-163. [PMID: 33426993 PMCID: PMC7983068 DOI: 10.1080/02656736.2020.1845403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Convective transport is an important phenomenon for nanomedicine delivery. We present an imaging-based approach to recover tissue properties that are significant in the accumulation of nanoparticles delivered via systemic methods. The classical pharmacokinetic analysis develops governing equations for the particle transport from a first principle mass balance. Fundamentally, the governing equations for compartmental mass balance represent a spatially invariant mass transport between compartments and do not capture spatially variant convection phenomena. Further, the parameters recovered from this approach do not necessarily have direct meaning with respect to the governing equations for convective transport. In our approach, a framework is presented for directly measuring permeability in the sense of Darcy flow through porous tissue. Measurements from our approach are compared to an extended Tofts model as a control. We demonstrate that a pixel-wise iterative clustering algorithm may be applied to reduce the parameter space of the measurements. We show that measurements obtained from our approach are correlated with measurements obtained from the extended Tofts model control. These correlations demonstrate that the proposed approach contains similar information to an established compartmental model and may be useful in providing an alternative theoretical framework for parameterizing mathematical models for treatment planning and diagnostic studies involving nanomedicine where convection dominated effects are important.
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Affiliation(s)
- D Fuentes
- Departments of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - E Thompson
- Departments of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - M Jacobsen
- Departments of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - A Colleen Crouch
- Interventional Radiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - R R Layman
- Departments of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - B Riviere
- Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - E Cressman
- Interventional Radiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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13
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Zöllner FG, Dastrù W, Irrera P, Longo DL, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR. Analysis Protocol for Dynamic Contrast Enhanced (DCE) MRI of Renal Perfusion and Filtration. Methods Mol Biol 2021; 2216:637-653. [PMID: 33476028 PMCID: PMC9703217 DOI: 10.1007/978-1-0716-0978-1_38] [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: 06/12/2023]
Abstract
Here we present an analysis protocol for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data of the kidneys. It covers comprehensive steps to facilitate signal to contrast agent concentration mapping via T1 mapping and the calculation of renal perfusion and filtration parametric maps using model-free approaches, model free analysis using deconvolution, the Toft's model and a Bayesian approach.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.
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Affiliation(s)
- Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Walter Dastrù
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Pietro Irrera
- University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Torino, Italy.
| | - Kevin M Bennett
- Washington University School of Medicine, St. Louis, MO, USA
| | - Scott C Beeman
- Washington University School of Medicine, St. Louis, MO, USA
| | | | - Joel R Garbow
- Washington University School of Medicine, St. Louis, MO, USA
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14
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Pedersen M, Irrera P, Dastrù W, Zöllner FG, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR, Longo DL. Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts. Methods Mol Biol 2021; 2216:205-227. [PMID: 33476002 DOI: 10.1007/978-1-0716-0978-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Dynamic contrast-enhanced (DCE) MRI monitors the transit of contrast agents, typically gadolinium chelates, through the intrarenal regions, the renal cortex, the medulla, and the collecting system. In this way, DCE-MRI reveals the renal uptake and excretion of the contrast agent. An optimal DCE-MRI acquisition protocol involves finding a good compromise between whole-kidney coverage (i.e., 3D imaging), spatial and temporal resolution, and contrast resolution. By analyzing the enhancement of the renal tissues as a function of time, one can determine indirect measures of clinically important single-kidney parameters as the renal blood flow, glomerular filtration rate, and intrarenal blood volumes. Gadolinium-containing contrast agents may be nephrotoxic in patients suffering from severe renal dysfunction, but otherwise DCE-MRI is clearly useful for diagnosis of renal functions and for assessing treatment response and posttransplant rejection.Here we introduce the concept of renal DCE-MRI, describe the existing methods, and provide an overview of preclinical DCE-MRI applications to illustrate the utility of this technique to measure renal perfusion and glomerular filtration rate in animal models.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction is complemented by two separate publications describing the experimental procedure and data analysis.
