From computed tomography innovation to routine clinical application in radiation oncology – A joint initiative of close collaboration
Year: 2024
Type: article
Abstract: Since the clinical introduction of computed tomography (CT) more than 50 years ago [1Hounsfield G.N. Computerized transverse axial scanning (tomography): Part 1.Description of system. Br J Radiol. 1973; 46: 1016-1022https://doi.org/10.1259/0007-1285-46-552-1016Google Scholar, 2Schulz R.A. Stein J.A. Pelc N.J. How CT happened: the early development of medical computed tomography.J Med Imaging. 2021; 8https://doi.org/10.1117/1.JMI.8.5.052110Google Scholar], the technology has evolved tremendously and become indispensable in standard of care [3Hsieh J. Flohr T. Computed tomography recent history and future perspectives.J Med Imaging. 2021; 8https://doi.org/10.1117/1.JMI.8.5.052109Google Scholar, 4Booij R. Budde R.P.J. Dijkshoorn M.L. Van Straten M. Technological developments of X-ray computed tomography over half a century: User’s influence on protocol optimization.Eur J Radiol. 2020; 131109261https://doi.org/10.1016/j.ejrad.2020.109261Google Scholar]. The image quality and applicability of CT imaging has been improved by innovations like helical CT [[5]Kalender W.A. Polacin A. Physical performance characteristics of spiral CT scanning.Med Phys. 1991; 18: 910-915https://doi.org/10.1118/1.596607Google Scholar], multi-slice CT [[6]Dawson P. Lees W.R. Multi-slice Technology in Computed Tomography.Clin Radiol. 2001; 56: 302-309https://doi.org/10.1053/crad.2000.0651Google Scholar], time-resolved CT (4DCT) [[7]Rietzel E. Pan T. Chen G.T.Y. Four-dimensional computed tomography: Image formation and clinical protocol.Med Phys. 2005; 32: 874-889https://doi.org/10.1118/1.1869852Google Scholar], dual-energy CT (DECT) [[8]McCollough C.H. Leng S. Yu L. Fletcher J.G. Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications.Radiology. 2015; 276: 637-653https://doi.org/10.1148/radiol.2015142631Google Scholar], and recently photon-counting CT (PCCT) [[9]Flohr T. Schmidt B. Technical Basics and Clinical Benefits of Photon-Counting CT.Invest Radiol. 2023; 58: 441-450https://doi.org/10.1097/RLI.0000000000000980Google Scholar] – just to name a few examples. Most developments have been driven by various needs in diagnostic radiology to enable an individual optimization of CT acquisition and image reconstruction for each clinical case. The widespread clinical adoption of these CT innovations in radiotherapy (RT), however, is often delayed in time or has not even been pursued, because a change in CT workflows can affect the entire treatment chain, including therapeutic decision-making, tumour and organ segmentation, treatment planning, image-guided patient positioning [[10]Zhuang T. Gibbard G. Duan X. Tan J. Park Y. Lin M.-H. et al.Evaluation of fan-beam kilovoltage computed tomography image quality on a novel biological-guided radiotherapy platform.Phys Imaging Radiat Oncol. 2023; 26100438https://doi.org/10.1016/j.phro.2023.100438Google Scholar], as well as plan adaptation. Improvements of single tasks within an RT workflow through CT innovations are often deferred or downgraded in priority for the sake of simplicity, susceptibility to error, and generality of the whole workflow. Hence, only few (academic) RT departments with experienced staff perform the time-consuming commissioning of CT innovations without the help of detailed guidelines and recommendations. Even though CT imaging constitutes a vital part in RT, it is not always recognised as such. CT image quality is mainly discussed in the presence of artefacts and less during the initial setup of CT scan protocols, whereas a proactive consideration of CT capabilities can preserve image quality even in difficult cases, e.g. patients with large metal implants or irregular breathing motion [11Pettersson E. Bäck A. Björk-Eriksson T. Lindencrona U. Petruson K. Thilander-Klang A. Structure delineation in the presence of metal – A comparative phantom study using single and dual-energy computed tomography with and without metal artefact reduction.Phys Imaging Radiat Oncol. 