Tufve Nyholm

CT substitute derived from MRI sequences with ultrashort echo time

PURPOSE Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET/MRI applications, as well as for patient positioning and dose planning in MRI based radiation therapy workflows. This study presents a method for generating a drop in substitute for a CT image from a set of magnetic resonance (MR)images. METHODS A Gaussian mixture regression model was used to link the voxel values in CT images to the voxel values in images from three MRI sequences: one T2 weighted 3D spin echo based sequence and two dual echo ultrashort echo time MRI sequences with different echo times and flip angles. The method used a training set of matched MR and CT data that after training was able to predict a substitute CT (s-CT) based entirely on the MR information for a new patient. Method validation was achieved using datasets covering the heads of five patients and applying leave-one-out cross-validation (LOOCV). During LOOCV, the model was estimated from the MR and CT data of four patients (training set) and applied to the MR data of the remaining patient (validation set) to generate an s-CT image. This procedure was repeated for all five training and validation data combinations. RESULTS The mean absolute error for the CT number in the s-CT images was 137 HU. No large differences in method accuracy were noted for the different patients, indicating a robust method. The largest errors in the s-CT images were found at air-tissue and bone-tissue interfaces. The model accurately discriminated between air and bone, as well as between soft tissues and nonsoft tissues. CONCLUSIONS The s-CT method has the potential to provide an accurate estimation of CT information without risk of geometrical inaccuracies as the model is voxel based. Therefore, s-CT images could be well suited as alternatives to CT images for dose planning in radiotherapy and attenuation correction in PET/MRI.

Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions

BackgroundBecause of superior soft tissue contrast, the use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To keep the workflow simple and cost effective and to reduce patient dose, it is natural to strive for a treatment planning procedure based entirely on MRI. In the present study, we investigate the dose calculation accuracy for different treatment regions when using bulk density assignments on MRI data and compare it to treatment planning that uses CT data.MethodsMR and CT data were collected retrospectively for 40 patients with prostate, lung, head and neck, or brain cancers. Comparisons were made between calculations on CT data with and without inhomogeneity corrections and on MRI or CT data with bulk density assignments. The bulk densities were assigned using manual segmentation of tissue, bone, lung, and air cavities.ResultsThe deviations between calculations on CT data with inhomogeneity correction and on bulk density assigned MR data were small. The maximum difference in the number of monitor units required to reach the prescribed dose was 1.6%. This result also includes effects of possible geometrical distortions.ConclusionsThe dose calculation accuracy at the investigated treatment sites is not significantly compromised when using MRI data when adequate bulk density assignments are made. With respect to treatment planning, MRI can replace CT in all steps of the treatment workflow, reducing the radiation exposure to the patient, removing any systematic registration errors that may occur when combining MR and CT, and decreasing time and cost for the extra CT investigation.

DEDICATED MAGNETIC RESONANCE IMAGING IN THE RADIOTHERAPY CLINIC

PURPOSE To introduce a novel technology arrangement in an integrated environment and outline the logistics model needed to incorporate dedicated magnetic resonance (MR) imaging in the radiotherapy workflow. An initial attempt was made to analyze the value and feasibility of MR-only imaging compared to computed tomography (CT) imaging, testing the assumption that MR is a better choice for target and healthy tissue delineation in radiotherapy. METHODS AND MATERIALS A 1.5-T MR unit with a 70-cm-bore size was installed close to a linear accelerator, and a special trolley was developed for transporting patients who were fixated in advance between the MR unit and the accelerator. New MR-based workflow procedures were developed and evaluated. RESULTS MR-only treatment planning has been facilitated, thus avoiding all registration errors between CT and MR scans, but several new aspects of MR imaging must be considered. Electron density information must be obtained by other methods. Generation of digitally reconstructed radiographs (DRR) for x-ray setup verification is not straight forward, and reliable corrections of geometrical distortions must be applied. The feasibility of MR imaging virtual simulation has been demonstrated, but a key challenge to overcome is correct determination of the skeleton, which is often needed for the traditional approach of beam modeling. The trolley solution allows for a highly precise setup for soft tissue tumors without the invasive handling of radiopaque markers. CONCLUSIONS The new logistics model with an integrated MR unit is efficient and will allow for improved tumor definition and geometrical precision without a significant loss of dosimetric accuracy. The most significant development needed is improved bone imaging.

Voxel-wise uncertainty in CT substitute derived from MRI.

PURPOSE In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences. METHODS An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities. RESULTS The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation. CONCLUSIONS The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.

Technical Note: MRI only prostate radiotherapy planning using the statistical decomposition algorithm.