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Affiliation(s)
- Michael Pedersen
- Department of Clinical Medicine - Comparative Medicine Lab, Aarhus University, Aarhus, Denmark
| | - Pietro Irrera
- University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Walter Dastrù
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kevin M Bennett
- Washington University School of Medicine, St. Louis, MO, USA
| | - Scott C Beeman
- Washington University School of Medicine, St. Louis, MO, USA
| | | | - Joel R Garbow
- Washington University School of Medicine, St. Louis, MO, USA
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Torino, Italy.
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15
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Zhang Z, He D, Song Y, Yan Z, Wang X, Shao J, Hou Z. Exploring the Inter-voxel Information in Pharmacokinetic Maps for Cervical Carcinoma Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1477-1480. [PMID: 33018270 DOI: 10.1109/embc44109.2020.9176233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Physiological parameters can be estimated from dynamic contrast enhanced magnetic resonance imaging (DCEMRI) data using pharmacokinetic models. This work evaluates the performance of various pharmacokinetic models through a retrospective study on cervix cancer, including two generalized kinetic models and three 2-compartment exchange models (2CXMs). In the current clinical practice, region of interest (ROI) is treated as a whole and the models are assessed by their top pharmacokinetic parameters. We explore the intervoxel relationship in the pharmacokinetic parameter maps and demonstrate that, for those insignificant parameters, texture descriptors can largely improve their discriminative power. Multi-parametric classifiers are developed to fuse the information carried by physiological parameters and the descriptors. Assessed merely by the top parameter, the DP (distributed parameter) model is the best one with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.80; by combining multiple pharmacokinetic parameters, the ExTofts model is the winner with an AUC of 0.837. Finally, the classifier of the AATH (adiabatic approximation to the tissue homogeneity) model build on combined features achieves an AUC of 0.92.Clinical Relevance - Using data from 36 cervical cancer patients and 17 normal subjects, this work quantitatively compared the various pharmacokinetic models and provided recommendations for model selection in cervical cancer diagnosis.
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16
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Chen S, Shu Z, Li Y, Chen B, Tang L, Mo W, Shao G, Shao F. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients. Front Oncol 2020; 10:1410. [PMID: 32923392 PMCID: PMC7456979 DOI: 10.3389/fonc.2020.01410] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 07/03/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose: The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.
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Affiliation(s)
- Shujun Chen
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yongfeng Li
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Bo Chen
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Pathology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Lirong Tang
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Wenju Mo
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Guoliang Shao
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Feng Shao
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou, China
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17
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Besson FL, Fernandez B, Faure S, Mercier O, Seferian A, Mignard X, Mussot S, le Pechoux C, Caramella C, Botticella A, Levy A, Parent F, Bulifon S, Montani D, Mitilian D, Fadel E, Planchard D, Besse B, Ghigna-Bellinzoni MR, Comtat C, Lebon V, Durand E. 18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data. EJNMMI Res 2020; 10:88. [PMID: 32734484 PMCID: PMC7392998 DOI: 10.1186/s13550-020-00671-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.
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Affiliation(s)
- Florent L Besson
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France.
- Department of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, CHU Bicêtre, 94270, Le Kremlin-Bicêtre, France.
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France.
| | | | - Sylvain Faure
- Laboratoire de Mathématiques d'Orsay, CNRS, Université Paris-Saclay, 91405, Orsay, France
| | - Olaf Mercier
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Andrei Seferian
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Xavier Mignard
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
| | - Sacha Mussot
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Cecile le Pechoux
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Caroline Caramella
- Department of Radiology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Angela Botticella
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Florence Parent
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Sophie Bulifon
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - David Montani
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Delphine Mitilian
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Elie Fadel
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - David Planchard
- Department of Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Benjamin Besse
- Department of Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | | | - Claude Comtat
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Vincent Lebon
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Emmanuel Durand
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- Department of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, CHU Bicêtre, 94270, Le Kremlin-Bicêtre, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
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18
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Bliesener Y, Acharya J, Nayak KS. Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1712-1723. [PMID: 31794389 PMCID: PMC8887912 DOI: 10.1109/tmi.2019.2953901] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.