2019; 9: 43-49https://doi.org/10.1016/j.phro.2019.01.001Google Scholar, 12Szkitsak J. Werner R. Fernolendt S. Schwarz A. Ott O.J. Fietkau R. et al.First clinical evaluation of breathing controlled four-dimensional computed tomography imaging.Phys Imaging Radiat Oncol. 2021; 20: 56-61https://doi.org/10.1016/j.phro.2021.09.005Google Scholar]. During the 4th ESTRO Physics workshop in 2021, an open exchange of clinical CT experiences and CT commissioning strategies between RT departments was identified as important cornerstone of standardisation for leveraging the benefits of CT in RT. The common understanding gained from inter-centre knowledge sharing can then be translated into guidelines and recommendations on how to implement CT innovations in routine practice. This joint initiative of close collaboration originating from an ESTRO Physics workshop has been transitioned into an active CT-in-RT working group with three focus areas: Respiratory 4DCT, DECT for delineation and DECT for dose calculation. Some of the results have already been published in this special issue [13Bakkali Tahiri J. Kyndt M. Dhont J. Gulyban A. Szkitsak J. Bogaert E. et al.A comprehensive quality assurance program for four-dimensional computed tomography in radiotherapy.Phys Imaging Radiat Oncol. 2023; 27100475https://doi.org/10.1016/j.phro.2023.100475Google Scholar, 14Burghelea M. Bakkali Tahiri J. Dhont J. Kyndt M. Gulyban A. Szkitsak J. et al.Results of a multicenter 4D computed tomography quality assurance audit: Evaluating image accuracy and consistency.Phys Imaging Radiat Oncol. 2023; 28100479https://doi.org/10.1016/j.phro.2023.100479Google Scholar]. Dose calculation in photon and proton therapy relies on an accurate and precise prediction of material parameters like electron density, mass density, or stopping power from CT numbers [[15]Schneider U. Pedroni E. Lomax A. The calibration of CT Hounsfield units for radiotherapy treatment planning.Phys Med Biol. 1996; 41: 111-124https://doi.org/10.1088/0031-9155/41/1/009Google Scholar]. Hence, variations in CT numbers of the same material directly translate into uncertainties in dose calculation. Several CT innovations, which improve the stability of CT numbers, have been widely implemented by now [[16]Decabooter E. Hilgers G.C. De Rouck J. Salvo K. Van Wingerden J. Bosmans H. et al.Survey on fan-beam computed tomography for radiotherapy: Imaging for dose calculation and delineation.Phys Imaging Radiat Oncol. 2024; 29100522https://doi.org/10.1016/j.phro.2023.100522Google Scholar]: Iterative image reconstruction for reducing image noise [[17]Beister M. Kolditz D. Kalender W.A. Iterative reconstruction methods in X-ray CT.Phys Med. 2012; 28: 94-108https://doi.org/10.1016/j.ejmp.2012.01.003Google Scholar]; Metal artefact reduction for mitigating streak artefacts caused by beam hardening, scattering and photon starvation near metal objects [[18]Giantsoudi D. De Man B. Verburg J. Trofimov A. Jin Y. Wang G. et al.Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction.Phys Med Biol. 2017; 62: R49-R80https://doi.org/10.1088/1361-6560/aa5293Google Scholar]; Beam hardening correction for reducing cupping artefacts and size dependencies of CT numbers in high-attenuating materials like bone or iodine [[19]Kyriakou Y. Meyer E. Prell D. Kachelrieß M. Empirical beam hardening correction (EBHC) for CT.Med Phys. 2010; 37: 5179-5187https://doi.org/10.1118/1.3477088Google Scholar]. However, the current inter-centre variation in stopping-power prediction from single-energy CT (SECT) using a Hounsfield look-up table (HLUT) still adds up to 2.5-3.0% in proton range as assessed by the European particle therapy network (EPTN) [20Taasti V.T. Bäumer C. Dahlgren C.V. Deisher A.J. Ellerbrock M. Free J. et al.Inter-centre variability of CT-based stopping-power prediction in particle therapy: Survey-based evaluation.Phys Imaging Radiat Oncol. 2018; 6: 25-30https://doi.org/10.1016/j.phro.2018.04.006Google Scholar, 21Peters N. Wohlfahrt P. Dahlgren C.V. De Marzi L. Ellerbrock M. Fracchiolla F. et al.Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy.Radiother Oncol. 