Research ArticleClinical Studies

Optimization of Fluence Map for CyberKnife Raster Scanning Intensity Modulated Radiotherapy

YUICHI AKINO, HIROYA SHIOMI, NOBUHISA MABUCHI, NORIHISA MASAI, RYOONG-JIN OH and KAZUHIKO OGAWA
Anticancer Research April 2023, 43 (4) 1637-1642; DOI: https://doi.org/10.21873/anticanres.16314

Abstract

Background/Aim: Stereotactic body radiotherapy for prostate cancer using CyberKnife with circular cone requires a long treatment time. Raster scanning intensity modulated radiotherapy (RS-IMRT) has a potential of improving treatment efficacy, introducing shorter treatment time, better target dose uniformity, and lower organ at risk (OAR) dose. The purpose of the study was to develop a fluence optimization system for RS-IMRT. Patients and Methods: RS-IMRT plans were created for five prostate cancer patients treated with the Novalis system and parameters were compared to the Novalis treatment plans. From 80 nodes available for the CyberKnife, twelve nodes were arbitrarily selected. On the beam’s eye view of each beam, a 100×100 matrix of optimization points was created at the target center plane. The beam fluence map was optimized using the attraction-repulsion model (ARM). The beam fluence maps were converted to the scanning sequence using the ARM and a final dose calculation was performed. Results: For planning target volume (PTV), RS-IMRT plans showed higher dose covering 2% of the volume (D2%) and lower D98% compared to the Novalis plans. However, the homogeneity was within our Institutional clinical protocol. The RS-IMRT plans showed significantly lower OAR dose parameters including bladder volume receiving 100% of prescribed dose (V100%) and dose delivered to 5 cm3 of rectum (D5 cc). Conclusion: We developed a fluence optimization system for RS-IMRT that performs the entire RS-IMRT treatment planning process, including scanning sequence optimization and final dose calculation. The RS-IMRT was capable of generating clinically acceptable plans.

Key Words:

During the last decade, the use of stereotactic body radiotherapy (SBRT) with extreme hypofractionation for prostate cancer has been rapidly increasing owing to excellent clinical outcomes and cost-effectiveness (1-4). Several clinical trials have reported prostate-specific antigen (PSA) control outcomes of approximately 95% and low incidence of grade 3 and higher toxicities for low-risk prostate cancer (5-8). To reduce toxicity of organs at risk (OARs), high conformal dose distribution to the target and accurate beam delivery are essential. CyberKnife robotic radiosurgery system (Accuray Inc., Sunnyvale, CA, USA) has a linear accelerator mounted on a robotic arm to achieve conformal irradiation from multiple noncoplanar beam angles (9). Additionally, the orthogonal kilovoltage imaging system detects fiducial markers implanted in the prostate, and the 6-degree robotic couch allows for precise beam delivery.

The latest version of CyberKnife is available with InCise, a multi-leaf collimator (MLC) device (10, 11). Many studies have reported that InCise has capabilities of treating patients in a shorter treatment time compared to circular cones (12, 13). However, many Institutions still treat patients with the circular cones. Previously, we reported a concept of raster scanning intensity-modulated radiotherapy (RS-IMRT), a new scanning irradiation method for the CyberKnife (14). Although this was a simulation study, the scanning irradiation will be possible because the CyberKnife has great potential of dynamic robotic arm movement and beam delivery to enable motion tracking treatment for lung and liver tumors (15, 16). The scanning sequences were generated by mimicking the beam fluence maps of intensity modulated radiotherapy (IMRT) plans of a Novalis radiosurgery system (BrainLAB, Munich, Germany). The RS-IMRT showed potential in generating dose distribution comparable to the Novalis plans in a shorter treatment time compared to the sequential treatment with the CyberKnife. With small cone size, RS-IMRT showed more homogeneous target dose coverage and lower OAR dose compared to CyberKnife sequential plans (14). This technology has the potential of improving treatment efficacy of the CyberKnife in terms of short treatment time and better dose distribution without additional MLC device.