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19
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Clinical Implementation of a Free-Breathing, Motion-Robust Dynamic Contrast-Enhanced MRI Protocol to Evaluate Pleural Tumors. AJR Am J Roentgenol 2020; 215:94-104. [PMID: 32348181 DOI: 10.2214/ajr.19.21612] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE. The purpose of this study was to develop a motion insensitive clinical dynamic contrast-enhanced MRI (DCE-MRI) protocol to assess the response of pleural tumors in clinical trials. MATERIALS AND METHODS. Thirty-two patients with pleura-based lesions were administered contrast material and imaged with gradient-recalled echo DCE-MRI sequence variants: either a traditional cartesian k-space acquisition (FLASH), a time-resolved imaging with stochastic trajectories acquisition (TWIST), or a radial stack-of-stars acquisition (radial) sequence in addition to other standard-of-care imaging sequences. Each image acquisition's sensitivity to motion was evaluated by comparing the motion of the thoracic border in 3D throughout the acquisition. One-way ANOVA was used to compare the image quality between different acquisitions. The 95% CIs were calculated for mean thoracic border displacement. The effects of motion on kinetic parameter estimation were explored with simulations according to clinically acquired data. RESULTS. Radial was the most motion-robust sequence with subvoxel mean displacement in the superior-inferior direction (0.4 ± 1.2 [SD] mm). FLASH showed intermediate displacement (4.6 ± 2.0 mm), whereas TWIST was most sensitive to motion (6.4 ± 3.4 mm). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images acquired with the radial sequence were on par or better than the FLASH and TWIST sequences when reconstructed with an improved density compensation algorithm. Simulations showed that motion on scans showing pleural-based lesions can lead to markedly inaccurate kinetic parameter estimation and inappropriate kinetic model convergence within a nested model analysis. CONCLUSION. A practical radial k-space trajectory sequence that provides motion-insensitive pharmacokinetic parameters was incorporated as part of the DCE-MRI protocol of pleural tumors. Validation and usefulness in clinical trials assessing response to therapy is needed.
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Toward noninvasive quantification of adipose tissue oxygenation with MRI. Int J Obes (Lond) 2020; 44:1776-1783. [PMID: 32231252 DOI: 10.1038/s41366-020-0567-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/27/2020] [Accepted: 03/04/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Molecular oxygen (O2) plays a key role in normal and pathological adipose tissue function, yet technologies to measure its role in adipose tissue function are limited. O2 is paramagnetic and, in principle, directly influences the magnetic resonance (MR) 1H longitudinal relaxation rate constant of lipids, R1; thus, we hypothesize that MR imaging (MRI) can directly measure adipose O2 via a simple measure of R1. METHODS R1 was measured in a 4.7T preclinical MRI system at discrete oxygen partial pressure (pO2) levels. These measures were made in vitro in an idealized system and in vivo in subcutaneous and visceral white adipose of rodents. pO2 was determined using an invasive fiber-optic oxygen monitor. From the MRI and fiber optic data we determined the "relaxivity" of O2 in lipid, a critical parameter in converting the MRI-based R1 measurement into pO2. We used breathing gas challenge to estimate the changes in lipid pO2 (ΔpO2). RESULTS The relaxivity of O2 in lipid was determined to be 1.7·10-3 ± 4·10-4 mmHg-1s-1 at 4.7T and 37 °C, and was consistent between in vitro and in vivo adipose tissue. There was a strong, significant correlation between MRI- and gold standard OxyLite-based measurements of lipid ΔpO2 for in vivo visceral and subcutaneous fat depots in rodents. CONCLUSION This study lays the foundation for a direct, noninvasive measure of adipose pO2 using MRI and will allow for noninvasive measurement of O2 flux in adipose tissue. The proposed approach would be of particular importance in the interrogation of the pathogenesis of type 2 diabetes, where it has been suggested that adipose tissue hypoxia is an independent driver of insulin resistance pathway.
