Magnetic Resonance–Based Treatment Planning for Prostate Intensity-Modulated Radiotherapy: Creation of Digitally Reconstructed Radiographs

Chen, Lili; Nguyen, Thai Binh; Jones, Élan; Chen, Zuoqun; Luo, Wei; Wang, Lu; Price, Robert A.; Pollack, Alan; Charlie, C.‐M.

doi:10.1016/j.ijrobp.2007.02.033

Cited by 37 publications

(29 citation statements)

References 17 publications

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“…This approach cannot generate useful reference images and leads to erroneous radiation dose calculation in nonhomogeneous tissues, and thus is unacceptable for MRI-based treatment planning. An improved approach is bulk density assignment based on manual or automatic tissue segmentation of MRI, which assigns a uniform density to a segmented region (Lambert et al 2011, Lee et al 2003, Chen et al 2007). The main drawback of this approach is the inter-observer inconsistency and auto-segmentation errors (Weltens et al 2001, Mazzara et al 2004, Isambert et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

Gudur

Hara

et al. 2014

Phys. Med. Biol.

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MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm's accuracy of electron density mapping and its ability to detect bone in the head for 8 patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as ROC's for bone detection (HU>200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p=2×10-4), 283 for the intensity approach (p=2×10-6) and 282 without density correction (p=5×10-6). For 90% sensitivity in bone detection, the proposed method achieved a specificity of 86%, compared with 80%, 11% and 10% using deformable registration, intensity and without density correction, respectively. Notably, the Bayesian approach was more robust against anatomical differences between patients, with a specificity of 62% in the worst case (patient), compared to 30% specificity in registration-based approach. In conclusion, the proposed unifying Bayesian method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous anatomy.

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Section: Introductionmentioning

confidence: 99%

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

Gudur

Hara

et al. 2014

Phys. Med. Biol.

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“…There is no straightforward way of generating digitally reconstructed radiographs. ( 6 ) Geometric distortions can be significant, with a magnitude of 1 to 2 cm at the periphery of large fields of view. ( 7 , 8 ) Hence, the use of MRI in radiation therapy treatment planning is usually accompanied by a computed tomography (CT) scan.…”

Section: Introductionmentioning

confidence: 99%

Comparison of bulk electron density and voxel‐based electron density treatment planning

Karotki

Mah

Meijer

et al. 2011

J Applied Clin Med Phys

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The use of magnetic resonance imaging (MRI) alone for radiation planning is limited by the lack of electron density for dose calculations. The purpose of this work is to evaluate the dosimetric accuracy of using bulk electron density as a substitute for computed tomography (CT)‐derived electron density in intensity‐modulated radiation therapy (IMRT) treatment planning of head and neck (HN) cancers. Ten clinically‐approved, CT‐based IMRT treatment plans of HN cancer were used for this study. Three dose distributions were calculated and compared for each treatment plan. The first calculation used CT‐derived density and was assumed to be the most accurate. The second calculation used a homogeneous patient density of 1 g/cm3. For the third dose calculation, bone and air cavities were contoured and assigned a uniform density of 1.5 g/cm3 and 0 g/cm3, respectively. The remaining tissues were assigned a density of 1 g/cm3. The use of homogeneous anatomy resulted in up to 4%–5% deviations in dose distribution as compared to CT‐derived electron density calculations. Assigning bulk density to bone and air cavities significantly improved the accuracy of the dose calculations. All parameters used to describe planning target volume coverage were within 2% of calculations based on CT‐derived density. For organs at risk, most of the parameters were within 2%, with the few exceptions located in low‐dose regions. The data presented here show that if bone and air cavities are overridden with the proper density, it is feasible to use a bulk electron density approach for accurate dose calculation in IMRT treatment planning of HN cancers. This may overcome the problem of the lack of electron density information should MRI‐only simulation be performed.PACS number: 87.55.D‐

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“…Manual segmentation of bone for DRR generation in the pelvic region has been demonstrated in a feasibility study (17). However, for real daily clinical use, more-advanced bone enhancing MR sequences, as well as automatic segmentation of bone and air, must be developed.…”

Section: Discussionmentioning

confidence: 99%

“…In modern MR scanners, the nonlinearity can be corrected by using algorithms provided by the MR scanner manufacturers. A number of groups have demonstrated the feasibility of using MR for treatment planning when distortions are properly accounted for by careful selection of MR sequences and application of adequate corrections for the remaining inhomogeneities (16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

Dedicated Magnetic Resonance Imaging in the Radiotherapy Clinic

Karlsson

Nyholm

et al. 2009

International Journal of Radiation Oncology*Biology*Physics

134

102

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Magnetic Resonance–Based Treatment Planning for Prostate Intensity-Modulated Radiotherapy: Creation of Digitally Reconstructed Radiographs

Cited by 37 publications

References 17 publications

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

Comparison of bulk electron density and voxel‐based electron density treatment planning

Dedicated Magnetic Resonance Imaging in the Radiotherapy Clinic

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