The purpose of this study was to develop an optimization system of the fluence maps for RS-IMRT. This work aims to deliver a planning system capable of performing entire planning processes of RS-IMRT without interference of commercial treatment planning systems (TPS).

Patients and Methods

Patients and treatment plans. Following Institutional Review Board approval, we analyzed data from five prostate cancer patients treated with the Novalis system, which equips a M3 microMLC with 3 mm leaf width. The patients were treated with a 6 MV photon beams from eight coplanar gantry angles. The treatment plans were developed according to our Institution’s protocol: the prescribed dose was 36.25 Gy/5 fr, normalized by the dose covering 95% of planning target volume (PTV D95%)=prescribed dose. Prescribed dose was targeted at 80% of PTV maximum dose (Dmax), with 70-90% being acceptable. Rectum was limited to dose delivered to 2 cm3 (D2 cc) <35 Gy, D5 cc <30 Gy, and volume receiving 50% of the organ (V50%) <40%. The bladder was limited to D10 cc <35 Gy, V50% <35 cm3, and V100% <5 cm3. Urethral dose was not evaluated in this study because it was not evaluated in the Novalis treatment plans. The prescribed dose of the Novalis plans were 72 Gy/30 fr. To evaluate the ability of the scanning irradiation developed in this study, the clinically used Novalis dose distribution was analyzed by rescaling to PTV D95%=36.25 Gy.

Raster scanning. Figure 1 shows a flow chart for RS-IMRT treatment planning. Targets, OARs, and shells for optimization were generated using iPLAN RT Image (ver. 4.1.2) TPS. Clinical target volume (CTV) was created with a 3 mm margin on the prostate, whereas 1 mm in posterior. PTV was created with a 2 mm margin on CTV. A 5 mm thick shell was created for optimization at 5 mm from the PTV.

Figure 1.
Figure 1.

Flowchart of treatment planning for the RS-IMRT. RS-IMRT: Raster-scanning intensity modulated radiotherapy; OAR: organ at risk.

The CyberKnife G4 system with a variable aperture Iris collimator (Accuray Inc.) was simulated in this study. For prostate cancer treatment, CyberKnife irradiation is possible from approximately 80 nodes. From these, twelve nodes were arbitrarily selected for treatment planning (Figure 2A). The nodes were selected so that a sufficient angle was opened between each beam. A 100×100-pixel beamlet was created on the beams-eye-view (BEV) of each beam. The resolution was set to 1 mm at the target center plane. An attraction-repulsion model (ARM) (17) was used to optimize the beamlets (Figure 2B). Each of the 100×100 pixels created for the 12 beams works as an optimization point, generating either attraction or repulsion. If the PTV dose is insufficient, the optimized point in the PTV generate an attraction force to increase the beam fluence. If the PTV dose is excessive, the optimization points generate repulsion force to decrease the beam fluence. If the OAR dose is excessive, the optimized points in the OAR generate a repulsive force to decrease the beam fluence.

Figure 2.
Figure 2.

An example case of treatment planning of the raster scanning IMRT: (A) Selection of nodes used for the treatment. (B) Optimization of fluence map. Red, green, and blue lines represent the differential DVH curves of PTV, rectum, and bladder, respectively. (C) Conversion of the fluence map to the scanning sequence. Spacing of allows represent the scanning speed. IMRT: Intensity-modulated radiotherapy; DVH: dose-volume histogram; PTV: planning target volume.

The obtained fluence map was converted to a scanning beam sequence for circular cone (Figure 2C). In this study, cones with a diameter of 10 mm were used. Control points were equally spaced at 5 mm intervals, half the cone diameter, and the dose rate at each control point was optimized using the ARM. Each optimization point generates an attraction and a repulsion force corresponding to the fluence map, which increases or decreases the dose rate of the scanning irradiation. The detail of the optimization of the scanning sequence is described elsewhere (14).

Three-dimensional dose distributions were calculated with the pencil beam algorithm using a ShioRIS 2.0 software. The calculation method and accuracy are described elsewhere (18). The dose distribution was normalized so that PTV D95%=prescription dose.

Evaluation. Dose-volume histogram (DVH) was calculated for PTV, bladder, and rectum; for PTV, D2%, D98%, and homogeneity index (HI) were evaluated (19). For bladder and rectum, DVH parameters were evaluated as clinical constraints and generalized equivalent uniform dose (gEUD) were calculated. HI and gEUD were calculated by the following equations:

Embedded Image (1)

Embedded Image (2)

where n is a parameter representing the volume dependence of the dose-response relationship for each organ. n values were 0.12 and 0.5 for the rectum and bladder, respectively, as previously reported by Burman et al. (20).

Statistical analysis. Differences between Novalis and RS-IMRT plans were compared using the paired t-test. Statistical significance was defined as a p-Value <0.05.

Results

The RS-IMRT plans were generated for five patients. The PTV were 64.1, 122.7, 74.2, 111.3, and 62.6 cm3 for patient #1, 2, 3, 4, and 5, respectively. Mean±standard deviation of the number of control points were 133,350±34,933. For all cases, the optimization of the fluence map completed within 10 s.

Figure 3A shows an example of dose distribution calculated with RS-IMRT, and Figure 3B shows an DVH of an example case. The solid and dash lines represent the RS-IMRT and Novalis plans, respectively. The Novalis plan showed steeper DVH curve of PTV than that of RS-IMRT, indicating better dose uniformity. On the other hand, in this case, the scanning irradiation plan achieved lower doses in the high-dose areas of the bladder and rectum.

Figure 3.
Figure 3.

(A) An example dose distribution of RS-IMRT. (B) DVH of an example case (#5). Solid and dash lines represent RS-IMRT and Novalis plans, respectively. RS-IMRT: Raster-scanning intensity-modulated radiotherapy; DVH, dose-volume histogram; PTV: planning target volume.

Figure 4 shows the results for the DVH parameters. The mean (min-max) values for each data are listed in Table I. The RS-IMRT showed significantly lower target homogeneity compared to Novalis. However, the values were within the tolerance of our clinical protocol. RS-IMRT showed significantly lower bladder V100%. and rectum D5 cc. For other parameters, significantly differences were not observed.

Figure 4.
Figure 4.

DVH parameters of Novalis and RS-IMRT plans calculated for each patient. DVH: Dose-volume histogram; RS-IMRT: raster-scanning intensity-modulated radiotherapy; PTV: planning target volume; Dx% (or x cc): dose delivered to the volume of x% (or x cm3); V100%: volume which receives 100% prescribed dose.

View this table:
Table I.

DVH parameters of Novalis and RS-IMRT plans.

Discussion

In this study, we developed an optimization system for RS-IMRT for prostate SBRT. In our previous study, we demonstrated that the RS-IMRT achieved good dose distribution with short treatment time (14). However, in the previous study, the scan sequence was created by mimicking the IMRT fluence map created by iPLAN TPS. The treatment planning could not be performed only with the system alone. In the present study, we simulated beams from nodes that can actually be irradiated with the CyberKnife and created plans with head motion velocities in the range feasible with the CyberKnife. This study enabled a more realistic simulation, and the system developed in this study enabled to optimize the fluence, convert it to a scanning sequence, and perform dose calculation with this system. If this technology is incorporated into the CyberKnife, Institutions without InCise MLC would be able to provide better treatment to patients.

The RS-IMRT plans showed superior OAR dose reduction compared to the Novalis plans. This would be due to the improved dose conformity achieved by using noncoplanar beams. In the previous study, Novalis plans showed lower rectal and bladder doses compared to the RS-IMRT plans, probably because the scanning beam was designed to reproduce the fluence map of Novalis plan and thus could not produce a better plan than the Novalis plan.