PURPOSE In order to enable a magnetic resonance imaging (MRI) only workflow in radiotherapy treatment planning, methods are required for generating Hounsfield unit (HU) maps (i.e., synthetic computed tomography, sCT) for dose calculations, directly from MRI. The Statistical Decomposition Algorithm (SDA) is a method for automatically generating sCT images from a single MR image volume, based on automatic tissue classification in combination with a model trained using a multimodal template material. This study compares dose calculations between sCT generated by the SDA and conventional CT in the male pelvic region. METHODS The study comprised ten prostate cancer patients, for whom a 3D T2 weighted MRI and a conventional planning CT were acquired. For each patient, sCT images were generated from the acquired MRI using the SDA. In order to decouple the effect of variations in patient geometry between imaging modalities from the effect of uncertainties in the SDA, the conventional CT was nonrigidly registered to the MRI to assure that their geometries were well aligned. For each patient, a volumetric modulated arc therapy plan was created for the registered CT (rCT) and recalculated for both the sCT and the conventional CT. The results were evaluated using several methods, including mean average error (MAE), a set of dose-volume histogram parameters, and a restrictive gamma criterion (2% local dose/1 mm). RESULTS The MAE within the body contour was 36.5 ± 4.1 (1 s.d.) HU between sCT and rCT. Average mean absorbed dose difference to target was 0.0% ± 0.2% (1 s.d.) between sCT and rCT, whereas it was -0.3% ± 0.3% (1 s.d.) between CT and rCT. The average gamma pass rate was 99.9% for sCT vs rCT, whereas it was 90.3% for CT vs rCT. CONCLUSIONS The SDA enables a highly accurate MRI only workflow in prostate radiotherapy planning. The dosimetric uncertainties originating from the SDA appear negligible and are notably lower than the uncertainties introduced by variations in patient geometry between imaging sessions.

A review of substitute CT generation for MRI-only radiation therapy.

Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is an area of growing scientific interest due to the increasing use of MRI for both target and normal tissue delineation and the development of MR based delivery systems. One major issue in MRI-only RT is the assignment of electron densities (ED) to MRI scans for dose calculation and a similar need for attenuation correction can be found for hybrid PET/MR systems. The ED assigned MRI scan is here named a substitute CT (sCT). In this review, we report on a collection of typical performance values for a number of main approaches encountered in the literature for sCT generation as compared to CT. A literature search in the Scopus database resulted in 254 papers which were included in this investigation. A final number of 50 contributions which fulfilled all inclusion criteria were categorized according to applied method, MRI sequence/contrast involved, number of subjects included and anatomical site investigated. The latter included brain, torso, prostate and phantoms. The contributions geometric and/or dosimetric performance metrics were also noted. The majority of studies are carried out on the brain for 5–10 patients with PET/MR applications in mind using a voxel based method. T1 weighted images are most commonly applied. The overall dosimetric agreement is in the order of 0.3–2.5%. A strict gamma criterion of 1% and 1mm has a range of passing rates from 68 to 94% while less strict criteria show pass rates > 98%. The mean absolute error (MAE) is between 80 and 200 HU for the brain and around 40 HU for the prostate. The Dice score for bone is between 0.5 and 0.95. The specificity and sensitivity is reported in the upper 80s% for both quantities and correctly classified voxels average around 84%. The review shows that a variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences. A consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.

Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information – potential application for MR-only radiotherapy and attenuation correction in positron emission tomography

Abstract Background. Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT. Material and methods. MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient. Results. The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities. Conclusions. This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.

Counterpoint: Opportunities and Challenges of a Magnetic Resonance Imaging–Only Radiotherapy Work Flow

Magnetic resonance (MR) imaging plays an important role in modern radiotherapy. The benefits of MR as compared with those of computed tomography for the definition of target volumes is evident for many soft tissue tumor types. It has been suggested that for these patient groups, the computed tomography examination is unnecessary as part of the preparation for radiotherapy. Here, we review the rationale for an MR-only radiotherapy work flow, as well as the technical challenges and solutions connected to it.

Treatment planning of intracranial targets on MRI derived substitute CT data

BACKGROUND The use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To eliminate systematic uncertainties due to image registration, a workflow based entirely on MRI may be preferable. In the present pilot study, we investigate dose calculation accuracy for automatically generated substitute CT (s-CT) images of the head based on MRI. We also produce digitally reconstructed radiographs (DRRs) from s-CT data to evaluate the feasibility of patient positioning based on MR images. METHODS AND MATERIALS Five patients were included in the study. The dose calculation was performed on CT, s-CT, s-CT data without inhomogeneity correction and bulk density assigned MRI images. Evaluation of the results was performed using point dose and dose volume histogram (DVH) comparisons, and gamma index evaluation. RESULTS The results demonstrate that the s-CT images improve the dose calculation accuracy compared to the method of non-inhomogeneity corrected dose calculations (mean improvement 2.0% points) and that it performs almost identically to the method of bulk density assignment. The s-CT based DRRs appear to be adequate for patient positioning of intra-cranial targets, although further investigation is needed on this subject. CONCLUSION The s-CT method is very fast and yields data that can be used for treatment planning without sacrificing accuracy.

ADC texture—An imaging biomarker for high‐grade glioma?

PURPOSE Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers. METHODS Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression. RESULTS The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001. CONCLUSIONS By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort.