pseudo-CT Images Research Articles

Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT

Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods.
 Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach.
 Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition.
 Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning

To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis.We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average HU and the ME HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of in the PTV for , and between and 0.05% in the PTV, bladder, rectum and femur heads for Dmean and .Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP.

EP-1676: Comparison of accuracy of Hounsfield units obtained from pseudo-CT and true CT images

EP-1676: Comparison of accuracy of Hounsfield units obtained from pseudo-CT and true CT images

Pseudo CT Estimation from MRI Using Patch-based Random Forest.

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

Quantification of confounding factors in MRI-based dose calculations as applied to prostate IMRT

Magnetic resonance (MR)-only radiotherapy treatment planning requires pseudo-CT (pCT) images to enable MR-based dose calculations. To verify the accuracy of MR-based dose calculations, institutions interested in introducing MR-only planning will have to compare pCT-based and computer tomography (CT)-based dose calculations. However, interpreting such comparison studies may be challenging, since potential differences arise from a range of confounding factors which are not necessarily specific to MR-only planning. Therefore, the aim of this study is to identify and quantify the contribution of factors confounding dosimetric accuracy estimation in comparison studies between CT and pCT. The following factors were distinguished: set-up and positioning differences between imaging sessions, MR-related geometric inaccuracy, pCT generation, use of specific calibration curves to convert pCT into electron density information, and registration errors.The study comprised fourteen prostate cancer patients who underwent CT/MRI-based treatment planning. To enable pCT generation, a commercial solution (MRCAT, Philips Healthcare, Vantaa, Finland) was adopted. IMRT plans were calculated on CT (gold standard) and pCTs. Dose difference maps in a high dose region (CTV) and in the body volume were evaluated, and the contribution to dose errors of possible confounding factors was individually quantified.We found that the largest confounding factor leading to dose difference was the use of different calibration curves to convert pCT and CT into electron density (0.7%). The second largest factor was the pCT generation which resulted in pCT stratified into a fixed number of tissue classes (0.16%). Inter-scan differences due to patient repositioning, MR-related geometric inaccuracy, and registration errors did not significantly contribute to dose differences (0.01%).The proposed approach successfully identified and quantified the factors confounding accurate MRI-based dose calculation in the prostate. This study will be valuable for institutions interested in introducing MR-only dose planning in their clinical practice.

49. Feasibility of direct VMAT treatment planning on MRI generated Pseudo-CT image

Introduction In radiation therapy, MRI images are necessary to precisely delineate patient’s structures.Several commercially available algorithms exist that generate Pseudo-CT (pCT) images, directly from MRI sequences, allowing dose calculation without realizing any supplementary CT-simulation acquisition.In this study, an optimized algorithm is used to produce the pCT images for pelvic regions.VMAT plans were then calculated using these images and compared to the calculations realized on the conventional CT (cCT) data. Methods Generally, the pCT image generation is realized by algorithms that work on existing patient databases (Atlas-based algorithms), that include CT and MRI series, already matched.A mean value (MV) image is generated from the atlas, and an elastic fusion is performed on the patient MRI, in order to produce the pCT image required.The new proposed algorithm realizes a locally weighted (LW) selection from the atlas images, before proceeding with the fusion and pCT generation. A retrospective study was realized, for a sample of 12 patients treated for rectal tumors.pCT images generated by classic algorithms (MV), by the new optimized algorithm (LW), as also homogeneous patient body images (HU=0) were inserted to the treatment planning system (TPS). The initial treated plan (reference) was re-calculated on the different image series, and the resulting dose matrices were compared in terms of DVH and 2D Gamma Index (GI). The absolute isodose volumes corresponding to the dose differences were also calculated. TPS: Eclipse v13 – Calculation algorithm: AAA v.10 – Energy: 6MegaVolt – Irradiation technique: VMAT – Calculation resolution: 2.5 × 2.5 mm Results The dosimetric comparison showed that calculations based on the new optimized algorithm (LW) are well precise.However, the difference between calculations on the different series is not clinically relevant.The GI based differences were only visible for 1% dose and 1mm distance criteria.In that case, the percentage of points with GI > 1 were (mean ± 1SD in %) 91.40 ± 7.56 CT-RT vs homogeneous image, 96.00 ± 4.11 CT-RT vs MV image (classic pCT), 97.67 ± 3.60, CT-RT vs LW image (improved pCT) The results for the mean volume of the dose differences are visible in the annex (Fig.1). Download : Download high-res image (91KB) Download : Download full-size image Conclusions The treatment planning on pCT images is possible and sufficiently precise of pelvic regions. The new algorithm proposed showed a promising potential for use on dose calculations in MRI-only treatment planning.

