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Yamauchi R, Itazawa T, Kobayashi T, Kashiyama S, Akimoto H, Mizuno N, Kawamori J. Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation. Med Dosim 2023; 49:167-176. [PMID: 38061916 DOI: 10.1016/j.meddos.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/03/2023] [Accepted: 11/02/2023] [Indexed: 08/04/2024]
Abstract
Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. Recently, deep learning auto-segmentation (DL-AS) algorithms have emerged as the state-of-the-art in medical image segmentation. This study aimed to evaluate the effect of auto-segmentation on the clinical workflow for contouring different anatomical sites of cancer, such as head and neck (H&N), breast, abdominal region, and prostate. Patients with H&N, breast, abdominal, and prostate cancer (n = 30 each) were enrolled in the study. Twenty-seven different organs at four sites were evaluated. RayStation was used to apply the ABAS. Siemens AI-Rad Companion Organs RT was used to apply the DL-AS. Evaluations were performed with similarity indices using geometric methods, time-evaluation, and qualitative scoring visual evaluations by radiation oncologists. The DL-AS algorithm was more accurate than ABAS algorithm on geometric indices for half of the structures. The qualitative scoring results of the two algorithms were significantly different, and DL-AS was more accurate on many contours. DL-AS had 41%, 29%, 86%, and 15% shorter edit times in the HnN, breast, abdomen, and prostate groups, respectively, than ABAS. There were no correlations between the geometric indices and visual assessments. The time required to edit the contours was considerably shorter for DL-AS than for ABAS. Auto-segmentation with deep learning could be the first step for clinical workflow optimization in radiotherapy.
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Affiliation(s)
- Ryohei Yamauchi
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan.
| | - Tomoko Itazawa
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Takako Kobayashi
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Shiho Kashiyama
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Japanese Red Cross Saitama Hospital, Saitama, Japan
| | - Hiroyoshi Akimoto
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Nippon Medical School Musashikosugi Hospital, Kanagawa, Japan
| | - Norifumi Mizuno
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Jiro Kawamori
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
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Tulip R, Manolopoulos S, Richmond N, Walker C. An evaluation of an energy independent CT reconstruction algorithm for use in radiotherapy treatment planning. Br J Radiol 2023; 96:20230004. [PMID: 37751165 PMCID: PMC10646643 DOI: 10.1259/bjr.20230004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE Radiotherapy treatment planning relies upon density information provided by CT for accurate dose calculations. Hounsfield units (HUs) are converted to electron/physical density via an energy dependant calibration curve. Multiple curves are required to make full use of the available accelerating potentials (kVp). The curves are bi-linear with a discontinuity occurring at soft-tissue densities. The commercial algorithm, DirectDensityTM (Siemens Healthcare GmbH), constructs a single calibration curve covering all available kVp. This enables the optimisation of the CT image quality, e.g. in terms of contrast, or the reduction of the imaging dose, whilst rendering the radiotherapy treatment dose calculation robust to the energy used to acquire the CT image. We report our investigations on the clinical utilisation of the DirectDensityTM algorithm for radiotherapy treatments, by using all accelerating potentials, i.e. from 70 kVp up to 140 kVp, available at our CT treatment simulator, in contrast to previous studies that were limited to accelerating potentials spanning a subset of the available kVp. METHODS The DirectDensityTM (DD) reconstruction algorithm available on a SOMATOM go.Open Pro CT scanner (Siemens Healthineers) was evaluated using the RayStation v. 9 treatment planning system (RaySearch Laboratories, Stockholm, Sweden) and a CIRS Model 002LFC IMRT Thorax Phantom (SunNuclear, Melbourne, FL), which was imaged at all available kVp with clinical protocols corresponding to various anatomical sites. The DD images were compared to those with the standard reconstruction algorithm acquired only at 120 kVp, as per our routine clinical practice. The effect of increasing kVp on HU is investigated for relevant tissue substitutes. In addition, a dosimetric comparison is performed for a VMAT plan technique with 6 MV X-rays using retrospective patient CT data sets representing four anatomical sites (pelvis, thorax, brain and "head and neck") with five patients for each site. The original dose distributions were calculated on images acquired at 120 kVp using the standard clinical iterative reconstruction (Qr40) and compared with dose distributions recalculated on images reconstructed with the new DD (Sm40) algorithm. RESULTS The maximum difference for radiotherapy doses calculated using images of the phantom reconstructed with Qr40 (120 kVp) or DD (all available kVp) was 0.73%. The patient plans on the anatomically representative sites studied here showed a mean PTV dose difference of -0.2% (s.d. 0.7) for D99%, -0.4% (s.d. 0.4%) for D50% and -0.3% (s.d. 0.4%) for D2%. Incidentally, we found a previously unreported decrease in HU, mostly notable for bone type inserts (~34 HU (cortical bone)), at 110 kVp for the DD reconstructed images. The effect was not noted for the standard Qr40 reconstructions. CONCLUSION DD has a minimal dosimetric impact in the dose calculations for radiotherapy treatments and could be implemented with existing clinical workflows. Attention should be paid to the HU values for images acquired at 110 kVp (DD algorithm), which warrants further investigation. ADVANCES IN KNOWLEDGE This is the first paper where DD was evaluated at all available kVp, leading to the incidental discovery of abnormal HU values at 110 kVp for this algorithm.
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Affiliation(s)
- Rachael Tulip
- Northern Centre for Cancer Care – North Cumbria, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Spyros Manolopoulos
- Northern Centre for Cancer Care – North Cumbria, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Neil Richmond
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Christopher Walker
- Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
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Decoene C, Crop F. Using density computed tomography images for photon dose calculations in radiation oncology: A patient study. Phys Imaging Radiat Oncol 2023; 27:100463. [PMID: 37497189 PMCID: PMC10366581 DOI: 10.1016/j.phro.2023.100463] [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: 03/21/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/28/2023] Open
Abstract
Background and purpose Conventional workflows for dose calculations require conversions between Hounsfield Units (HU) and the mass or electron density for Computed Tomography (CT) images in the Treatment Planning System (TPS). These conversions are scanner- and mostly kVp-dependent. A density representation or reconstruction at the CT level can potentially simplify the workflow. This study aimed to investigate the agreement between these two methods for patients and different calculation algorithms. Materials and methods Density conversions for conventional HU-density conversions were first established using two phantoms with appropriate inserts. Next, the differences in density and dose calculations between both methods were assessed using 95% Limits of Agreement (LOA) Bland-Altman analysis for 44 consecutive clinical patient cases. These cases represented a mix of indications, algorithms (collapsed cone, convolution superposition, ray tracing, finite-size pencil beam, and Monte Carlo), and scan kVp (80 to 140) in two different commercial TPS. Results No statistically significant bias in density or dose calculations was found between the two methods. Furthermore, 95% LOAs between both methods were ±0.05 g/cm3 and ±0.1 Gy for density and dose, respectively. Small but clinically irrelevant dose differences were found in high-density gradient regions for convolution superposition calculations or CT scans with non-delayed contrast agent injections with targets nearby vessels. Conclusions The in vivo density-reconstructed images at the CT level were assessed to be equivalent. Therefore, they can simplify and improve clinical workflows, allowing patient-specific acquisitions for contouring and density-reconstructed images for dose calculations.
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Affiliation(s)
- Camille Decoene
- Corresponding author at: Service of Medical physics, Centre Oscar Lambret, 3, Rue Frédéric Combemale, Lille 59000.
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Yasui K, Muramatsu R, Kamomae T, Toshito T, Kawabata F, Hayashi N. Evaluating the usefulness of the direct density reconstruction algorithm for intensity modulated and passively scattered proton therapy: Validation using an anthropomorphic phantom. Phys Med 2021; 92:95-101. [PMID: 34891108 DOI: 10.1016/j.ejmp.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/14/2021] [Accepted: 11/20/2021] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Accurate calculation of the proton beam range inside a patient is an important topic in proton therapy. In recent times, a computed tomography (CT) image reconstruction algorithm was developed for treatment planning to reduce the impact of the variation of the CT number with changes in imaging conditions. In this study, we investigated the usefulness of this new reconstruction algorithm (DirectDensity™: DD) in proton therapy based on its comparison with filtered back projection (FBP). METHODS We evaluated the effects of variations in the X-ray tube potential and target size on the FBP- and DD-image values and investigated the usefulness of the DD algorithm based on the range variations and dosimetric quantity variations. RESULTS For X-ray tube potential variations, the range variation in the case of FBP was up to 12.5 mm (20.8%), whereas that of DD was up to 3.3 mm (5.6%). Meanwhile, for target size variations, the range variation in the case of FBP was up to 2.2 mm (2.5%), whereas that of DD was up to 0.9 mm (1.4%). Moreover, the variations observed in the case of DD were smaller than those of FBP for all dosimetric quantities. CONCLUSION The dose distributions obtained using DD were more robust against variations in the CT imaging conditions (X-ray tube potential and target size) than those obtained using FBP, and the range variations were often less than the dose calculation grid (2 mm). Therefore, the DD algorithm is effective in a robust workflow and reduces uncertainty in range calculations.