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Shao J, Zhang Z, Liu H, Song Y, Yan Z, Wang X, Hou Z. DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction. Comput Biol Med 2020; 118:103634. [PMID: 32174312 DOI: 10.1016/j.compbiomed.2020.103634] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 01/15/2020] [Accepted: 01/27/2020] [Indexed: 12/30/2022]
Abstract
Pharmacokinetic parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time course data enable the physio-biological interpretation of tissue angiogenesis. This study aims to develop machine learning approaches for cervical carcinoma prediction based on pharmacokinetic parameters. The performance of individual parameters was assessed in terms of their efficacy in differentiating cancerous tissue from normal cervix tissue. The effect of combining parameters was evaluated using the following two approaches: the first approach was based on support vector machines (SVMs) to combine the parameters from one pharmacokinetic model or across several models; the second approach was based on a novel method called APITL (artificial pharmacokinetic images for transfer learning), which was designed to fully utilize the comprehensive pharmacokinetic information acquired from DCE-MRI data. A "winner-takes-all" strategy was employed to consolidate the slice-wise prediction into subject-wise prediction. Experiments were carried out with a dataset comprising 36 patients with cervical cancer and 17 healthy subjects. The results demonstrated that parameter Ve, representing volume fraction of the extracellular extravascular space (EES), attained high discriminative power regardless of the pharmacokinetic model used for estimation. An approximately 10% improvement in the accuracy was achieved with the SVM approach. The APITL method further outperformed SVM and attained a subject-wise prediction accuracy of 94.3%. Our experiment demonstrated that APITL could predict cervical carcinoma with high accuracy and had potential in clinical applications.
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Affiliation(s)
| | - Zhuo Zhang
- Institute for Infocomm Research, Singapore.
| | | | - Ying Song
- Institute for Infocomm Research, Singapore
| | - Zhihan Yan
- The Second Affiliated Hospital of Wenzhou Medical University, China
| | - Xue Wang
- The Second Affiliated Hospital of Wenzhou Medical University, China
| | - Zujun Hou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China
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Paudyal R, Lu Y, Hatzoglou V, Moreira A, Stambuk HE, Oh JH, Cunanan KM, Nunez DA, Mazaheri Y, Gonen M, Ho A, Fagin JA, Wong RJ, Shaha A, Tuttle RM, Shukla-Dave A. Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers. NMR IN BIOMEDICINE 2020; 33:e4166. [PMID: 31680360 PMCID: PMC7687051 DOI: 10.1002/nbm.4166] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/04/2019] [Accepted: 07/17/2019] [Indexed: 06/10/2023]
Abstract
The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Andre Moreira
- Department of Pathology, NYU Langone Medical Center, New
York, USA
| | - Hilda E. Stambuk
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Kristen M. Cunanan
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - David Aramburu Nunez
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - Alan Ho
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - James A. Fagin
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Ashok Shaha
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - R. Michael Tuttle
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
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23
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Mittermeier A, Ertl-Wagner B, Ricke J, Dietrich O, Ingrisch M. Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application. Phys Med Biol 2019; 64:18NT02. [PMID: 31404913 DOI: 10.1088/1361-6560/ab3a5a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen's d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates.
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Affiliation(s)
- Andreas Mittermeier
- Department of Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany. Author to whom any correspondence should be addressed
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Inglese M, Ordidge KL, Honeyfield L, Barwick TD, Aboagye EO, Waldman AD, Grech-Sollars M. Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models. Neuroradiology 2019; 61:1375-1386. [PMID: 31392385 PMCID: PMC6848046 DOI: 10.1007/s00234-019-02265-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 07/12/2019] [Indexed: 12/12/2022]
Abstract
Purpose The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. Conclusions The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.