Dosimetric characterization of MRI-only treatment planning for brain tumors in atlas-based pseudo-CT images generated from standard T1-weighted MR images.

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning requires accurate pseudo-CT (pCT) images for precise dose calculation. The current work introduced an atlas-based method combined with MR intensity information. pCT analyses and Monte Carlo dose calculations for intracranial stereotactic treatments were performed. Twenty-two patients, representing 35 tumor targets, were scanned using a 3D T1-weighted MRI sequence according to the clinical protocol. The MR atlas image was registered to the MR patient image using a deformable algorithm, and the deformation was then applied to the atlas CT. Two methods were applied. The first method (MRdef) was based on deformations only, while the second (MRint) also used the actual MR intensities. pCT analysis was performed using the mean (absolute) error, as well as an in-house tool based on a gamma index. Dose differences between pCT and true CT were analyzed using dose-volume histogram (DVH) parameters, statistical tests, the gamma index, and probability density functions. An unusual case, where the patient underwent an operation (part of the skull bone was removed), was studied in detail. Soft tissues presented a mean error inferior to 50 HUs, while low-density tissues and bones presented discrepancies up to 600 HUs for hard bone. The MRdef method led to significant dose differences compared with the true CT (p-value < 0.05; Wilcoxon-signed-rank test). The MRint method performed better. The DVH parameter differences compared with CT were between -2.9% and 3.1%, except for two cases where the tumors were located within the sphenoid bone. For these cases, the dose errors were up to 6.6% and 5.4% (D98 and D95). Furthermore, for 85% of the tested patients, the mean dose to the planning target volume agreed within 2% with the calculation using the actual CT. Fictitious bone was generated in the unusual case using atlas-based methods. Generally, the atlas-based method led to acceptable dose distributions. The use of common T1 sequences allows the implementation of this method in clinical routine. However, unusual patient anatomy may produce large dose calculation errors. The detection of large anatomic discrepancies using MR image subtraction can be realized, but an alternative way to produce synthetic CT numbers in these regions is still required.

Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET–MRI-guided radiotherapy treatment planning

Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of −1.5 ± 5.0% (mean ± SD) in bony structures compared to −19.9 ± 11.8% and −8.1 ± 8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40 ± 7.56%, 96.00 ± 4.11% and 97.67 ± 3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.

Various approaches for pseudo-CT scan creation based on ultrasound to ultrasound deformable image registration between different treatment time points for radiotherapy treatment plan adaptation in prostate cancer patients

The purpose of this study was to evaluate eight possible approaches to create pseudo-CT images for radiotherapy (RT) treatment re-planning. These re-planning CT scans would normally require a separate CT scan session. If important changes occur in patient’s anatomy between simulation (SIM) and treatment (TX) stages, 3D ultrasound (US) images acquired at the two stages, available in US guided RT workflows, can be used to produce a deformation field. Proof of concept research showed that the application of this deformation field to the SIM CT image yields a pseudo-CT which can be more representative of the patient at TX than SIM CT. Co-registered CT and US volumes acquired at five different time points during the RT course of a prostate cancer patient were combined into data pairs, providing ground truth CT images (CTtx). Eight different methods were explored to create the deformation field that was used to produce the pseudo-CT scan. Anatomical structure comparison and γ index calculations were used to compare the similarity of the pseudo-CT volumes and the reference TX CT volumes. In five out of ten data pairs, all the eight approaches resulted in the creation of a pseudo-CT equally or more similar to the TX CT than the SIM CT within the region of interest, with an average improvement of 54.1% (range: 5.1%–126.5%) in dice similarity coefficient (DSC) and 32.3% (range: 0.3%–52.6%) in γ index. For the remaining data pairs, four up to seven approaches resulted in an improvement in both DSC (range: 4.3%–54%) and γ index (range: 0.8%–41.3%). In conclusion, at least four out of eight explored approaches resulted in more representative pseudo-CT images in all the data pairs. In particular, the approaches in which an initial rigid alignment was combined with deformable registration performed best.