On the other hand, the uniformity of PTV is significantly worse than that of Novalis plans. This would be because the beamlets produced by Novalis are made of MLCs and have no joints, whereas the beamlets produced by raster scanning are made of cones overlapping by half their diameter, so the boundaries between beams are not uniform, even though they overlap. Furthermore, because the control point is set at the center plane of the target, the size of the cone is smaller upstream of the beam, which is expected to increase inhomogeneity. However, the dose uniformity of the PTV was within our criteria, and the dose distribution seemed clinically acceptable.

Tomida et al. and Masi et al. previously reported that InCize MLC shortens treatment time by reducing monitor units (MU) and nodes compared to the variable aperture Iris collimator (12, 13). Furthermore, a new optimization system, VOLO, has been reported to reduce the number of nodes and MUs compared to the conventional sequential optimization, enabling high quality treatment in a short time (21-24).

RS-IMRT also achieves good dose distribution with a much smaller number of nodes. In addition, as previously reported, combining a static beam with a large cone diameter significantly shorten the treatment time (14). This technique would be useful for improving treatment efficacy for facilities which do not obtain MLC-equipped machine.

The limitation of this study is that it is a virtual simulation and the current version of the CyberKnife cannot perform such a movement. However, CyberKnife itself has sufficient potential of performing scanning irradiation. The RS-IMRT would become possible if the software is updated to support this.

Conclusion

In this study, we developed an optimization system for RS-IMRT in SBRT for prostate cancer. Our approach allowed the entire planning process of fluence optimization, conversion to scanning sequences, and final dose calculation to be completed within the system. RS-IMRT produced a clinically acceptable treatment plan with fewer nodes than sequential treatment plans. Compared to sequential treatment, the CyberKnife IMRT planning system proposed herein has therapeutic advantages of: (i) shorter treatment times due to fewer nodes, (ii) better uniformity of target dose, and (iii) lower OAR dose. The proposed RS-IMRT technique has the potential to provide a treatment showcasing these benefits without an additional MLC device.

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP20K08022.

Footnotes

  • Authors’ Contributions

    Data analysis and investigation: Yuichi Akino and Hiroya Shiomi; Funding: Yuichi Akino; Supervision: Nobuhisa Mabuchi, Norihisa Masai, Ryoong-Jin, and Kazuhiko Ogawa.

  • Conflicts of Interest

    The Author HS is a developer of the ShioRIS 2.0 software.

  • Received December 20, 2022.
  • Revision received January 6, 2023.
  • Accepted January 23, 2023.

