Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 
REVIEW ARTICLE
Ahead of print publication  

Challenges and developments of magnetic resonance image-guided radiation therapy for brain tumors


1 Department of Radiation Oncology, Yashoda Hospitals, Yashoda Cancer Institute, Hyderabad, Telangana; School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India
2 School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Date of Submission04-Aug-2020
Date of Decision28-Dec-2020
Date of Acceptance12-Jan-2021
Date of Web Publication13-Apr-2021

Correspondence Address:
Anu Radha Chandrasekaran,
School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_1023_20

 > Abstract 


Treatment of brain lesions with magnetic resonance imaging (MRI) is indispensable. Although computed tomography (CT) is the gold standard for radiotherapy (RT) planning, recent developments in image-guided RT have compelling reasons to remove CT from the regular RT workflow. Thus, magnetic resonance (MR) only RT is gaining interest rapidly. In this review, we look at how MRI has gained the importance in radiation planning at various stages and why we try to refrain CT imaging. We have elaborated the MR simulation techniques for the brain from different expertise to get a clear insight about the imaging protocols and quality assurance steps tweaked for RT requirements. We have described the basic and complex algorithms utilized to promulgate a CT-like data set from appropriate MR sequences for crucial treatment planning process. In a small section, we have narrated the significance of dose escalation using the functional MR images. In the final section, we describe a pivotal (yet least researched) area of digital radiograph generation using MR dataset for day-to-day pretreatment image verification on couch.

Keywords: Brain tumors, dose escalation, magnetic resonance only radiotherapy, magnetic resonance simulation, synthetic computed tomography



How to cite this URL:
Ilamurugu A, Chandrasekaran AR. Challenges and developments of magnetic resonance image-guided radiation therapy for brain tumors. J Can Res Ther [Epub ahead of print] [cited 2021 Jun 22]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=313663




 > Introduction Top


Poor soft-tissue contrast and radiation dose are the primary concerns to exclude the computed tomography (CT) scans from the radiotherapy (RT) process. Former is the most compelling reason that made RT community to consider magnetic resonance imaging (MRI) as the most viable imaging solution. As such MRI is not new to the RT planning and has been used in conjunction with CT images.[1],[2] CT images are gold standard in RT planning since the advent of computerized planning.[3] A rigid registered CT-magnetic resonance (MR) data set helps in obtaining MR-based volumes for target delineation and CT-based electron densities for dose calculations. Furthermore, there are problems associated with the MR-CT registration process which can be avoided if MRI is directly used for RT planning and delivery.

For the effective use of MR image for target delineation, it is routinely coregistered with the CT image which is exclusively done for the treatment planning purpose. Vendors associated with radiation treatment planning (RTP) have come out with best registration algorithms to register the CT and MR; often using the mutual information from the datasets. There are very few studies to quantify the rigid registrations between two different image sequences. Contour-based analysis postregistration is one of the methods to verify the registration algorithms. Registration between images is inevitable process in the treatment planning process as there are differences in the patient anatomy during MR and CT examination. Accuracy of contours done on planning CT with the MR images in background depends on the credibility of image fusion process. The uncertainty of registration error is hardly quantified in RT.

The world is moving toward MR only planning, and there is cost benefit and reduced workload if CT imaging is ruled out from radiation planning. In this review, we see the development of MR-based simulation, treatment planning with credible dose escalation, and delivery methods up-to-date in brain tumors. An electronic search in performed and peer-reviewed English publications was sorted for this overview. The search terms include “CT versus MRI in oncology,” “advantages of MRI over CT in brain tumors,” “MR imaging in radiation therapy planning,” “MR simulation,” “synthetic CT generation methods,” “DRR verification for MR only radiotherapy,” “dose escalation in radiotherapy,” and “dose escalation using functional MRI.” The references in the popular review and original articles were also chosen. Primarily, they were sorted based on the abstract information, and later full papers were collected.


 > Rationale Top


The use of MR imaging in RT planning was evaluated way back in 1985 by Shuman et al.,[4] where three-dimensional (3D) planning was not prevalent. They found a measurable difference in the plan when MR was made available in addition to CT. It was a work in progress at that time and they predicted that MR will become a necessary procedure in planning for many cases, as they increased confidence in the original plan. Vaghi et al.,[5] in 1986, found that MR imaging has the upper hand in depicting the extension of tumor and its anatomic relationship for cerebral gliomas. They showed that the differentiation between tumor and edema was difficult using CT images, whereas the tumor and the necrotic areas are clearly evident using MR images.