Simulation of pseudo‐CT images based on deformable image registration of ultrasound images: A proof of concept for transabdominal ultrasound imaging of the prostate during radiotherapy

Imaging of patient anatomy during treatment is a necessity for position verification and for adaptive radiotherapy based on daily dose recalculation. Ultrasound (US) image guided radiotherapy systems are currently available to collect US images at the simulation stage (USsim), coregistered with the simulation computed tomography (CT), and during all treatment fractions. The authors hypothesize that a deformation field derived from US-based deformable image registration can be used to create a daily pseudo-CT (CTps) image that is more representative of the patients' geometry during treatment than the CT acquired at simulation stage (CTsim). The three prostate patients, considered to evaluate this hypothesis, had coregistered CT and US scans on various days. In particular, two patients had two US-CT datasets each and the third one had five US-CT datasets. Deformation fields were computed between pairs of US images of the same patient and then applied to the corresponding USsim scan to yield a new deformed CTps scan. The original treatment plans were used to recalculate dose distributions in the simulation, deformed and ground truth CT (CTgt) images to compare dice similarity coefficients, maximum absolute distance, and mean absolute distance on CT delineations and gamma index (γ) evaluations on both the Hounsfield units (HUs) and the dose. In the majority, deformation did improve the results for all three evaluation methods. The change in gamma failure for dose (γDose, 3%, 3 mm) ranged from an improvement of 11.2% in the prostate volume to a deterioration of 1.3% in the prostate and bladder. The change in gamma failure for the CT images (γCT, 50 HU, 3 mm) ranged from an improvement of 20.5% in the anus and rectum to a deterioration of 3.2% in the prostate. This new technique may generate CTps images that are more representative of the actual patient anatomy than the CTsim scan.

SU‐E‐J‐224: Using UTE and T1 Weighted Spin Echo Pulse Sequences for MR‐Only Treatment Planning; Phantom Study

Purpose:Investigating a new approach in MRI based treatment planning using the combination of (Ultrashort Echo Time) UTE and T1 weighted spin echo pulse sequences to delineate air, bone and water (soft tissues) in generating pseudo CT images comparable with CT.Methods:A gel phantom containing chicken bones, ping pang balls filled with distilled water and air bubbles, was made. It scanned with MRI using UTE and 2D T1W SE pulse sequences with (in plane resolution= 0.53mm, slice thickness= 2 mm) and CT with (in plane resolution= 0.5 mm and slice thickness= 0.75mm) as a ground truth for geometrical accuracy. The UTE and T1W SE images were registered with CT using mutual information registration algorithm provided by Philips Pinnacle treatment planning system. The phantom boundaries were detected using Canny edge detection algorithm for CT, and MR images. The bone, air bubbles and water in ping pong balls were segmented from CT images using threshold 300HU, ‐ 950HU and 0HU, respectively. These tissue inserts were automatically segmented from combined UTE and T1W SE images using edge detection and relative intensity histograms of the phantom. The obtained segmentations of air, bone and water inserts were evaluated with those obtained from CT.Results:Bone and air can be clearly differentiated in UTE images comparable to CT. Combining UTE and T1W SE images successfully segmented the air, bone and water. The maximum segmentation differences from combine MRI images (UTE and T1W SE) and CT are within 1.3 mm, 1.1mm for bone, air, respectively. The geometric distortion of UTE sequence is small less than 1 pixel (0.53 mm) of MR image resolution.Conclusion:Our approach indicates that MRI can be used solely for treatment planning and its quality is comparable with CT.