References

  1. ↵
    1. Mahase SS,
    2. D’Angelo D,
    3. Kang J,
    4. Hu JC,
    5. Barbieri CE and
    6. Nagar H
    : Trends in the use of stereotactic body radiotherapy for treatment of prostate cancer in the United States. JAMA Netw Open 3(2): e1920471, 2020. PMID: 32022878. DOI: 10.1001/jamanetworkopen.2019.20471
    OpenUrlCrossRefPubMed
    1. Lukka HR,
    2. Pugh SL,
    3. Bruner DW,
    4. Bahary JP,
    5. Lawton CAF,
    6. Efstathiou JA,
    7. Kudchadker RJ,
    8. Ponsky LE,
    9. Seaward SA,
    10. Dayes IS,
    11. Gopaul DD,
    12. Michalski JM,
    13. Delouya G,
    14. Kaplan ID,
    15. Horwitz EM,
    16. Roach M 3rd.,
    17. Pinover WH,
    18. Beyer DC,
    19. Amanie JO,
    20. Sandler HM and
    21. Kachnic LA
    : Patient reported outcomes in NRG oncology RTOG 0938, evaluating two ultrahypofractionated regimens for prostate cancer. Int J Radiat Oncol Biol Phys 102(2): 287-295, 2018. PMID: 29913254. DOI: 10.1016/j.ijrobp.2018.06.008
    OpenUrlCrossRefPubMed
    1. Nakamura R,
    2. Hirata T,
    3. Suzuki O,
    4. Otani K,
    5. Kai N,
    6. Hatano K,
    7. Fujita K,
    8. Uemura M,
    9. Imamura R,
    10. Tanaka K,
    11. Yoshioka Y,
    12. Nonomura N and
    13. Ogawa K
    : Stereotactic body radiotherapy using CyberKnife® for localized low- and intermediate-risk prostate cancer: Initial report on a phase I/II trial. Anticancer Res 40(4): 2053-2057, 2020. PMID: 32234896. DOI: 10.21873/anticanres.14162
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Parthan A,
    2. Pruttivarasin N,
    3. Davies D,
    4. Taylor DC,
    5. Pawar V,
    6. Bijlani A,
    7. Lich KH and
    8. Chen RC
    : Comparative cost-effectiveness of stereotactic body radiation therapy versus intensity-modulated and proton radiation therapy for localized prostate cancer. Front Oncol 2: 81, 2012. PMID: 22934286. DOI: 10.3389/fonc.2012.00081
    OpenUrlCrossRefPubMed
  5. ↵
    1. Katz AJ and
    2. Kang J
    : Stereotactic body radiotherapy as treatment for organ confined low- and intermediate-risk prostate carcinoma, a 7-year study. Front Oncol 4: 240, 2014. PMID: 25229051. DOI: 10.3389/fonc.2014.00240
    OpenUrlCrossRefPubMed
    1. King CR,
    2. Freeman D,
    3. Kaplan I,
    4. Fuller D,
    5. Bolzicco G,
    6. Collins S,
    7. Meier R,
    8. Wang J,
    9. Kupelian P,
    10. Steinberg M and
    11. Katz A
    : Stereotactic body radiotherapy for localized prostate cancer: pooled analysis from a multi-institutional consortium of prospective phase II trials. Radiother Oncol 109(2): 217-221, 2013. PMID: 24060175. DOI: 10.1016/j.radonc.2013.08.030
    OpenUrlCrossRefPubMed
    1. Zelefsky MJ,
    2. Kollmeier M,
    3. McBride S,
    4. Varghese M,
    5. Mychalczak B,
    6. Gewanter R,
    7. Garg MK,
    8. Happersett L,
    9. Goldman DA,
    10. Pei I,
    11. Lin M,
    12. Zhang Z and
    13. Cox BW
    : Five-year outcomes of a phase 1 dose-escalation study using stereotactic body radiosurgery for patients with low-risk and intermediate-risk prostate cancer. Int J Radiat Oncol Biol Phys 104(1): 42-49, 2019. PMID: 30611838. DOI: 10.1016/j.ijrobp.2018.12.045
    OpenUrlCrossRefPubMed
  8. ↵
    1. Kishan AU,
    2. Dang A,