Shuman et al. in 1987, have investigated the benefit of MR imaging in Oligodendro-Glioma[6] of nine patients. In six out of nine cases, MR found tumor volumes which were not evident in CT. The abnormality interface was well correlated using MR, especially in the tumor adjacent edema regions. They believed that MR is superior to CT in providing adequate information for radiation planning purposes. Furthermore, in December 1987, Fraass et al.[7] have widely discussed the technical considerations to integrate the MR images into planning. MRI has shown significant change in portals of ten out of 17 cases in comparison to CT as reported by Just et al.[8] CT and MR image correlation leads to improved target volume delineation in contrast to CT-alone information for RTP. This study was done by Phillips et al.[9] and Schad et al.[10] on arteriovenous malformations and basal meningiomas, respectively. [Figure 1] shows for true disease extent from MR data in comparison to CT data.
Figure 1: Visual discrimination of computed tomography versus magnetic resonance data for cranial malignancies. (a) Sagittal views of T1W contrast-enhanced magnetic resonance (TE/TR = 4.2 ms/20.7 ms) and contrast computed tomography of Grade III glioma. (b) Axial views of a patient showing clear target visualization of left-sided acoustic neuroma on three-dimensional fast spoiled gradient echo (gadolinium enhanced) magnetic resonance image (TE/TR = 1.75 ms/4.2 ms) in comparison to computed tomography with contrast. (c) Coronal and axial view comparisons of glioblastoma between T1W contrast magnetic resonance (TE/TR = 10.3 ms/17.08 ms) and Contrast-enhanced computed tomography

Click here to view


The quantitative assessment and clinical utility of addition of MRI to 3D-CT-based planning were assessed by Ten Haken et al.[11] and Thornton et al.,[12] respectively, for brain neoplasms. They found that the MR data integration with CT data is essential and practical. Heesters et al., in 1993,[13] have found a large difference in field size and positions for low-grade gliomas (non-CT contrast-enhancing lesions) but not in high-grade (HG) gliomas (CT contrast-enhancing tumors). Pardo et al.[14] and Hamilton et al.[15] have explored the use of functional MRI data in RTP for brain tumors, although it yielded a less probable significance in planning. As seen from the above references, MRI is a highly preferable modality to access the cranial lesions. It provides the excellent visualization of tumor and organs-at-risk compared to CT. It also reduces the inter-observer and intra-observer variations in brain tumors. The inter-observer variation was reduced significantly by CT-MR registration for acoustic neuromas, astrocytomas, and the lesion of brainstem and cerebellum.[16],[17] Similar results were inferred by Cattaneo et al.[18] for postoperative irradiation of HG gliomas, thereby reducing the target volume margins.


 > Challenges and Developments for Magnetic Resonance-Only Radiotherapy in the Brain Top


Simulation

In late 90s, registration of CT and MR images is commonly performed to define the targets for RTP. MR image suffer from geometric distortion (both patient and machine induced), whereas CT are usually regarded as geometrically stable. Spatial MR distortions vary with field strengths and acquisition protocols. It was reported as early in 2001 by Fransson et al.[19] that phantom-based correction techniques are sufficient at low magnetic fields, and patient-related distortion corrections are also needed at higher field strength. Phantom studies for distortions were further done using low-field MR.[20],[21] Indefinite geometric fidelity is an important reason that RT community is apprehensive to use MRI for RTP. MR-only RTP for the brain is promising for the same reason that the distortion will be minimal as the intracranial movements are less or negligible. In addition, the external motion for the skull can be effectively immobilized. [Table 1] summarizes that how MR simulation (for RTP) is distinct from diagnostic MR scans.
Table 1: Differentiation of magnetic resonance simulation for radiation planning from regular diagnostic magnetic resonance

Click here to view


A comprehensive MR simulation protocols were introduced by Paulson et al.[22] with a 70 cm, three tesla (3T) dedicated scanner for RT procedures. The author has given practical strategies for clinical MR scans for different sites including the brain and has standardized the MR images to be used for RTP after distortion correction (postprocessing). They were successful in lodging the patient setup and immobilization devices to get uniform high contrast-to-noise ratio MR images. They also implemented quality assurance (QA) program tweaked for RT-specific MR simulation to maintain reproducibility and accuracy. Similar works to integrate MR simulation into RT workflow is done by Glide-Hurst et al.[23] They have given site-specific coils, immobilization devices, and appropriate image sequences for MR-simulation procedures. More recently, Taghizadeh et al.[24] attempted to generate nondistorted MR sequences with respect to stereotactic radiosurgery (SRS) planning [Table 2]. They have evaluated the geometric constancy and artefacts by scanning commercial phantoms with SRS frames and localizers.
Table 2: Tumor-specific magnetic resonance sequences for stereotactic radiosurgery/stereotactic radiotherapy simulation techniques

