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Kadria-Vili Y, Schwartz JA, Polascik TJ, Goodrich GP, Jorden D, Pinder D, Halas NJ, Rastinehad AR. A Detailed Clinical Case of Localized Prostate Tumors Treated with Nanoparticle-Assisted Sub-Ablative Laser Ablation. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1261. [PMID: 39120366 PMCID: PMC11313886 DOI: 10.3390/nano14151261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/20/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
AuroLase® Therapy-a nanoparticle-enabled focal therapy-has the potential to safely and effectively treat localized prostate cancer (PCa), preserving baseline functionality. This article presents a detailed case of localized PCa treated with AuroLase, providing insight on expectations from the diagnosis of PCa to one year post-treatment. AuroLase Therapy is a two-day treatment consisting of a systemic infusion of gold nanoshells (~150-nm hydrodynamic diameter) on Day 1, and sub-ablative laser treatment on Day 2. Multiparametric MRI (mpMRI) was used for tumor visualization, treatment planning, and therapy response assessment. The PCa was targeted with a MR/Ultrasound-fusion (MR/US) transperineal approach. Successful treatment was confirmed at 6 and 12 months post-treatment by the absence of disease in MR/US targeted biopsies. On the mpMRI, confined void space was evident, an indication of necrotic tissues encompassing the treated lesion, which was completely resolved at 12 months, forming a band-like scar with no evidence of recurrent tumor. The patient's urinary and sexual functions were unchanged. During the one-year follow-up, changes on the DCE sequence and in the Ktrans and ADC values assist in qualitatively and quantitatively evaluating tissue changes. The results highlight the potential of gold-nanoparticle-enabled sub-ablative laser treatment to target and control localized PCa, maintain quality of life, and preserve baseline functionality.
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Affiliation(s)
- Yara Kadria-Vili
- Nanospectra Biosciences Inc., Houston, TX 77054, USA; (Y.K.-V.); (D.J.)
| | - Jon A. Schwartz
- Nanospectra Biosciences Inc., Houston, TX 77054, USA; (Y.K.-V.); (D.J.)
| | - Thomas J. Polascik
- Department of Urology, Duke University Medical Center, Durham, NC 27710, USA; (T.J.P.)
| | - Glenn P. Goodrich
- Nanospectra Biosciences Inc., Houston, TX 77054, USA; (Y.K.-V.); (D.J.)
| | - David Jorden
- Nanospectra Biosciences Inc., Houston, TX 77054, USA; (Y.K.-V.); (D.J.)
| | - Diane Pinder
- Department of Urology, Duke University Medical Center, Durham, NC 27710, USA; (T.J.P.)
| | - Naomi J. Halas
- Laboratory for Nanophotonics, Rice University, 6100 Main Street, Houston, TX 77005, USA
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX 77005, USA
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
- Department of Physics and Astronomy, Rice University, 6100 Main Street, Houston, TX 77005, USA;
| | - Ardeshir R. Rastinehad
- Smith Institute for Urology at Lenox Hill Hospital, Northwell Health, Zucker School of Medicine at Hofstra/Northwell, New York, NY 10075, USA
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Tham BZ, Aleman D, Nordström H, Nygren N, Coolens C. Plan Assessment Metrics for Dose Painting in Stereotactic Radiosurgery. Adv Radiat Oncol 2023; 8:101281. [PMID: 37415903 PMCID: PMC10320410 DOI: 10.1016/j.adro.2023.101281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/23/2023] [Indexed: 07/08/2023] Open
Abstract
Purpose As radiation therapy treatment precision increases with advancements in imaging and radiation delivery, dose painting treatment becomes increasingly feasible, where targets receive a nonuniform radiation dose. The high precision of stereotactic radiosurgery (SRS) makes it a good candidate for dose painting treatments, but no suitable metrics to assess dose painting SRS plans exist. Existing dose painting assessment metrics weigh target overdose and underdose equally but are unsuited for SRS plans, which typically avoid target underdose more. Current SRS metrics also prioritize reducing healthy tissue dose through selectivity and dose fall-off, and these metrics assume single prescriptions. We propose a set of metrics for dose painting SRS that would meet clinical needs and are calculated with nonuniform dose painting prescriptions. Methods and Materials Sample dose painting SRS prescriptions are first created from Gamma Knife SRS cases, apparent diffusion coefficient magnetic resonance images, and various image-to-prescription functions. Treatment plans are found through semi-infinite linear programming optimization and using clinically determined isocenters, then assessed with existing and proposed metrics. Modified versions of SRS metrics are proposed, including coverage, selectivity, conformity, efficiency, and gradient indices. Quality factor, a current dose painting metric, is applied both without changes and with modifications. A new metric, integral dose ratio, is proposed as a measure of target overdose. Results The merits of existing and modified metrics are demonstrated and discussed. A modified conformity index using mean or minimum prescription dose would be suitable for dose painting SRS with integral or maximum boost methods, respectively. Either modified efficiency index is a suitable replacement for the existing gradient index. Conclusions The proposed modified SRS metrics are appropriate measures of plan quality for dose painting SRS plans and have the advantage of giving equal values as the original SRS metrics when applied to single-prescription plans.
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Affiliation(s)
- Benjamin Z. Tham
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Dionne Aleman
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Catherine Coolens
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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Sinno N, Taylor E, Hompland T, Milosevic M, Jaffray DA, Coolens C. Incorporating cross-voxel exchange for the analysis of dynamic contrast-enhanced imaging data: pre-clinical results. Phys Med Biol 2022; 67. [PMID: 36541560 DOI: 10.1088/1361-6560/aca512] [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: 12/10/2021] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Tumours exhibit abnormal interstitial structures and vasculature function often leading to impaired and heterogeneous drug delivery. The disproportionate spatial accumulation of a drug in the interstitium is determined by several microenvironmental properties (blood vessel distribution and permeability, gradients in the interstitial fluid pressure). Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the quantification of tracer perfusion based on contrast enhancement measured with non-invasive imaging techniques. An advanced cross-voxel exchange model (CVXM) was recently developed to provide a comprehensive description of tracer extravasation as well as advection and diffusion based on cross-voxel tracer kinetics (Sinnoet al2021). Transport parameters were derived from DCE-MRI of twenty TS-415 human cervical carcinoma xenografts by using CVXM. Tracer velocity flows were measured at the tumour periphery (mean 1.78-5.82μm.s-1) pushing the contrast outward towards normal tissue. These elevated velocity measures and extravasation rates explain the heterogeneous distribution of tracer across the tumour and its accumulation at the periphery. Significant values for diffusivity were deduced across the tumours (mean 152-499μm2.s-1). CVXM resulted in generally smaller values for the extravasation parameterKext(mean 0.01-0.04 min-1) and extravascular extracellular volume fractionve(mean 0.05-0.17) compared to the standard Tofts parameters, suggesting that Toft model underestimates the effects of inter-voxel exchange. The ratio of Tofts' extravasation parameters over CVXM's was significantly positively correlated to the cross-voxel diffusivity (P< 0.0001) and velocity (P= 0.0005). Tofts' increasedvemeasurements were explained using Sinnoet al(2021)'s theoretical work. Finally, a scan time of 15 min renders informative estimations of the transport parameters. However, a duration as low as 7.5 min is acceptable to recognize the spatial variation of transport parameters. The results demonstrate the potential of utilizing CVXM for determining metrics characterizing the exchange of tracer between the vasculature and the tumour tissue. Like for many earlier models, additional work is strongly recommended, in terms of validation, to develop more confidence in the results, motivating future laboratory work in this regard.
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Affiliation(s)
- Noha Sinno
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Edward Taylor
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada
| | - Tord Hompland
- Department of Radiation Biology, Oslo University Hospital, Oslo, Norway
| | - Michael Milosevic
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada
| | - David A Jaffray
- The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,University of Texas, MD Anderson Cancer Centre, Texas, United States of America
| | - Catherine Coolens
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,The Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
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Sinno N, Taylor E, Milosevic M, Jaffray DA, Coolens C. Incorporating cross-voxel exchange into the analysis of dynamic contrast-enhanced imaging data: theory, simulations and experimental results. Phys Med Biol 2021; 66. [PMID: 34650009 DOI: 10.1088/1361-6560/ac2205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/27/2021] [Indexed: 12/26/2022]
Abstract
Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the estimation of perfusion properties of tumour tissues but many assume no dispersion associated with tracer transport away from the capillaries and through the tissue. At the level of a voxel, this translates to assuming no cross-voxel tracer exchange, often leading to the misinterpretation of derived perfusion parameters. Tofts model (TM), a compartmental model widely used in oncology, also makes this assumption. A more realistic description is required to quantify kinetic properties of tracers, such as convection and diffusion. We propose a Cross-Voxel Exchange Model (CVXM) for analysing cross-voxel tracer kinetics.In silicodatasets quantifying the roles of convection and diffusion in tracer transport (which TM ignores) were employed to investigate the interpretation of Tofts' perfusion parameters compared to CVXM. TM returned inaccurate values ofKtransandvewhere diffusive and convective mechanisms are pronounced (up to 20% and 300% error respectively). A mathematical equation, developed in this work, predicts and gives the correct physiological interpretation of Tofts've.Finally, transport parameters were derived from dynamic contrast enhanced-magnetic resonance imaging of a TS-415 human cervical carcinoma xenograft by using TM and CVXM. The latter deduced lower values ofKtransandvecompared to TM (lower by up to 63% and 76% respectively). It also allowed the detection of a diffusive flux (mean diffusivity 155μm2s-1) in the tumour tissue, as well as an increased convective flow at the periphery (mean velocity 2.3μm s-1detected). The results serve as a proof of concept establishing the feasibility of using CVXM for accurately determining transport metrics that characterize the exchange of tracer between voxels. CVXM needs to be investigated further as its parameters can be linked to the tumour microenvironment properties (permeability, pressure…), potentially leading to enhanced personalized treatment planning.
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Affiliation(s)
- Noha Sinno
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Edward Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada
| | - Michael Milosevic
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Institute of Medical Science, University of Toronto, Toronto, Canada
| | - David A Jaffray
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada.,University of Texas, MD Anderson Cancer Centre, Texas, United States of America
| | - Catherine Coolens
- The Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,TECHNA Institute, University Health Network, Toronto, Canada
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van Houdt PJ, Saeed H, Thorwarth D, Fuller CD, Hall WA, McDonald BA, Shukla-Dave A, Kooreman ES, Philippens MEP, van Lier ALHMW, Keesman R, Mahmood F, Coolens C, Stanescu T, Wang J, Tyagi N, Wetscherek A, van der Heide UA. Integration of quantitative imaging biomarkers in clinical trials for MR-guided radiotherapy: Conceptual guidance for multicentre studies from the MR-Linac Consortium Imaging Biomarker Working Group. Eur J Cancer 2021; 153:64-71. [PMID: 34144436 PMCID: PMC8340311 DOI: 10.1016/j.ejca.2021.04.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/14/2022]
Abstract
Quantitative imaging biomarkers (QIBs) derived from MRI techniques have the potential to be used for the personalised treatment of cancer patients. However, large-scale data are missing to validate their added value in clinical practice. Integrated MRI-guided radiotherapy (MRIgRT) systems, such as hybrid MRI-linear accelerators, have the unique advantage that MR images can be acquired during every treatment session. This means that high-frequency imaging of QIBs becomes feasible with reduced patient burden, logistical challenges, and costs compared to extra scan sessions. A wealth of valuable data will be collected before and during treatment, creating new opportunities to advance QIB research at large. The aim of this paper is to present a roadmap towards the clinical use of QIBs on MRIgRT systems. The most important need is to gather and understand how the QIBs collected during MRIgRT correlate with clinical outcomes. As the integrated MRI scanner differs from traditional MRI scanners, technical validation is an important aspect of this roadmap. We propose to integrate technical validation with clinical trials by the addition of a quality assurance procedure at the start of a trial, the acquisition of in vivo test-retest data to assess the repeatability, as well as a comparison between QIBs from MRIgRT systems and diagnostic MRI systems to assess the reproducibility. These data can be collected with limited extra time for the patient. With integration of technical validation in clinical trials, the results of these trials derived on MRIgRT systems will also be applicable for measurements on other MRI systems.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 102, Amsterdam, 1066CX, the Netherlands.
| | - Hina Saeed
- Department of Radiation Oncology, Medical College of Wisconsin, 9200 W Wisconsin Av, Milwaukee, WI, 53226, USA.
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Hoppe-Seyler-Str. 3, Tübingen, 72076, Germany.