    3. Katz AJ,
    4. Mantz CA,
    5. Collins SP,
    6. Aghdam N,
    7. Chu FI,
    8. Kaplan ID,
    9. Appelbaum L,
    10. Fuller DB,
    11. Meier RM,
    12. Loblaw DA,
    13. Cheung P,
    14. Pham HT,
    15. Shaverdian N,
    16. Jiang N,
    17. Yuan Y,
    18. Bagshaw H,
    19. Prionas N,
    20. Buyyounouski MK,
    21. Spratt DE,
    22. Linson PW,
    23. Hong RL,
    24. Nickols NG,
    25. Steinberg ML,
    26. Kupelian PA and
    27. King CR
    : Long-term outcomes of stereotactic body radiotherapy for low-risk and intermediate-risk prostate cancer. JAMA Netw Open 2(2): e188006, 2019. PMID: 30735235. DOI: 10.1001/jamanetworkopen.2018.8006
    OpenUrlCrossRefPubMed
  9. ↵
    1. Antypas C and
    2. Pantelis E
    : Performance evaluation of a CyberKnife G4 image-guided robotic stereotactic radiosurgery system. Phys Med Biol 53(17): 4697-4718, 2008. PMID: 18695294. DOI: 10.1088/0031-9155/53/17/016
    OpenUrlCrossRefPubMed
  10. ↵
    1. Asmerom G,
    2. Bourne D,
    3. Chappelow J,
    4. Goggin LM,
    5. Heitz R,
    6. Jordan P,
    7. Kilby W,
    8. Laing T,
    9. Maurer CR Jr.,
    10. Noll JM,
    11. Sayeh S and
    12. Weber A
    : The design and physical characterization of a multileaf collimator for robotic radiosurgery. Biomed Phys Eng Express 2(1): 017003, 2016. DOI: 10.1088/2057-1976/2/1/017003
    OpenUrlCrossRef
  11. ↵
    1. Fürweger C,
    2. Prins P,
    3. Coskan H and
    4. Heijmen BJ
    : Characteristics and performance of the first commercial multileaf collimator for a robotic radiosurgery system. Med Phys 43(5): 2063, 2016. PMID: 27147318. DOI: 10.1118/1.4944740
    OpenUrlCrossRefPubMed
  12. ↵
    1. Tomida M,
    2. Kamomae T,
    3. Suzuki J,
    4. Ohashi Y,
    5. Itoh Y,
    6. Oguchi H and
    7. Okuda T
    : Clinical usefulness of MLCs in robotic radiosurgery systems for prostate SBRT. J Appl Clin Med Phys 18(5): 124-133, 2017. PMID: 28691256. DOI: 10.1002/acm2.12128
    OpenUrlCrossRefPubMed
  13. ↵
    1. Masi L,
    2. Zani M,
    3. Doro R,
    4. Calusi S,
    5. Di Cataldo V,
    6. Bonucci I,
    7. Cipressi S,
    8. Francolini G,
    9. Bonomo P and
    10. Livi L
    : CyberKnife MLC-based treatment planning for abdominal and pelvic SBRT: Analysis of multiple dosimetric parameters, overall scoring index and clinical scoring. Phys Med 56: 25-33, 2018. PMID: 30527086. DOI: 10.1016/j.ejmp.2018.11.012
    OpenUrlCrossRefPubMed
  14. ↵
    1. Shiomi H,
    2. Akino Y,
    3. Sumida I,
    4. Masai N,
    5. Oh RJ and
    6. Ogawa K
    : Development of raster scanning IMRT using a robotic radiosurgery system. J Radiat Res 62(2): 364-373, 2021. PMID: 33454766. DOI: 10.1093/jrr/rraa136
    OpenUrlCrossRefPubMed
  15. ↵
    1. Ozhasoglu C,
    2. Saw CB,
    3. Chen H,
    4. Burton S,
    5. Komanduri K,
    6. Yue NJ,
    7. Huq SM and
    8. Heron DE
    : Synchrony—cyberknife respiratory compensation technology. Med Dosim 33(2): 117-123, 2008. PMID: 18456163. DOI: 10.1016/j.meddos.2008.02.004
    OpenUrlCrossRefPubMed
  16. ↵
    1. Akino Y,
    2. Sumida I,
    3. Shiomi H,
    4. Higashinaka N,
    5. Murashima Y,
    6. Hayashida M,
    7. Mabuchi N and
    8. Ogawa K