Click here to view


Liney and Moerland[25] believed that present MR scanners could provide satisfactory results if relevant pulse sequence techniques [Table 3] are adopted for RTP. Their recommendations are primitive and form a good basis to implement MR-only RT planning. They have given following recommendation for dedicated/existing MR scanners to acquire scans for RT planning based on a detailed analysis in MR simulation.
Table 3: Pulse sequence techniques relevant to radiation treatment planning

Click here to view


  • Field strength 1.5 >T< 3.0
  • Wide bore magnet with closed configuration
  • A flat table top to mimic RT treatment position
  • Multichannel radiofrequency (RF) coils with intensity correction
  • Verification of geometric distortion in all three planes with proper slice coverage
  • Fast or turbo spin echo should be preferred for lesser acquisition time
  • MR slice thickness should be matched with CT slice thickness for apt registration
  • Acquisition must be strictly site specific and appropriate
  • QA of image protocol must be carried our periodically.


Planning

Electron density information from CT images is vital for RT dose calculations which use megavoltage (MV) beams. As Compton interaction dominates in MV range, and it is linearly proportional to the electron density (electrons/gm), correction for tissue in-homogeneity is a gold-standard in RTP. MRI lacks this information, and this is the second most limiting factor apart from image distortions. Many authors attempted to solve this problem in the following ways:

  • Assigning bulk densities to three or more structures and performing a dosimetric comparison
  • Performing dose calculation without in-homogeneity correction and comparing with clinical CT plans
  • Developing a synthetic CT (sCT) form MR images form atlas-based/hybrid methods with or without dose comparison.


sCT generation is a promising concept and does not come without challenges. With the existing methods air, bone, soft tissue, and fat are easily segmented using a MRI image, whereas bone segmentation is crucial and difficult. It has been understood clinically that ultra-short time (UTE) MR image sequence is good for bones and connective tissue visibility better than other sequences.[26] It was recognized that tissues such as cortical bone which have short T2, the MR signal with short echo times (TE) is not detectable as these decay very rapidly and thus they appear dark. Pulse sequence with shorter TE in the range of 8–200 microseconds (μs) can be produced (thus the name UTE). These pulse sequences have TE 10–200 times shorter than the routine TE used in MR systems. Thus, the cortical bone with mean T2 of the order of 0.4 to 0.5 ms can be easily visualized.

Hsu et al.[27] have been successful in discriminating air and bone using postprocessed UTE images. Johansson et al.[28] used a Gaussian mixture regression model to link the voxel value of CT and MR sequences. They have utilized one T2-W 3D spin echo and dual UTE MRI (with different TE and flip angles) to train and generate substitute CT or sCT. There was no large difference in accuracy and considered robust as it is voxel based. They also did a pilot study to investigate the dose calculation accuracy on MRI. The dose calculation is done on CT, sCT, sCT without heterogeneity correction and on bulk-density assigned MR images. As per their results, dose calculation accuracy on sCT is improved with only 2% difference compared to the dose calculation done on sCT without heterogeneity correction.[29] This team has also investigated the accuracy of inverse planning for volumetric modulated arc therapy and a geometric comparison of digitally reconstructed radiographs (DRR) derived from sCT and actual CT for brain lesions.[30] A typical flowchart of sCT preparation is show in [Figure 2].
Figure 2: A typical flowchart for the generation of synthetic computed tomography using Atlas-based method

Click here to view


Further studies were carried out to verify the dosimetric accuracy of MR generated sCT by various methods and models, and the results were fruitful for brain tumors.[31],[32],[33] The dose volume histogram spread of all plans and the least difference of isodose comparison between clinical CT and sCT for brain gives us more confidence. Investigation of dose calculation variation of bulk-density assigned fast-spoiled gradient echo MR image is done to figure out if there is a little deviation between the plans with and without progressive resolution optimization.[34] This study was deliberately done in the cerebellopontine region to rule out any significance of re-optimization (without changing the constraints) in highly heterogeneous area. Koivula et al. have done a feasibility study of MR-only planning for proton therapy treatment plans (intensity modulated proton therapy). The author has observed maximum absolute dose difference of 8.9% and 1.4% for homogeneous and heterogeneous sCT, respectively, for brain tumor clinical target volumes.[35] Rank et al.[36] have also noted very small deviation for ion and photon-based treatment plans in the brain regions. More recently, dosimetric evaluation of MR-based sCT was done for large cohort of 52 patients in the cranial regions and found accurate.[37]

Dose escalation

New technologies in RT have made a progressive contribution in the past two decades for dose escalation (for example, advent of multi-leaf collimators). Functional MRI is one such modality which has the potential to discriminate the aggressive nature of tumors and help us to escalate the dose[38] suitably [Figure 3]. This will have a meaningful influence on treatment outcomes. Highly packed tumor cells reduce extracellular diffusion compared to the intracellular diffusion. Detection of this hypercellularity in tumors is the basis for diffusion-weighted imaging (DWI). Zhu et al. (from Mreading in RT, 3rd Edition ESTRO 2017, 26–31) showed that high b-value (3000 s/mm2) DWI improves the accuracy of target delineation by detecting hyper-cellular components in glioblastoma (GBM) with high specificity. They evaluated that gross tumor volume definition is possibly improved with increase in the sensitivity of DWI.
Figure 3: A pictorial representation of dose escalation schema (b) in comparison to routine treatment volumes (a); GTV - Gross tumor volume; CTV - Clinical target volume; PTV - Planning target volume; BTV - Biological target volume.

Click here to view


Rogers et al. (Mreading in RT, 2nd Edition ESTRO 2016, 29–31) have explored diffusion tensor imaging along with fractional angiography to adequately include the regions of microscopic filtration and exclude the normal brain for HG gliomas. They derived biological contours instead of regular clinical contours from T1W contrast MRI. This helps prevents the risk of neurotoxicity by irradiating large volumes in the brain. Spectroscopic MRI (sMRI) of GBM is studied for prominent dose escalation by Saumya et al. They believed that not escalating the dose beyond 60 Gy and inability to identify the high-risk tumor lead to disease progression and recurrence. As image processing and expertise in sMRI complicate the implementation, they have developed a brain imaging collaboration suite to integrate sMRI to evaluate metabolic activity (Mreading in RT, 4th Edition ESTRO 2018, 40–43). This has enabled physicians to delineate tumor on voxel basis to selectively give higher dose (up to 75Gy) in RT planning.

Although surgical resections for GBM are still routinely performed, the ability of MR-IGRT has the potential to obviate the need for surgery in future. With the ablative doses, and with and without the utilization of biologic agents during MR imaging, a novel noninvasive stereotactic radiation could be performed before the regular conformal RT dose of 60Gy to improve the prognosis.[39] Integrated MR systems would be used to both radiation and chemoresponses. With adaptive planning tools available, nonresponsive areas could be dose escalated designed on the daily basis and could be delivered online. Early response assessment post-RT MR imaging could be done in a treatment room itself to determine residual areas with disease.[40]

Treatment delivery

Beam delivery is incomplete without verification image. Image verification is next big challenge for MR-only RT where DRR cannot be directly generated from MRI sequence unlike CT. Radiation units, worldwide employ either MV or Kilovoltage (kV)-based portal images for verification and target localization. In routine CT-based treatments, patient setup is verified using kV or MR image pairs primarily against DRR produced from CT datasets. Poor bone visualization of regular MR sequence poses problem in the generation of DRR too. As MR-simulation is clinically feasible as discussed earlier, it is compelling that MR-simulation images need to be transformed into a “CT-like” DRR image to validate the patient setup on daily basis. Ramsey and Oliver in 1998 have attempted MR-derived DRR[41] images from T1-weighted scan sequence of anthromorphic RANDO head phantom and found equivalent to a CT based DRR. Yin et al. have also concluded that MR-DRR could be used as a reference for portal verification.[42] In 1999, Ramsey et al.[43] have demonstrated the clinical utility of MR-DRR for setup verification which has come about 3–10 mm of misalignments. Contrast-enhanced T1-W MR scans with a slice thickness of 5 mm were acquired using 1.5 T scanner (using standard head coil) for this study.

Yang et al.[44] have been able to capture cranial, facial, and vertebral landmarks using UTE images which uses ultrashort T2 signals. They have accessed the accuracy of UTE-based DRRs for cranial tumors. A single UTE-MRI protocol with flip angle of 18° is acquired in 50% less time in contrast to the twin protocol proposed by Johansson et al.[28] Manual reference points were used to find the registration discrepancies in the range of sub-millimeters between CT-DRR and UTE-DRR, which is quite remarkable. Price et al.[45] have found a close agreement between the accuracy of sCT generated DRR to regular CT-DRR. They have evaluated volumetric and planar images using phantom as well as brain patients. This work strongly backs the implementation of MR-only treatment in the brain. It has been shown that MR-based DRRs could very well replace CT-based DRRs for MR only treatment.


 > Magnetic Resonance-Guided Radiotherapy Systems Top


VewRay developed its first commercial MR-guided RT system[46],[47] which combines both MR imaging and intensity-modulated radiation therapy. This system has been in use since 2014, comprises 0.35 T MRI and houses 3 Cobalt-60 sources for beam delivery with simultaneous tumor tracking facility (gating). The RF signal interference from Linac and the impact of magnetic field on the path of electrons in an MR-Linac are avoided. This system fits in any existing Linac vaults which avoids delay in installation. The MR-linac version from VewRay Inc. (Oakward, USA) integrates a 0.35 T MRI with a 6MV flattening-filter-free linear accelerator.[48] This system offers pretreatment and posttreatment MR imaging of a patient. It offers real-time organ position and automated beam gating (SmartTARGET) for accurate dose delivery, whereby the clinicians could escalate dose wherever possible. It also offers integrated adaptive treatment which allows re-optimization of treatment plan with current treatment position (SmartADAPT).

The Elekta Unity system has been developed and started its clinical used in 2017.[49] This hybrid 1.5 T MR-linac is built in UMC Utrecht with Electa AB (Stokholm, Sweden) and Philips (Best, The Netherlands).[50] The goal of the integration is to enable the visualization of soft tissue and other anatomical changes directly from the couch top during the course of treatment.[51] Daily MRI is used for position verification, replanning, dose accumulation, or real-time replanning.

Particle therapy (PT) along with MR imaging could be used instead of photons. MR-integrated proton therapy (MRiPT) hybrid systems offer unprecedented soft-tissue contrast of MRI with most conformal, best dose steering capability provided by modern PT.[52] MRiPT is in its infancy stages and several research groups have started addressing the technical difficulties to bring into clinical reality.[53] The challenges associated with the systems are magnetic interactions between the MRI and PT system,[54] prediction and measurements of dose in the presence of magnetic fields,[55] dose calculation from MR information and hardware and software improvements.[56]


 > Conclusion Top


The future of MR RT will not only help us to “see what we treat” but also to “look what we have done.” Thus, we see a huge opportunity for tumor visualization from accurate MRI sequences to use directly for treatment planning. Furthermore, the treatment delivery and monitoring the tumor response almost on a day-to-day basis for plan adaptation and/or dose escalation are very much feasible with MR-only RT.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
 > References Top

1.
Brock KK, Dawson LA. Point: Principles of magnetic resonance imaging integration in a computed tomography-based radiotherapy workflow. Semin Radiat Oncol 2014;24:169-74.  Back to cited text no. 1
    
2.
Kessler ML. Image registration and data fusion in radiation therapy. Br J Radiol 2006;79(special_issue_1):S99-108.  Back to cited text no. 2
    
3.
Khoo VS, Dearnaley DP, Finnigan DJ, Padhani A, Tanner SF, Leach MO. Magnetic resonance imaging (MRI): Considerations and applications in radiotherapy treatment planning. Radiother Oncol 1997;42:1-5.  Back to cited text no. 3
    
4.
Shuman WP, Griffin BR, Haynor DR, Johnson JS, Jones DC, Cromwell LD, et al. MR imaging in radiation therapy planning. Work in progress. Radiology 1985;156:143-7.  Back to cited text no. 4
    
5.
Vaghi MA, Visciani A, Passerini A, Longone V, Broggi G. Magnetic resonance in the study of cerebral gliomas. Radiol Med 1986;72:431-8.  Back to cited text no. 5
    
6.
Shuman WP, Griffin BR, Haynor DR, Jones DC, Johnson JS, Cromwell LD, et al. The utility of MR in planning the radiation therapy of oligodendroglioma. AJR Am J Roentgenol 1987;148:595-600.  Back to cited text no. 6
    
7.
Fraass BA, McShan DL, Diaz RF, Ten Haken RK, Aisen A, Gebarski S, et al. Integration of magnetic resonance imaging into radiation therapy treatment planning: I. Technical considerations. Int J Radiat Oncol Biol Phys 1987;13:1897-908.  Back to cited text no. 7
    
8.
Just M, Rösler HP, Higer HP, Kutzner J, Thelen M. MRI-assisted radiation therapy planning of brain tumors-clinical experiences in 17 patients. Magn Reson Imaging 1991;9:173-7.  Back to cited text no. 8
    
9.
Phillips MH, Kessler M, Chuang FY, Frankel KA, Lyman JT, Fabrikant JI, et al. Image correlation of MRI and CT in treatment planning for radiosurgery of intracranial vascular malformations. Int J Radiat Oncol Biol Phys 1991;20:881-9.  Back to cited text no. 9
    
10.
Schad LR, Gademann G, Knopp M, Zabel HJ, Schlegel W, Lorenz WJ. Radiotherapy treatment planning of basal meningiomas: Improved tumor localization by correlation of CT and MR imaging data. Radiother Oncol 1992;25:56-62.  Back to cited text no. 10
    
11.
Ten Haken RK, Thornton AF Jr., Sandler HM, LaVigne ML, Quint DJ, Fraass BA, et al. A quantitative assessment of the addition of MRI to CT-based, 3-D treatment planning of brain tumors. Radiother Oncol 1992;25:121-33.  Back to cited text no. 11
    
12.
Thornton AF Jr., Sandler HM, Ten Haken RK, McShan DL, Fraass BA, La Vigne ML, et al. The clinical utility of magnetic resonance imaging in 3-dimensional treatment planning of brain neoplasms. Int J Radiat Oncol Biol Phys 1992;24:767-75.  Back to cited text no. 12
    
13.
Heesters MA, Wijrdeman HK, Struikmans H, Witkamp T, Moerland MA. Brain tumor delineation based on CT and MR imaging. Implications for radiotherapy treatment planning. Strahlenther Onkol 1993;169:729-33.  Back to cited text no. 13
    
14.
Pardo FS, Aronen HJ, Kennedy D, Moulton G, Paiva K, Okunieff P, et al. Functional cerebral imaging in the evaluation and radiotherapeutic treatment planning of patients with malignant glioma. Int J Radiat Oncol Biol Phys 1994;30:663-9.  Back to cited text no. 14
    
15.
Hamilton RJ, Sweeney PJ, Pelizzari CA, Yetkin FZ, Holman BL, Garada B, et al. Functional imaging in treatment planning of brain lesions. Int J Radiat Oncol Biol Phys 1997;37:181-8.  Back to cited text no. 15
    
16.
Aoyama H, Shirato H, Nishioka T, Hashimoto S, Tsuchiya K, Kagei K, et al. Magnetic resonance imaging system for three-dimensional conformal radiotherapy and its impact on gross tumor volume delineation of central nervous system tumors. Int J Radiat Oncol Biol Phys 2001;50:821-7.  Back to cited text no. 16
    
17.
Weltens C, Menten J, Feron M, Bellon E, Demaerel P, Maes F, et al. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiother Oncol 2001;60:49-59.  Back to cited text no. 17
    
18.
Cattaneo GM, Reni M, Rizzo G, Castellone P, Ceresoli GL, Cozzarini C, et al. Target delineation in post-operative radiotherapy of brain gliomas: Interobserver variability and impact of image registration of MR (pre-operative) images on treatment planning CT scans. Radiother Oncol 2005;75:217-23.  Back to cited text no. 18
    
19.
Fransson A, Andreo P, Pötter R. Aspects of MR image distortions in radiotherapy treatment planning. Strahlenther Onkol 2001;177:59-73.  Back to cited text no. 19
    
20.
Krempien RC, Schubert K, Zierhut D, Steckner MC, Treiber M, Harms W, et al. Open low-field magnetic resonance imaging in radiation therapy treatment planning. Int J Radiat Oncol Biol Phys 2002;53:1350-60.  Back to cited text no. 20
    
21.
Mah D, Steckner M, Palacio E, Mitra R, Richardson T, Hanks GE. Characteristics and quality assurance of a dedicated open 0.23 T MRI for radiation therapy simulation. Med Phys 2002;29:2541-7.  Back to cited text no. 21
    
22.
Paulson ES, Erickson B, Schultz C, Allen Li X. Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. Med Phys 2015;42:28-39.  Back to cited text no. 22
    
23.
Glide-Hurst CK, Wen N, Hearshen D, Kim J, Pantelic M, Zhao B, et al. Initial clinical experience with a radiation oncology dedicated open 1.0T MR-simulation. J Appl Clin Med Phys 2015;16:5201.  Back to cited text no. 23
    
24.
Taghizadeh S, Labuda C, Yang CC, Morris B, Kanakamedala MR, Vijayakumar S, et al. Optimizing MRI sequences and images for MRI-based stereotactic radiosurgery treatment planning. Rep Pract Oncol Radiother 2019;24:12-9.  Back to cited text no. 24
    
25.
Liney GP, Moerland MA. Magnetic resonance imaging acquisition techniques for radiotherapy planning. Semin Radiat Oncol 2014;24:160-8.  Back to cited text no. 25
    
26.
Reichert IL, Robson MD, Gatehouse PD, He T, Chappell KE, Holmes J, et al. Magnetic resonance imaging of cortical bone with ultrashort TE pulse sequences. Magn Reson Imaging 2005;23:611-8.  Back to cited text no. 26
    
27.
Hsu SH, Cao Y, Lawrence TS, Tsien C, Feng M, Grodzki DM, et al. Quantitative characterizations of ultrashort echo (UTE) images for supporting air–bone separation in the head. Phys Med Biol 2015;60:2869.  Back to cited text no. 27
    
28.
Johansson A, Karlsson M, Nyholm T. CT substitute derived from MRI sequences with ultrashort echo time. Med Phys 2011;38:2708-14.  Back to cited text no. 28
    
29.
Jonsson JH, Johansson A, Söderström K, Asklund T, Nyholm T. Treatment planning of intracranial targets on MRI derived substitute CT data. Radiother Oncol 2013;108:118-22.  Back to cited text no. 29
    
30.
Jonsson JH, Akhtari MM, Karlsson MG, Johansson A, Asklund T, Nyholm T. Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions. Radiat Oncol 2015;10:13.  Back to cited text no. 30
    
31.
Edmund JM, Kjer HM, Van Leemput K, Hansen RH, Andersen JA, Andreasen D. A voxel-based investigation for MRI-only radiotherapy of the brain using ultra short echo times. Phys Med Biol 2014;59:7501.  Back to cited text no. 31
    
32.
Paradis E, Cao Y, Lawrence TS, Tsien C, Feng M, Vineberg K, et al. Assessing the dosimetric accuracy of magnetic resonance-generated synthetic CT images for focal brain VMAT radiation therapy. Int J Radiat Oncol Biol Phys 2015;93:1154-61.  Back to cited text no. 32
    
33.
Demol B, Boydev C, Korhonen J, Reynaert N. Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images. Med Phys 2016;43:6557-68.  Back to cited text no. 33
    
34.
Ilamurugu A, Chandrasekaran AR, Ayyalusamy A, Shanmugam S, Velayudham R, KattaCharu GR, et al. Feasibility of MR-only radiation planning for hypofractionated stereotactic radiotherapy of schwannomas using non-coplanar volumetric modulated arc therapy. Radiol Med 2019;124:400-7.  Back to cited text no. 34
    
35.
Koivula L, Wee L, Korhonen J. Feasibility of MRI-only treatment planning for proton therapy in brain and prostate cancers: Dose calculation accuracy in substitute CT images. Med Phys 2016;43:4634-42.  Back to cited text no. 35
    
36.
Rank CM, Hünemohr N, Nagel AM, Röthke MC, Jäkel O, Greilich S. MRI-based simulation of treatment plans for ion radiotherapy in the brain region. Radiother Oncol 2013;109:414-8.  Back to cited text no. 36
    
37.
Dinkla AM, Wolterink JM, Maspero M, Savenije MHF, Verhoeff JJC, Seravalli E, et al. MR-only brain radiation therapy: Dosimetric evaluation of synthetic CTs generated by a dilated convolutional neural network. Int J Radiat Oncol Biol Phys 2018;102:801-12.  Back to cited text no. 37
    
38.
van der Heide UA, Houweling AC, Groenendaal G, Beets-Tan RG, Lambin P. Functional MRI for radiotherapy dose painting. Magn Reson Imaging 2012;30:1216-23.  Back to cited text no. 38
    
39.
Einstein DB, Wessels B, Bangert B, Fu P, Nelson AD, Cohen M, et al. Phase II trial of radiosurgery to magnetic resonance spectroscopy-defined high-risk tumor volumes in patients with glioblastoma multiforme. Int J Radiat Oncol Biol Phys 2012;84:668-74.  Back to cited text no. 39
    
40.
Gladwish A, Koh ES, Hoisak J, Lockwood G, Millar BA, Mason W, et al. Evaluation of early imaging response criteria in glioblastoma multiforme. Radiat Oncol 2011;6:121.  Back to cited text no. 40
    
41.
Ramsey CR, Oliver AL. Magnetic resonance imaging based digitally reconstructed radiographs, virtual simulation, and three-dimensional treatment planning for brain neoplasms. Med Phys 1998;25:1928-34.  Back to cited text no. 41
    
42.
Yin FF, Gao Q, Xie H, Nelson DF, Yu Y, Kwok WE, et al. MR image-guided portal verification for brain treatment field. Int J Radiat Oncol Biol Phys 1998;40:703-11.  Back to cited text no. 42
    
43.
Ramsey CR, Arwood D, Scaperoth D, Oliver AL. Clinical application of digitally-reconstructed radiographs generated from magnetic resonance imaging for intracranial lesions. Int J Radiat Oncol Biol Phys 1999;45:797-802.  Back to cited text no. 43
    
44.
Yang Y, Cao M, Kaprealian T, Sheng K, Gao Y, Han F, et al. Accuracy of UTE-MRI-based patient setup for brain cancer radiation therapy. Med Phys 2016;43:262-7.  Back to cited text no. 44
    
45.
Price RG, Kim JP, Zheng W, Chetty IJ, Glide-Hurst C. Image guided radiation therapy using synthetic computed tomography images in brain cancer. Int J Radiat Oncol Biol Phys 2016;95:1281-9.  Back to cited text no. 45
    
46.
Olsen J, Green O, Kashani R. World's first application of MR-guidance for radiotherapy. Missouri Med 2015;112:358.  Back to cited text no. 46
    
47.
Acharya S, Fischer-Valuck BW, Kashani R, Parikh P, Yang D, Zhao T, et al. Online magnetic resonance image guided adaptive radiation therapy: First clinical applications. Int J Radiat Oncol Biol Phys 2016;94:394-403.  Back to cited text no. 47
    
48.
Liney GP, Whelan B, Oborn B, Barton M, Keall P. MRI-Linear Accelerator Radiotherapy Systems. Clin Oncol (R Coll Radiol) 2018;30:686-91.  Back to cited text no. 48
    
49.
Lagendijk JJ, Raaymakers BW, Van Vulpen M. The magnetic resonance imaging–linac system. Semin Radiat Oncol 2014;24:207-9.  Back to cited text no. 49
    
50.
Raaymakers BW, Lagendijk JJ, Overweg J, Kok JG, Raaijmakers AJ, Kerkhof EM, et al. Integrating a 1.5 T MRI scanner with a 6 MV accelerator: Proof of concept. Phys Med Biol 2009;54:N229.  Back to cited text no. 50
    
51.
Raaymakers BW, Jürgenliemk-Schulz IM, Bol GH, Glitzner M, Kotte AN, Van Asselen B, et al. First patients treated with a 1.5 T MRI-linac: Clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment. Phys Med Biol 2017;62:L41.  Back to cited text no. 51
    
52.
Oborn BM, Dowdell S, Metcalfe PE, Crozier S, Mohan R, Keall PJ. Future of medical physics: Real-time MRI-guided proton therapy. Med Phys 2017;44:e77-90.  Back to cited text no. 52
    
53.
Schellhammer SM, Hoffmann AL, Gantz S, Smeets J, van der Kraaij E, Quets S, et al. Integrating a low-field open MR scanner with a static proton research beam line: Proof of concept. Phys Med Biol 2018;63:23LT01.  Back to cited text no. 53
    
54.
Oborn BM, Dowdell S, Metcalfe PE, Crozier S, Mohan R, Keall PJ. Proton beam deflection in MRI fields: Implications for MRI-guided proton therapy. Med Phys 2015;42:2113-24.  Back to cited text no. 54
    
55.
Fuchs H, Moser P, Gröschl M, Georg D. Magnetic field effects on particle beams and their implications for dose calculation in MR-guided particle therapy. Med Phys 2017;44:1149-56.  Back to cited text no. 55
    
56.
Hoffmann A, Oborn B, Moteabbed M, Yan S, Bortfeld T, Knopf A, et al. MR-guided proton therapy: A review and a preview. Radiat Oncol 2020;15:129.  Back to cited text no. 56
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

 
Top
 
 
  Search
 
     Search Pubmed for
 
    -  Ilamurugu A
    -  Chandrasekaran AR
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  >Abstract>Introduction>Rationale>Challenges and D...>Magnetic Resonan...>Conclusion>Article Figures>Article Tables
  In this article
>References

 Article Access Statistics
    Viewed163    
    PDF Downloaded5    

Recommend this journal