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Affiliation(s)
- Keisuke Yasui
- Fujita Health University, Faculty of Radiological Technology, School of Health Sciences, 1-98 Dengakugakubo Kutsukake-cho, Toyoake, Aichi 470-1192, Japan.
| | - Rie Muramatsu
- Nagoya Proton Therapy Center, Nagoya City University West Medical Center, 1-1-1 Hirate-cho Kita-ku, Nagoya, Aichi 462-8508, Japan
| | - Takeshi Kamomae
- Nagoya University Hospital, 65 Tsuruma-cho Shouwa-ku, Nagoya, Aichi 466-8560, Japan
| | - Toshiyuki Toshito
- Nagoya Proton Therapy Center, Nagoya City University West Medical Center, 1-1-1 Hirate-cho Kita-ku, Nagoya, Aichi 462-8508, Japan
| | - Fumitaka Kawabata
- Nagoya University Hospital, 65 Tsuruma-cho Shouwa-ku, Nagoya, Aichi 466-8560, Japan
| | - Naoki Hayashi
- Fujita Health University, Faculty of Radiological Technology, School of Health Sciences, 1-98 Dengakugakubo Kutsukake-cho, Toyoake, Aichi 470-1192, Japan
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Fernandez-Velilla Cepria E, González-Ballester MÁ, Quera Jordana J, Pera O, Sanz Latiesas X, Foro Arnalot P, Membrive Conejo I, Rodriguez de Dios N, Reig Castillejo A, Algara Lopez M. Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters. Med Phys 2021; 48:5085-5095. [PMID: 34287956 DOI: 10.1002/mp.15120] [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: 03/10/2021] [Revised: 06/29/2021] [Accepted: 07/12/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Virtual monoenergetic images (VMI) obtained from Dual-Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at-risk (OAR). The energies used to vary from 40 to 70 keV, but noise at low keV and the use of Single Energy CT (SECT) at low kVp settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images. Our study proposes a procedure to determine this optimal range, based on common image quality parameters, and establishes this range in a Siemens Somatom Confidence and a Head and Neck protocol. METHODS We compared the quality of the VMI series at 40-60 keV versus single X-ray tube voltage computed tomography (SECT) at 80 and 120 kVp . Our reference was 120 kVp . DECT images were sequentially acquired using the Siemens Somatom Confidence RT Pro CT according to the head and neck protocol in our department. VMI series were constructed using the Syngo Via software Monoenergetic+ algorithm. Quality parameters were: image uniformity, high- and low-contrast resolution, noise, and sensitivity to the iodinated contrast. We used the Catphan 604 phantom for quality control, except when assessing iodine sensitivity. To evaluate high contrast resolution, we calculated the modulation transfer function (MTF) using the point spread function estimation of a point bead and the slanted edge methods. For the low-contrast resolution, we used a statistical method for assessing differences between contrast structures and local noise. To measure the absolute value of noise and compare its texture, we used the standard deviation and the noise power spectrum. We measured iodine sensitivity by dissolving the Optiray Ultraject iodinated contrast in water in concentrations of 0 to 4500 mg/l and then compared the contrast to noise ratio (CNR) and analyzed the linear correlation between concentration and HU. RESULTS The entire series met the minimum quality requirements. However, the one at 40 keV presented uniformity at the limits of acceptability. The high- and low-contrast resolutions were similar between series. The noise of the VMI series decreased with increasing energy, while sensitivity to the contrast displayed the opposite behavior. All series showed linearity of HUs from very low iodine concentrations. Images at 60 keV presented lower iodine sensitivity than SECT at 80 kVp , while those at 55 keV were similar to them. CONCLUSIONS Our method of image comparison based on standard quality parameters in phantom gave clear results about the optimal range and can be used as a guide to characterize any other DECT imaging protocols. The optimal range for using VMI images in iodinated contrasts in the Siemens system was 45-55 keV. Lower energies lacked noise and uniformity, while higher ones could be substituted by SECT images at low kilovoltage (80 kVp ).
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Affiliation(s)
- Enric Fernandez-Velilla Cepria
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Miguel Ángel González-Ballester
- Department of Information and Communication Technologies, BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain.,ICREA, Barcelona, Spain
| | - Jaume Quera Jordana
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Oscar Pera
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Xavier Sanz Latiesas
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Palmira Foro Arnalot
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Ismael Membrive Conejo
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Nuria Rodriguez de Dios
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Anna Reig Castillejo
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Manuel Algara Lopez
- Radiation Oncology Department, Hospital del Mar, Barcelona, Spain.,IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.,Universitat Autònoma de Barcelona, Barcelona, Spain
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