    : Evaluation of the accuracy of the CyberKnife Synchrony™ Respiratory Tracking System using a plastic scintillator. Med Phys, 2018. PMID: 29858498. DOI: 10.1002/mp.13028
    OpenUrlCrossRefPubMed
  17. ↵
    1. Sumida I,
    2. Shiomi H,
    3. Oh RJ,
    4. Tanaka E,
    5. Isohashi H,
    6. Inoue T and
    7. Inoue T
    : An optimization algorithm of dose distribution using attraction-repulsion model (application to low-dose-rate interstitial brachytherapy). Int J Radiat Oncol Biol Phys 59(4): 1217-1223, 2004. PMID: 15234058. DOI: 10.1016/j.ijrobp.2004.02.061
    OpenUrlCrossRefPubMed
  18. ↵
    1. Kurosu K,
    2. Sumida I,
    3. Shiomi H,
    4. Mizuno H,
    5. Yamaguchi H,
    6. Okubo H,
    7. Tamari K,
    8. Seo Y,
    9. Suzuki O,
    10. Ota S,
    11. Inoue S and
    12. Ogawa K
    : A robust measurement point for dose verification in delivery quality assurance for a robotic radiosurgery system. J Radiat Res 58(3): 378-385, 2017. PMID: 27811201. DOI: 10.1093/jrr/rrw103
    OpenUrlCrossRefPubMed
  19. ↵
    1. ICRU
    : Prescribing, recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT). J ICRU 10(1), 2010. DOI: 10.1093/jicru_ndq002
    OpenUrlCrossRef
  20. ↵
    1. Burman C,
    2. Kutcher GJ,
    3. Emami B and
    4. Goitein M
    : Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 21(1): 123-135, 1991. PMID: 2032883. DOI: 10.1016/0360-3016(91)90172-z
    OpenUrlCrossRefPubMed
  21. ↵
    1. Zeverino M,
    2. Marguet M,
    3. Zulliger C,
    4. Durham A,
    5. Jumeau R,
    6. Herrera F,
    7. Schiappacasse L,
    8. Bourhis J,
    9. Bochud FO and
    10. Moeckli R
    : Novel inverse planning optimization algorithm for robotic radiosurgery: First clinical implementation and dosimetric evaluation. Phys Med 64: 230-237, 2019. PMID: 31515024. DOI: 10.1016/j.ejmp.2019.07.020
    OpenUrlCrossRefPubMed
    1. Calusi S,
    2. Doro R,
    3. Di Cataldo V,
    4. Cipressi S,
    5. Francolini G,
    6. Bonucci I,
    7. Livi L and
    8. Masi L
    : Performance assessment of a new optimization system for robotic SBRT MLC-based plans. Phys Med 71: 31-38, 2020. PMID: 32088563. DOI: 10.1016/j.ejmp.2020.02.009
    OpenUrlCrossRefPubMed
    1. Schüler E,
    2. Lo A,
    3. Chuang CF,
    4. Soltys SG,
    5. Pollom EL and
    6. Wang L
    : Clinical impact of the VOLO optimizer on treatment plan quality and clinical treatment efficiency for CyberKnife. J Appl Clin Med Phys 21(5): 38-47, 2020. PMID: 32212374. DOI: 10.1002/acm2.12851
    OpenUrlCrossRefPubMed
  24. ↵
    1. Giżyńska MK,
    2. Rossi L,
    3. den Toom W,
    4. Milder MTW,
    5. de Vries KC,
    6. Nuyttens J and
    7. Heijmen BJM
    : Largely reduced OAR doses, and planning and delivery times for challenging robotic SBRT cases, obtained with a novel optimizer. J Appl Clin Med Phys 22(3): 35-47, 2021. PMID: 33475227. DOI: 10.1002/acm2.13172
    OpenUrlCrossRefPubMed
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Optimization of Fluence Map for CyberKnife Raster Scanning Intensity Modulated Radiotherapy
YUICHI AKINO, HIROYA SHIOMI, NOBUHISA MABUCHI, NORIHISA MASAI, RYOONG-JIN OH, KAZUHIKO OGAWA
Anticancer Research Apr 2023, 43 (4) 1637-1642; DOI: 10.21873/anticanres.16314
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