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Affiliation(s)
- Marianna Inglese
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.
| | | | - Lesley Honeyfield
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Tara D Barwick
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Adam D Waldman
- Department of Medicine, Imperial College London, London, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Matthew Grech-Sollars
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
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Lecler A, Balvay D, Cuenod C, Marais L, Zmuda M, Sadik J, Galatoire O, Farah E, El Methni J, Zuber K, Bergès O, Savatovsky J, Fournier L. Quality‐based pharmacokinetic model selection on DCE‐MRI for characterizing orbital lesions. J Magn Reson Imaging 2019; 50:1514-1525. [DOI: 10.1002/jmri.26747] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/15/2019] [Accepted: 03/18/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Augustin Lecler
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Daniel Balvay
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Charles‐André Cuenod
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
- Radiology Department, Hôpital Européen Georges PompidouUniversité Paris Descartes Sorbonne Paris Cité, Assistance Publique‐Hôpitaux de Paris Paris France
| | - Louise Marais
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Mathieu Zmuda
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Jean‐Claude Sadik
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Olivier Galatoire
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Edgar Farah
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Jonathan El Methni
- MAP5, UMR CNRS 8145Université Paris Descartes Sorbonne Paris Cité France
| | - Kevin Zuber
- Department of Clinical ResearchFoundation Adolphe de Rothschild Hospital Paris France
| | - Olivier Bergès
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Julien Savatovsky
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Laure Fournier
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
- Radiology Department, Hôpital Européen Georges PompidouUniversité Paris Descartes Sorbonne Paris Cité, Assistance Publique‐Hôpitaux de Paris Paris France
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Klawer EME, van Houdt PJ, Simonis FFJ, van den Berg CAT, Pos FJ, Heijmink SWTPJ, Isebaert S, Haustermans K, van der Heide UA. Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients. Magn Reson Med 2019; 81:3358-3369. [PMID: 30656738 PMCID: PMC6590420 DOI: 10.1002/mrm.27646] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 11/07/2018] [Accepted: 12/04/2018] [Indexed: 12/31/2022]
Abstract
Purpose The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast‐enhanced (DCE)‐MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients. Methods Twenty‐two patients with biopsy‐proven prostate cancer underwent a 3T MRI exam twice. DCE‐MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIFMAGN), phase (AIFPHASE), and complex signal (AIFCOMPLEX). The Tofts model was applied to extract Ktrans, kep and ve. Repeatability of AIF curve characteristics and TK parameters was assessed with the within‐subject coefficient of variation (wCV). Results The wCV for peak height and full width at half maximum for AIFCOMPLEX (7% and 8%) indicated an improved repeatability compared to AIFMAGN (12% and 12%) and AIFPHASE (12% and 7%). This translated in lower wCV values for Ktrans (11%) with AIFCOMPLEX in comparison to AIFMAGN (24%) and AIFPHASE (15%). For kep, the wCV was 16% with AIFMAGN, 13% with AIFPHASE, and 13% with AIFCOMPLEX. Conclusion Repeatability of AIFPHASE and AIFCOMPLEX is higher than for AIFMAGN, resulting in a better repeatability of TK parameters. Thus, use of either AIFPHASE or AIFCOMPLEX improves the robustness of quantitative analysis of DCE‐MRI in prostate cancer.