2021; 163: 7-13https://doi.org/10.1016/j.radonc.2021.07.019Google Scholar]. To improve the overall performance, the CT-in-RT working group and EPTN decided to join forces for setting up a consensus guide on HLUT specification and evaluation [[22]Peters N. Trier Taasti V. Ackermann B. Bolsi A. Vallhagen Dahlgren C. Ellerbrock M. et al.Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy.Radiother Oncol. 2023; 184109675https://doi.org/10.1016/j.radonc.2023.109675Google Scholar] to encourage standardisation and facilitate HLUT commissioning in new proton centres. DECT is a promising CT acquisition technique and has been proven to be advantageous in RT [23Van Elmpt W. Landry G. Das M. Verhaegen F. Dual energy CT in radiotherapy: Current applications and future outlook.Radiother Oncol. 2016; 119: 137-144https://doi.org/10.1016/j.radonc.2016.02.026Google Scholar, 24Wohlfahrt P. Richter C. Status and innovations in pre-treatment CT imaging for proton therapy.Br J Radiol. 2020; 93: 20190590https://doi.org/10.1259/bjr.20190590Google Scholar, 25Paganetti H. Beltran C. Both S. Dong L. Flanz J. Furutani K. et al.Roadmap: proton therapy physics and biology.Phys Med Biol. 2021;66:05RM01.; https://doi.org/10.1088/1361-6560/abcd16Google Scholar, 26Yang M. Wohlfahrt P. Shen C. Bouchard H. Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential.Phys Med Biol. 2023;68:04TR01.; https://doi.org/10.1088/1361-6560/acabfaGoogle Scholar, 27Richter C, Wohlfahrt P. Dual-Energy CT in Radiation Oncology. In: Alkadhi H, Euler A, Maintz D, Sahani D, editors. Spectr. Imaging, Cham: Springer International Publishing; 2022, p. 333–46. https://doi.org/10.1007/978-3-030-96285-2_23.Google Scholar], both for delineation [28Noid G. Zhu J. Tai A. Mistry N. Schott D. Prah D. et al.Improving Structure Delineation for Radiation Therapy Planning Using Dual-Energy CT.Front Oncol. 2020; 10: 1694https://doi.org/10.3389/fonc.2020.01694Google Scholar, 29Kraft J. Lutyj P. Grabenbauer F. Ströhle S.-P. Tamihardja J. Razinskas G. et al.Assessment of dual-energy computed tomography derived virtual monoenergetic imaging for target volume delineation of brain metastases.Radiother Oncol. 2023; 187109840https://doi.org/10.1016/j.radonc.2023.109840Google Scholar] and dose calculation [30Näsmark T. Andersson J. The influence of dual-energy computed tomography image noise in proton therapy treatment planning.Phys Imaging Radiat Oncol. 2023; 28100493https://doi.org/10.1016/j.phro.2023.100493Google Scholar, 31Wohlfahrt P. Möhler C. Stützer K. Greilich S. Richter C. Dual-energy CT based proton range prediction in head and pelvic tumor patients.Radiother Oncol. 2017; 125: 526-533https://doi.org/10.1016/j.radonc.2017.09.042Google Scholar, 32Peters N. Wohlfahrt P. Hofmann C. Möhler C. Menkel S. Tschiche M. et al.Reduction of clinical safety margins in proton therapy enabled by the clinical implementation of dual-energy CT for direct stopping-power prediction.Radiother Oncol. 2022; 166: 71-78https://doi.org/10.1016/j.radonc.2021.11.002Google Scholar, 33Taasti V.T. Decabooter E. Eekers D. Compter I. Rinaldi I. Bogowicz M. et al.Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients.Br J Radiol. 2023; 96: 20230110https://doi.org/10.1259/bjr.20230110Google Scholar, 34Afifah M. Bulthuis M.C. Goudschaal K.N. Verbeek-Spijkerman J.M. Rosario T.S. Den Boer D. et al.Virtual unenhanced dual-energy computed tomography for photon radiotherapy: The effect on dose distribution and cone-beam computed tomography based position verification. Phys Imaging.Radiat Oncol. 2024; 100545https://doi.org/10.1016/j.phro.2024.100545Google Scholar]. However, the clinical transition from conventional CT to DECT is not only a simple change of scan protocols, but it impacts the entire RT chain from delineation and treatment planning to image registration at the treatment machine before dose delivery. Consequently, it is rarely used in routine RT workflows up to now [16Decabooter E. Hilgers G.C. De Rouck J. Salvo K. Van Wingerden J. Bosmans H. et al.Survey on fan-beam computed tomography for radiotherapy: Imaging for dose calculation and delineation.Phys Imaging Radiat Oncol. 