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 0097, Houston, TX, 77030, USA.
| | - William A Hall
- Department of Radiation Oncology, Medical College of Wisconsin, 9200 W Wisconsin Av, Milwaukee, WI, 53226, USA.
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 0097, Houston, TX, 77030, USA.
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| | - Ernst S Kooreman
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 102, Amsterdam, 1066CX, the Netherlands.
| | - Marielle E P Philippens
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
| | - Astrid L H M W van Lier
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
| | - Rick Keesman
- Department of Radiation Oncology, Radboud University Medical Center, Geert Grooteplein Zuid 32, Nijmegen, 6525GA, the Netherlands.
| | - Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Kløvervænget 19, Odense C, 5000, Denmark; Department of Clinical Research, University of Southern Denmark, J. B. Winsløws Vej 19.3, Odense C, 5000, Denmark.
| | - Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, 700 University Avenue, Toronto, Ontario, M5M 1G7, Canada.
| | - Teodor Stanescu
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, 700 University Avenue, Toronto, Ontario, M5M 1G7, Canada; Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 0097, Houston, TX, 77030, USA.
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, 15 Cotswold Road, London, SM2 5NG, United Kingdom.
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 102, Amsterdam, 1066CX, the Netherlands.
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Coolens C, Gwilliam MN, Alcaide-Leon P, de Freitas Faria IM, Ynoe de Moraes F. Transformational Role of Medical Imaging in (Radiation) Oncology. Cancers (Basel) 2021; 13:cancers13112557. [PMID: 34070984 PMCID: PMC8197089 DOI: 10.3390/cancers13112557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints. Abstract Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.
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Affiliation(s)
- Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- TECHNA Institute, University Health Network, Toronto, ON M5G 1Z5, Canada
- Correspondence:
| | - Matt N. Gwilliam
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
| | - Paula Alcaide-Leon
- Joint Department of Medical Imaging, University Health Network, Toronto, ON M5G 1Z5, Canada;
| | | | - Fabio Ynoe de Moraes
- Department of Oncology, Division of Radiation Oncology, Queen’s University, Kingston, ON K7L 5P9, Canada;
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Svistoun I, Driscoll B, Coolens C. Accuracy and Performance of Functional Parameter Estimation Using a Novel Numerical Optimization Approach for GPU-Based Kinetic Compartmental Modeling. ACTA ACUST UNITED AC 2019; 5:209-219. [PMID: 30854459 PMCID: PMC6403032 DOI: 10.18383/j.tom.2018.00048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Quantitative kinetic parameters derived from dynamic contrast-enhanced (DCE) data are dependent on signal measurement quality and choice of pharmacokinetic model. However, the fundamental optimization analysis method is equally important and its impact on pharmacokinetic parameters has been mostly overlooked. We examine the effects of those choices on accuracy and performance of parameter estimation using both computer processing unit and graphical processing unit (GPU) numerical optimization implementations and evaluate the improvements offered by a novel optimization approach. A test framework was developed where experimentally derived population-average arterial input function and randomly sampled parameter sets {Ktrans, Kep, Vb, τ} were used to generate known tissue curves. Five numerical optimization algorithms were evaluated: sequential quadratic programming, downhill simplex (Nelder–Mead), pattern search, simulated annealing, and differential evolution. This was combined with various objective function implementation details: delay approximation, discretization and varying sampling rates. Then, impact of noise and CPU/GPU implementation was tested for speed and accuracy. Finally, the optimal method was compared to conventional implementation as applied to clinical DCE computed tomography. Nelder–Mead, differential evolution and sequential quadratic programming produced good results on clean and noisy input data outperforming simulated annealing and pattern search in terms of speed and accuracy in the respective order of 10−8%, 10−7%, and ×10−6%). A novel approach for DCE numerical optimization (infinite impulse response with fractional delay approximation) was implemented on GPU for speed increase of at least 2 orders of magnitude. Applied to clinical data, the magnitude of overall parameter error was <10%.
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Affiliation(s)
- Igor Svistoun
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, Toronto, Canada
| | - Brandon Driscoll
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, Toronto, Canada
| | - Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre and University Health Network, Toronto, Canada.,Departments of Radiation Oncology and IBBME, University of Toronto, Toronto, Canada; and.,TECHNA Institute, University Health Network, Toronto, Canada
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