WE-AB-204-04: Feature Selection and Clustering Optimization for Pseudo-CT Generation in MR-Based Attenuation Correction and Radiation Therapy Planning

Purpose: Accurate and robust photon attenuation derived from MR is essential for PET/MR and MR-based radiation treatment planning applications. Although the fuzzy C-means (FCM) algorithm has been applied for pseudo-CT generation, the input feature combination and the number of clusters have not been optimized. This study aims to optimize both for clinically practical pseudo-CT generation. Methods: Nine volunteers were recruited. A 190-second, single-acquisition UTE-mDixon with 25% (angular) sampling and 3D radial readout was performed to acquire three primitive MR features at TEs of 0.1, 1.5, and 2.8 ms: the free-induction-decay (FID), the first and the second echo images. Three derived images, Dixon-fat and Dixon-water generated by two-point Dixon water/fat separation, and R2* (1/T2*) map, were also created. To identify informative inputs for generating a pseudo-CT image volume, all 63 combinations, choosing one to six of the feature images, were used as inputs to FCM for pseudo-CT generation. Further, the number of clusters was varied from four to seven to find the optimal approach. Mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R) of different combinations were compared for feature selection. Results: Among the 63 feature combinations, the four that resulted in the best MAPD and R were further compared along with the set containing all six features. The results suggested that R2* and Dixon-water are the most informative features. Further, including FID also improved the performance of pseudo-CT generation. Consequently, the set containing FID, Dixon-water, and R2* resulted in the most accurate, robust pseudo-CT when the number of cluster equals to five (5C). The clusters were interpreted as air, fat, bone, brain, and fluid. The six-cluster Result additionally included bone marrow. Conclusion: The results suggested that FID, Dixon-water, R2* are the most important features. The findings can be used to facilitate pseudo-CT generation for unsupervised clustering. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees

CT synthesis in the head & neck region for PET/MR attenuation correction: an iterative multi-atlas approach.

In this work, we propose to tackle the problem of attenuation correction in the head and neck by synthesising CT from MR images using an iterative multi-atlas approach. The proposed method relies on pre-acquired T2-weighted MRI and CT images of the neck. For each subject, the MRI is non-rigidly mapped to the CT. To synthesise a pseudo CT, all the MRIs in the database are first registered to the target MRI. This registration consists of a robust affine followed by a non-rigid registration. The pseudo CT is obtained by fusing the mapped atlases according to their morphological similarity to the target. In contrast to CTs, T2 images do not provide a good estimate of the bone location. Combining multiple modalities at both the registration and image similarity stages is expected to provide more realistic mappings and to reduce the bias. An initial pseudo CT (pCT) is combined with the target MRI to form a MRI-pCT pair. The MRI-pCT pair is registered to all the MRI-CT pairs from the database. An improved pseudo CT is obtained by fusing the mapped MRI-CT pairs according to their morphological similarity to the target MRI-pCT pair. Results showed that the proposed CT synthesis algorithm based on a multi-atlas information propagation scheme and iterative process is able to synthesise pseudo CT images in a region challenging for registration algorithms. The results also demonstrate that the robust affine decreases the absolute error compared to the classic approach and that the bone refinement process reduces the bias in the bone region. The proposed method could be used to correct for attenuation PET/MR data, but also for dosimetry calculations in the context of MR-based radiotherapy treatment planning.

Advanced digital subtraction angiography and MR fusion imaging protocol applied to accurate placement of flow diverter device

In recent years there has been a progressive increase in interventional neuroradiology procedures, partially due to improvements in devices, but also to the simultaneous development of technologies and radiological images....