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Affiliation(s)
- Edzo M E Klawer
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petra J van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frank F J Simonis
- Department of Radiation Oncology, Imaging Division, University Medical Center, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiation Oncology, Imaging Division, University Medical Center, Utrecht, The Netherlands
| | - Floris J Pos
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Sofie Isebaert
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Karin Haustermans
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
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Hansen MB, Tietze A, Haack S, Kallehauge J, Mikkelsen IK, Østergaard L, Mouridsen K. Robust estimation of hemo-dynamic parameters in traditional DCE-MRI models. PLoS One 2019; 14:e0209891. [PMID: 30605459 PMCID: PMC6317807 DOI: 10.1371/journal.pone.0209891] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 11/09/2018] [Indexed: 01/04/2023] Open
Abstract
PURPOSE In dynamic contrast enhanced (DCE) MRI, separation of signal contributions from perfusion and leakage requires robust estimation of parameters in a pharmacokinetic model. We present and quantify the performance of a method to compute tissue hemodynamic parameters from DCE data using established pharmacokinetic models. METHODS We propose a Bayesian scheme to obtain perfusion metrics from DCE MRI data. Initial performance is assessed through digital phantoms of the extended Tofts model (ETM) and the two-compartment exchange model (2CXM), comparing the Bayesian scheme to the standard Levenberg-Marquardt (LM) algorithm. Digital phantoms are also invoked to identify limitations in the pharmacokinetic models related to measurement conditions. Using computed maps of the extra vascular volume (ve) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity ve values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance. RESULTS The Bayesian parameter estimation scheme demonstrated superior performance over the LM technique in the digital phantom simulations. In addition, we identified limitations in parameter reliability in relation to scan duration for the 2CXM. DCE data for glioma and cervical cancer patients was analyzed with both algorithms and demonstrated improvement in image readability for the Bayesian method. The Bayesian method demonstrated significantly fewer non-physiological high-intensity ve values for the ETM (p<0.0001) and the 2CXM (p<0.0001). CONCLUSION We have demonstrated substantial improvement of the perceptive quality of pharmacokinetic parameters from advanced compartment models using the Bayesian parameter estimation scheme as compared to the LM technique.
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Affiliation(s)
- Mikkel B. Hansen
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Anna Tietze
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Inst. of Neuroradiology, Charité University Medicine Berlin, Berlin, Germany
| | - Søren Haack
- Department of Clinical Engineering, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jesper Kallehauge
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Irene K. Mikkelsen
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Kim Mouridsen
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Klawer EM, van Houdt PJ, Pos FJ, Heijmink SW, van Osch MJ, van der Heide UA. Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer. NMR IN BIOMEDICINE 2018; 31:e3946. [PMID: 29974981 PMCID: PMC6175355 DOI: 10.1002/nbm.3946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 04/12/2018] [Accepted: 04/24/2018] [Indexed: 06/08/2023]
Abstract
The volume transfer constant Ktrans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported Ktrans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. Ktrans , kep and ve ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T1 -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. Ktrans , kep and ve were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in Ktrans , kep or ve for the studied injection durations. The study demonstrates that the TKA parameters Ktrans , kep and ve , measured in the prostate, do not show a significant change as a function of injection duration.
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Affiliation(s)
- Edzo M.E. Klawer
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Petra J. van Houdt
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Floris J. Pos
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | | | | | - Uulke A. van der Heide
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
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Yin T, Liu Y, Peeters R, Feng Y, Ni Y. Pancreatic imaging: Current status of clinical practices and small animal studies. World J Methodol 2017; 7:101-107. [PMID: 29026690 PMCID: PMC5618143 DOI: 10.5662/wjm.v7.i3.101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Revised: 08/22/2017] [Accepted: 09/04/2017] [Indexed: 02/06/2023] Open
Abstract
Different causative factors acting on the pancreas can result in diseases such as pancreatitis, diabetes and pancreatic tumors. The high incidence and mortality of pancreatic diseases have placed diagnostic imaging in a crucial position in daily clinical practice. In this mini-review article different pancreatic imaging techniques are discussed, from the standard clinical imaging modalities and state of the art clinical magnetic resonance imaging techniques to current situations in pre-clinical pancreatic imaging studies. In particular, the challenges of pre-clinical rodent pancreatic imaging are addressed, with both the image acquisition techniques and the post-processing methods for rodent pancreatic imaging elaborated.