2024; 29100522https://doi.org/10.1016/j.phro.2023.100522Google Scholar, 21Peters N. Wohlfahrt P. Dahlgren C.V. De Marzi L. Ellerbrock M. Fracchiolla F. et al.Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy.Radiother Oncol. 2021; 163: 7-13https://doi.org/10.1016/j.radonc.2021.07.019Google Scholar]. Most RT departments still use one single image dataset for all required RT tasks, whereas DECT can provide a huge variety of different datasets tailored to each clinical step [[27]Richter C, Wohlfahrt P. Dual-Energy CT in Radiation Oncology. In: Alkadhi H, Euler A, Maintz D, Sahani D, editors. Spectr. Imaging, Cham: Springer International Publishing; 2022, p. 333–46. https://doi.org/10.1007/978-3-030-96285-2_23.Google Scholar]: A virtual monoenergetic image (VMI) of low energy can enhance the tissue contrast for tumour and organ segmentation; A VMI of high energy can be beneficial to mitigate artifacts caused by beam hardening (e.g., weakly attenuating metallic implants); An iodine enhancement image can be used for perfusion assessment in tumour and healthy tissue; Material parameters such as relative electron density or stopping-power ratio can be directly derived from DECT and used for dose calculation. Hence, an efficient handling of multiple datasets needs to be established for DECT workflows to optimally benefit from its variety and to ensure the correct use of images for each task at hand. This can be verified in an end-to-end test during commissioning [[35]J. Edmund M. Feen Rønjom F.M. Van Overeem C. Maare A. Margrete Juul Dam E. Tsaggari et al. Split-filter dual energy computed tomography radiotherapy: From calibration to image guidance. Phys Imaging Radiat Oncol 2023;28:100495. 10.1016/j.phro.2023.100495Google Scholar]. Due to the many new possibilities offered by DECT, its clinical introduction will greatly benefit from knowledge-sharing, standardisation and guidance documents to ensure safe, smooth and efficient clinical procedures. In addition to CT innovations driven by diagnostic requirements, several CT applications with RT purpose were introduced recently. Special reconstruction kernels for SECT provide images with information on mass density or electron density independent from the selected tube voltage, which can be directly used for photon dose calculation without the need of voltage-specific HLUTs [16Decabooter E. Hilgers G.C. De Rouck J. Salvo K. Van Wingerden J. Bosmans H. et al.Survey on fan-beam computed tomography for radiotherapy: Imaging for dose calculation and delineation.Phys Imaging Radiat Oncol. 2024; 29100522https://doi.org/10.1016/j.phro.2023.100522Google Scholar, 36Grohmann M. Petersen C. Todorovic M. Benefits and considerations in using a novel computed tomography system optimized for radiotherapy planning.Phys Imaging Radiat Oncol. 2023; 28100510https://doi.org/10.1016/j.phro.2023.100510Google Scholar, 37Decoene C. Crop F. Using density computed tomography images for photon dose calculations in radiation oncology: A patient study.Phys Imaging Radiat Oncol. 2023; 27100463https://doi.org/10.1016/j.phro.2023.100463Google Scholar, 38Van Der Heyden B. Öllers M. Ritter A. Verhaegen F. Van Elmpt W. Clinical evaluation of a novel CT image reconstruction algorithm for direct dose calculations.Phys Imaging Radiat Oncol. 2017; 2: 11-16https://doi.org/10.1016/j.phro.2017.03.001Google Scholar]. This allows for optimising the image contrast by automatically selecting the most suited tube voltage for each patient and body region examined. The implementation of this feature is a first step towards a more patient-centred CT protocol with multiple image reconstructions tailored individually to the needs of delineation and dose calculation. A commissioning guide compiled by experienced RT departments can help facilitating its widespread clinical implementation [[39]Van Der Heyden B. Wohlfahrt P. Taasti V. Bogaert E. PO-1678 DirectDensity CT image reconstruction algorithm in clinical practice: a survey.Radiother Oncol. 