Pseudo-CT generation from multiple MR images for small animal irradiation

Introduction. Computed tomography (CT) is the standard imaging modality for radiation therapy treatment planning (RTTP) because of its ability to provide information on electron density. However, magnetic resonance (MR) imaging provides superior soft tissue contrast, especially in small animal imaging, facilitating the precise selection of the target volume. This makes the technique interesting for irradiation of brain tumors. The aim of this study was to present an MR-only based workflow for RTTP on a small animal radiation research platform (SARRP) by investigating the potential of probabilistic classification of voxels using multiple MR sequences. Methods. Six female Fisher rats were anesthetized using isoflurane and individually fixed on an in-house made multimodality bed before starting MR and CT acquisitions. MR measurements were performed on a 7-Tesla system using a rat brain volume coil. Four different MR sequences were acquired for each animal, including a T1-weighted (MDEFT) sequence, a T2-weighted (RARE) sequence, an ultra-short echo time sequence with 20 μs echo time (UTE1) and an ultra- short echo time sequence with 2 ms echo time (UTE2). UTE offers the opportunity to acquire images from proton-poor structures with very short transverse relaxation times, such as bone, by using a rapid readout of the fast decaying signal. Following MR, the animals were moved to the SARRP to start a cone-beam CT (CB-CT) by acquiring 720 projections over 360°. Cone-beam CT projection data were reconstructed by filtered back-projection to obtain the standard-CT for RTTP. Then the images were bias field corrected and manually co-registered to the CB-CT. After that, images were segmented in three tissue classes (air, soft tissue and bone) with k-means for the CB-CT and fuzzy c-means segmentation algorithm (FCM) for the MR images with multiple MR images as input. The membership probability can be between 0 and 1, with one indicating 100% probability and zero indicating 0% probability to belong to a specific tissue class. To obtain a pseudo-CT image, voxels were assigned to the tissue class having the highest membership probability. The dice coefficient was used to evaluate the correctness of the segmentation for soft-tissue and bone. The pseudo-CT images with the highest similarity index were used for further radiotherapy treatment planning (RTTP), in addition, to the standard UTE1-UTE2. The target of the RTTP that was selected in the primary cortex (M1) and three different beam arrangements were investigated to compare CB-CT and MR-based dose calculations. The dose plans were a single static beam of 3x3 mm, using a single arc (3x3 mm beam size, 120° arc, couch at 0°), and three non-coplanar arcs (3x3 mm beam size, 120° arc, couch at 0°, 45° and 90°). Dose distributions were calculated using the TPS of the SARRP and cumulative dose volume histograms (DVHs) of the target and normal brain tissue were obtained for the three dose plans. Results The highest dice coefficient was obtained for the T1-UTE1-T2 combination, which was used for further RTTP. The contribution of bone to the total dice coefficient did not exceed 27%. However, bone accounts for only 2% of the image, therefore a misclassified bone pixel has a bigger effect in the dice coefficient than a misclassified soft tissue pixel. Using only 1 beam, both MR combinations underestimate the dose to be delivered to the target. When more complex beam configurations were used to irradiate the target, very small differences were observed between CB-CT and MR based dose calculations.

Feasibility of MRI-based reference images for image-guided radiotherapy of the pelvis with either cone-beam computed tomography or planar localization images

Purpose. This study introduces methods to conduct image-guided radiotherapy (IGRT) of the pelvis with either cone-beam computed tomography (CBCT) or planar localization images by relying solely on magnetic resonance imaging (MRI)-based reference images.Material and methods. Feasibility of MRI-based reference images for IGRT was evaluated against kV CBCT (50 scans, 5 prostate cancer patients) and kV & MV planar (5 & 5 image pairs and patients) localization images by comparing the achieved patient position corrections to those obtained by standard CT-based reference images. T1/T2*-weighted in-phase MRI, Hounsfield unit conversion-based heterogeneous pseudo-CT, and bulk pseudo-CT images were applied for reference against localization CBCTs, and patient position corrections were obtained by automatic image registration. IGRT with planar localization images was performed manually by 10 observers using reference digitally reconstructed radiographs (DRRs) reconstructed from the pseudo-CTs and standard CTs. Quality of pseudo-DRRs against CT-DRRs was evaluated with image similarity metrics.Results. The SDs of differences between CBCT-to-MRI and CBCT-to-CT automatic gray-value registrations were ≤ 1.0 mm & ≤ 0.8° and ≤ 2.5 mm & ≤ 3.6° with 10 cm diameter cubic VOI and prostate-shaped VOI, respectively. The corresponding values for reference heterogeneous pseudo-CT were ≤ 1.0 mm & ≤ 0.7° and ≤ 2.2 mm & ≤ 3.3°, respectively. Heterogeneous pseudo-CT was the only type of MRI-based reference image working reliably with automatic bone registration (SDs were ≤ 0.9 mm & ≤ 0.7°). The differences include possible residual errors from planning CT to MRI registration. The image similarity metrics were significantly (p ≤ 0.01) better in agreement between heterogeneous pseudo-DRRs and CT-DRRs than between bulk pseudo-DRRs and CT-DRRs. The SDs of differences in manual registrations (3D) with planar kV and MV localization images were ≤ 1.0 mm and ≤ 1.7 mm, respectively, between heterogeneous pseudo-DRRs and CT-DRRs, and ≤ 1.4 mm and ≤ 2.1 mm between bulk pseudo-DRRs and CT-DRRs.Conclusion. This study demonstrated that it is feasible to conduct IGRT of the pelvis with MRI-based reference images.

A deformable atlas of the laboratory mouse.

This paper presents a deformable mouse atlas of the laboratory mouse anatomy. This atlas is fully articulated and can be positioned into arbitrary body poses. The atlas can also adapt body weight by changing body length and fat amount. A training set of 103 micro-CT images was used to construct the atlas. A cage-based deformation method was applied to realize the articulated pose change. The weight-related body deformation was learned from the training set using a linear regression method. A conditional Gaussian model and thin-plate spline mapping were used to deform the internal organs following the changes of pose and weight. The atlas was deformed into different body poses and weights, and the deformation results were more realistic compared to the results achieved with other mouse atlases. The organ weights of this atlas matched well with the measurements of real mouse organ weights. This atlas can also be converted into voxelized images with labeled organs, pseudo CT images and tetrahedral mesh for phantom studies. With the unique ability of articulated pose and weight changes, the deformable laboratory mouse atlas can become a valuable tool for preclinical image analysis.

Influence of MRI-based bone outline definition errors on external radiotherapy dose calculation accuracy in heterogeneous pseudo-CT images of prostate cancer patients

Background. This work evaluates influences of susceptibility-induced bone outline shift and perturbations, and bone segmentation errors on external radiotherapy dose calculation accuracy in magnetic resonance imaging (MRI)-based pseudo-computed tomography (CT) images of the male pelvis.Material and methods. T1/T2*-weighted fast gradient echo, T1-weighted spin echo and T2-weighted fast spin echo images were used in bone detection investigation. Bone edge location and bone diameter in MRI were evaluated by comparing those in the images with actual physical measurements of fresh deer bones positioned in a gelatine phantom. Dose calculation accuracy in pseudo-CT images was investigated for 15 prostate cancer patients. Bone outlines in T1/T2*-weighted images were contoured and additional segmentation errors were simulated by expanding and contracting the bone contours with 1 mm spacing. Heterogeneous pseudo-CT images were constructed by adopting a technique transforming the MRI intensity values into Hounsfield units with separate conversion models within and outside of bone segment.Results. Bone edges and diameter in the phantom were illustrated correctly within a 1 mm-pixel size in MRI. Each 1 mm-sized systematic error in bone segment resulted in roughly 0.4% change to the prostate dose level in the pseudo-CT images. The prostate average (range) dose levels in pseudo-CT images with additional systematic bone segmentation errors of −2 mm, 0 mm and 2 mm were 0.5% (−0.5–1.4%), −0.2% (−1.0–0.7%), and −0.9% (−1.8–0.0%) compared to those in CT images, respectively, in volumetric modulated arc therapy treatment plans calculated by Monte Carlo algorithm.Conclusions. Susceptibility-induced bone outline shift and perturbations do not result in substantial uncertainty for MRI-based dose calculation. Dose consistency of 2% can be achieved reliably for the prostate if heterogeneous pseudo-CT images are constructed with ≤± 2 mm systematic error in bone segment.

A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer

The lack of electron density information in magnetic resonance images (MRI) poses a major challenge for MRI-based radiotherapy treatment planning (RTP). In this study the authors convert MRI intensity values into Hounsfield units (HUs) in the male pelvis and thus enable accurate MRI-based RTP for prostate cancer patients with varying tissue anatomy and body fat contents. T1/T2*-weighted MRI intensity values and standard computed tomography (CT) image HUs in the male pelvis were analyzed using image data of 10 prostate cancer patients. The collected data were utilized to generate a dual model HU conversion technique from MRI intensity values of the single image set separately within and outside of contoured pelvic bones. Within the bone segment local MRI intensity values were converted to HUs by applying a second-order polynomial model. This model was tuned for each patient by two patient-specific adjustments: MR signal normalization to correct shifts in absolute intensity level and application of a cutoff value to accurately represent low density bony tissue HUs. For soft tissues, such as fat and muscle, located outside of the bone contours, a threshold-based segmentation method without requirements for any patient-specific adjustments was introduced to convert MRI intensity values into HUs. The dual model HU conversion technique was implemented by constructing pseudo-CT images for 10 other prostate cancer patients. The feasibility of these images for RTP was evaluated by comparing HUs in the generated pseudo-CT images with those in standard CT images, and by determining deviations in MRI-based dose distributions compared to those in CT images with 7-field intensity modulated radiation therapy (IMRT) with the anisotropic analytical algorithm and 360° volumetric-modulated arc therapy (VMAT) with the Voxel Monte Carlo algorithm. The average HU differences between the constructed pseudo-CT images and standard CT images of each test patient ranged from -2 to 5 HUs and from 22 to 78 HUs in soft and bony tissues, respectively. The average local absolute value differences were 11 HUs in soft tissues and 99 HUs in bones. The planning target volume doses (volumes 95%, 50%, 5%) in the pseudo-CT images were within 0.8% compared to those in CT images in all of the 20 treatment plans. The average deviation was 0.3%. With all the test patients over 94% (IMRT) and 92% (VMAT) of dose points within body (lower than 10% of maximum dose suppressed) passed the 1 mm and 1% 2D gamma index criterion. The statistical tests (t- and F-tests) showed significantly improved (p ≤ 0.05) HU and dose calculation accuracies with the soft tissue conversion method instead of homogeneous representation of these tissues in MRI-based RTP images. This study indicates that it is possible to construct high quality pseudo-CT images by converting the intensity values of a single MRI series into HUs in the male pelvis, and to use these images for accurate MRI-based prostate RTP dose calculations.

SU‐E‐J‐34: A Multi‐Modality Image Atlas for Transforming An MR Image Into a Pseudo CT Image for MRI‐Based IGRT Application

Purpose: IGRT tools are most useful when registering similar datasets. In particular it is difficult to automatically register reference MR images with treatment CT or CBCT images. We proposed to create a pseudo CT from the reference MR images and to register the pseudo CT to the treatment CT. Methods: Two mice were sacrificed and immobilized on a rigid platform, and then imaged with small animal T1 and T2 weighted MRI and CBCT. Data from the first mouse was to develop an atlas and data from the 2nd mouse was to validate the system. To create a pseudo CT from an MRI, we first established the intensity conversion curve between the atlas CBCT and mouse MRI. This intensity conversion curve maps the intensity of fat tissue and soft tissue of the MRI onto the atlas CBCT intensity of the same tissue type. We used this conversion curve to create an intensity‐corrected MRI. Next, using global affine registration, local (bone‐by‐bone) affine registration, and deformable registration, we calculated the deformation field linking bony regions in the atlas MRI and the validation MRI. This deformation field was used to deform the CBCT image inside the atlas bone contour and to replace that section of the image in the validation mouse MRI. Results: The pseudo CT and validation CBCT are visually similar. Soft tissue in the pseudo CT is more recognizable and differentiable than in the validation CBCT. Contouring the bone structure on both images, the Dice index of the two bone structures was found to be 75%. The cross correlation of the image contents inside the fat‐soft tissue region between the pseudo CT and validation CBCT was 0.85. Conclusion: A novel approach was developed to create pseudo CT images from MR images that has potential applications in both small animal and human IGRT.