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Affiliation(s)
- Ting Yin
- Department of Imaging and Pathology, Biomedical Sciences Group, KU Leuven, 3000 Leuven, Belgium
| | - Yewei Liu
- Department of Imaging and Pathology, Biomedical Sciences Group, KU Leuven, 3000 Leuven, Belgium
| | - Ronald Peeters
- Department of Imaging and Pathology, Biomedical Sciences Group, KU Leuven, 3000 Leuven, Belgium
| | - Yuanbo Feng
- Department of Imaging and Pathology, Biomedical Sciences Group, KU Leuven, 3000 Leuven, Belgium
| | - Yicheng Ni
- Department of Imaging and Pathology, Biomedical Sciences Group, KU Leuven, 3000 Leuven, Belgium
- Department of Radiology, University Hospitals, KU Leuven, 3000 Leuven, Belgium
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Reproducibility and relative stability in magnetic resonance imaging indices of tumor vascular physiology over a period of 24h in a rat 9L gliosarcoma model. Magn Reson Imaging 2017; 44:131-139. [PMID: 28887206 DOI: 10.1016/j.mri.2017.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 08/03/2017] [Accepted: 09/01/2017] [Indexed: 02/06/2023]
Abstract
PURPOSE The objective was to study temporal changes in tumor vascular physiological indices in a period of 24h in a 9L gliosarcoma rat model. METHODS Fischer-344 rats (N=14) were orthotopically implanted with 9L cells. At 2weeks post-implantation, they were imaged twice in a 24h interval using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Data-driven model-selection-based analysis was used to segment tumor regions with varying vascular permeability characteristics. The region with the maximum number of estimable parameters of vascular kinetics was chosen for comparison across the two time points. It provided estimates of three parameters for an MR contrast agent (MRCA): i) plasma volume (vp), ii) forward volumetric transfer constant (Ktrans) and interstitial volume fraction (ve, ratio of Ktrans to reverse transfer constant, kep). In addition, MRCA extracellular distribution volume (VD) was estimated in the tumor and its borders, along with tumor blood flow (TBF) and peritumoral MRCA flux. Descriptors of parametric distributions were compared between the two times. Tumor extent was examined by hematoxylin and eosin (H&E) staining. Picrosirus red staining of secreted collagen was performed as an additional index for 9L cells. RESULTS Test-retest differences between population summaries for any parameter were not significant (paired t and Wilcoxon signed rank tests). Bland-Altman plots showed no apparent trends between the differences and averages of the test-retest measures for all indices. The intraclass correlation coefficients showed moderate to almost perfect reproducibility for all of the parameters, except vp. H&E staining showed tumor infiltration in parenchyma, perivascular space and white matter tracts. Collagen staining was observed along the outer edges of main tumor mass. CONCLUSION The data suggest the relative stability of these MR indices of tumor microenvironment over a 24h duration in this gliosarcoma model.
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Duan C, Kallehauge JF, Pérez-Torres CJ, Bretthorst GL, Beeman SC, Tanderup K, Ackerman JJH, Garbow JR. Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF. Mol Imaging Biol 2017; 20:150-159. [PMID: 28536804 DOI: 10.1007/s11307-017-1090-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. PROCEDURES Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. RESULTS When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. CONCLUSIONS The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.
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Affiliation(s)
- Chong Duan
- Department of Chemistry, Washington University, Saint Louis, MO, USA
| | - Jesper F Kallehauge
- Department of Medical Physics, Aarhus University, Aarhus, Denmark.,Department of Oncology, Aarhus University, Aarhus, Denmark
| | - Carlos J Pérez-Torres
- Department of Radiology, Washington University, Saint Louis, MO, USA.,School of Health Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - G Larry Bretthorst
- Department of Radiation Oncology, Washington University, Saint Louis, MO, USA
| | - Scott C Beeman
- Department of Radiology, Washington University, Saint Louis, MO, USA
| | - Kari Tanderup
- Department of Oncology, Aarhus University, Aarhus, Denmark.,Department of Radiation Oncology, Washington University, Saint Louis, MO, USA.,Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Joseph J H Ackerman
- Department of Chemistry, Washington University, Saint Louis, MO, USA.,Department of Radiology, Washington University, Saint Louis, MO, USA.,Department of Medicine, Washington University, Saint Louis, MO, USA.,Alvin J Siteman Cancer Center, Washington University, Saint Louis, MO, USA
| | - Joel R Garbow
- Department of Radiology, Washington University, Saint Louis, MO, USA. .,Alvin J Siteman Cancer Center, Washington University, Saint Louis, MO, USA.