2023; 182: S1384-S1385https://doi.org/10.1016/S0167-8140(23)66593-4Google Scholar]. Respiratory 4DCT is an essential CT acquisition technique in RT to assess the motion of moving targets and has experienced major improvements [40Werner R. Sentker T. Madesta F. Gauer T. Hofmann C. Intelligent 4D CT sequence scanning (i4DCT): Concept and performance evaluation.Med Phys. 2019; 46: 3462-3474https://doi.org/10.1002/mp.13632Google Scholar, 41Doi Y. Shimohigashi Y. Kai Y. Maruyama M. Toya R. Validation of four-dimensional computed tomography without external reference respiratory signals for radiation treatment planning of lung tumors.Biomed Phys Eng Express. 2022; 8055024https://doi.org/10.1088/2057-1976/ac8555Google Scholar]. However, irregular breathing patterns can still lead to severe image artefacts [[42]Yamamoto T. Langner U. Loo B.W. Shen J. Keall P.J. Retrospective Analysis of Artifacts in Four-Dimensional CT Images of 50 Abdominal and Thoracic Radiotherapy Patients.Int J Radiat Oncol. 2008; 72: 1250-1258https://doi.org/10.1016/j.ijrobp.2008.06.1937Google Scholar], hampering the assessment of tumour motion and dose calculation. With a recently available 4DCT scan mode, which automatically adapts the 4DCT acquisition according to the patient’s breathing pattern, artefacts caused by irregular breathing can be reduced [12Szkitsak J. Werner R. Fernolendt S. Schwarz A. Ott O.J. Fietkau R. et al.First clinical evaluation of breathing controlled four-dimensional computed tomography imaging.Phys Imaging Radiat Oncol. 2021; 20: 56-61https://doi.org/10.1016/j.phro.2021.09.005Google Scholar, 43Szkitsak J. Karius A. Hofmann C. Fietkau R. Bert C. Speer S. Quality assurance of a breathing controlled four-dimensional computed tomography algorithm.Phys Imaging Radiat Oncol. 2022; 23: 85-91https://doi.org/10.1016/j.phro.2022.06.007Google Scholar]. To assess the geometrical accuracy and precision of respiratory 4DCT imaging, a new standardised procedure for 4DCT quality assurance (QA) with an automated evaluation workflow has been proposed [[13]Bakkali Tahiri J. Kyndt M. Dhont J. Gulyban A. Szkitsak J. Bogaert E. et al.A comprehensive quality assurance program for four-dimensional computed tomography in radiotherapy.Phys Imaging Radiat Oncol. 2023; 27100475https://doi.org/10.1016/j.phro.2023.100475Google Scholar]. This QA procedure was successfully validated in twelve centres on CT scanners from three different vendors [[14]Burghelea M. Bakkali Tahiri J. Dhont J. Kyndt M. Gulyban A. Szkitsak J. et al.Results of a multicenter 4D computed tomography quality assurance audit: Evaluating image accuracy and consistency.Phys Imaging Radiat Oncol. 2023; 28100479https://doi.org/10.1016/j.phro.2023.100479Google Scholar]. According to the results of a recent survey among 31 RT departments [[44]Papalazarou C. Qamhiyeh S. Kaatee R. De Rouck J. Decabooter E. Hilgers G.C. et al.Survey on fan-beam computed tomography for radiotherapy: Current implementation and future perspectives of motion management and surface guidance devices.Phys Imaging Radiat Oncol. 2024; 29100523https://doi.org/10.1016/j.phro.2023.100523Google Scholar], 40% of all centres selected clear guidelines as the most important topic to handle the increasing complexity of CT in RT. This confirms the idea behind a joint initiative of close collaboration established by the CT-in-RT working group. The exchange of clinical experiences and mutual support between RT departments can facilitate the implementation of CT innovations and thus improve the quality of CT imaging before and during the course of treatment. A summary of best practices in consensus guides is another important result of multi-institutional collaboration to prevent time-consuming and error-prone implementations of CT innovations from scratch in each centre. This fruitful collaboration within the CT-in-RT working group will hopefully continue and provide further insights to transition more advanced CT imaging features from CT innovation to routine clinical application in radiation oncology. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Authors: Vicki Trier Taasti, Patrick Wohlfahrt
Institutions: Aarhus University Hospital, Siemens Healthineers (Germany)
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,680
OpenAlex ID: W4391656929