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Brix G, Salehi Ravesh M, Griebel J. Two-compartment modeling of tissue microcirculation revisited. Med Phys 2017; 44:1809-1822. [DOI: 10.1002/mp.12196] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 02/24/2017] [Accepted: 02/24/2017] [Indexed: 12/17/2022] Open
Affiliation(s)
- Gunnar Brix
- Department of Medical Radiation Protection; Federal Office for Radiation Protection; Ingolstädter Landstraße 1 D-85764 Oberschleissheim Germany
| | - Mona Salehi Ravesh
- Department of Congenital Heart Disease and Pediatric Cardiology; University Hospital Schleswig-Holstein; Arnold-Heller-Straße 3 D-24105 Kiel Germany
| | - Jürgen Griebel
- Department of Medical Radiation Protection; Federal Office for Radiation Protection; Ingolstädter Landstraße 1 D-85764 Oberschleissheim Germany
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Georgiou L, Sharma N, Broadbent DA, Wilson DJ, Dall BJ, Gangi A, Buckley DL. Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI. Magn Reson Med 2017; 79:317-326. [PMID: 28370289 DOI: 10.1002/mrm.26684] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 01/25/2017] [Accepted: 03/02/2017] [Indexed: 01/27/2023]
Abstract
PURPOSE To evaluate an interleaved MRI sampling strategy that acquires both high temporal resolution (HTR) dynamic contrast-enhanced (DCE) data for quantifying breast tumor blood flow (TBF) and high spatial resolution (HSR) DCE data for clinical reporting, following a single standard injection of contrast agent. METHODS A simulation study was used to evaluate the performance of the interleaved technique under different conditions. In a prospective clinical study, 18 patients with primary breast cancer, who were due to undergo neoadjuvant chemotherapy (NACT), were examined using interleaved HTR and HSR DCE-MRI at 1.5 Tesla. Tumor regions of interest were analyzed with a two-compartment tracer kinetic model. Paired parameters (n = 10) from the data acquired before and post-cycle 2 of NACT were compared using the nonparametric Wilcoxon signed-rank test. RESULTS Simulations demonstrated that TBF was reliably estimated using the proposed strategy. The region of interest analysis revealed significant changes in TBF (0.81-0.43 mL/min/mL; P = 0.002) following two cycles of NACT. The HSR data were reported in the normal way and enabled the assessment of tumor volume, which decreased by 53% following NACT (P = 0.065). CONCLUSIONS TBF can be measured reliably using the proposed strategy without compromising a standard clinical protocol. Furthermore, in our feasibility study, TBF decreased significantly following NACT, whereas capillary permeability surface-area product did not. Magn Reson Med 79:317-326, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Leonidas Georgiou
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom
| | - Nisha Sharma
- Department of Radiology, Leeds Teaching Hospital NHS Trust, Leeds, United Kingdom
| | - David A Broadbent
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Department of Medical Physics and Engineering, Leeds Teaching Hospital NHS Trust, Leeds, United Kingdom
| | - Daniel J Wilson
- Department of Medical Physics and Engineering, Leeds Teaching Hospital NHS Trust, Leeds, United Kingdom
| | - Barbara J Dall
- Department of Radiology, Leeds Teaching Hospital NHS Trust, Leeds, United Kingdom
| | - Anmol Gangi
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Western General Hospital, NHS Lothian, Edinburgh, United Kingdom
| | - David L Buckley
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom
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