Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

Background and purposeComputed tomography (CT) imaging is the current gold standard for radiotherapy treatment planning (RTP). The establishment of a magnetic resonance imaging (MRI) only RTP workflow requires the generation of a synthetic CT (sCT) for dose calculation. This study evaluates the feasibility of using a multi-atlas sCT synthesis approach (sCT_a) for head and neck and prostate patients.Material and methodsThe multi-atlas method was based on pairs of non-rigidly aligned MR and CT images. The sCT_a was obtained by registering the MRI atlases to the patient’s MRI and by fusing the mapped atlases according to morphological similarity to the patient. For comparison, a bulk density assignment approach (sCT_bda) was also evaluated. The sCT_bda was obtained by assigning density values to MRI tissue classes (air, bone and soft-tissue). After evaluating the synthesis accuracy of the sCTs (mean absolute error), sCT-based delineations were geometrically compared to the CT-based delineations. Clinical plans were re-calculated on both sCTs and a dose-volume histogram and a gamma analysis was performed using the CT dose as ground truth.ResultsResults showed that both sCTs were suitable to perform clinical dose calculations with mean dose differences less than 1% for both the planning target volume and the organs at risk. However, only the sCT_a provided an accurate and automatic delineation of bone.ConclusionsCombining MR delineations with our multi-atlas CT synthesis method could enable MRI-only treatment planning and thus improve the dosimetric and geometric accuracy of the treatment, and reduce the number of imaging procedures.     

A practical methodology to improve the dosimetric accuracy of MR-based radiotherapy simulation for brain tumors

PurposeTo investigate the dosimetric accuracy of synthetic computed tomography (sCT) images generated by a clinically-ready voxel-based MRI simulation package, and to develop a simple and feasible method to improve the accuracy.Methods20 patients with brain tumor were selected to undergo CT and MRI simulation. sCT images were generated by a clinical MRI simulation package. The discrepancy between planning CT and sCT in CT number and body contour were evaluated. To resolve the discrepancies, an sCT specific CT-relative electron density (RED) calibration curve was used, and a layer of pseudo-skin was created on the sCT. The dosimetric impact of these discrepancies, and the improvement brought about by the modifications, were evaluated by a planning study. Volumetric modulated arc therapy (VMAT) treatment plans for each patient were created and optimized on the planning CT, which were then transferred to the original sCT and the modified-sCT for dose re-calculation. Dosimetric comparisons and gamma analysis between the calculated doses in different images were performed.ResultsThe average gamma passing rate with 1%/1 mm criteria was only 70.8% for the comparison of dose distribution between planning CT and original sCT. The mean dose difference between the planning CT and the original sCT were −1.2% for PTV D₉₅ and −1.7% for PTV D_max, while the mean dose difference was within 0.7 Gy for all relevant OARs. After applying the modifications on the sCT, the average gamma passing rate was increased to 92.2%. Mean dose difference in PTV D₉₅ and D_max were reduced to −0.1% and −0.3% respectively. The mean dose difference was within 0.2 Gy for all OAR structures and no statistically significant difference were found.ConclusionsThe modified-sCT demonstrated improved dosimetric agreement with the planning CT. These results indicated the overall dosimetric accuracy and practicality of this improved MR-based treatment planning method.     

A study to determine the impact of IMPT optimization techniques on prostate synthetic CT image sets dose comparison against CT image sets

BackgroundThe objective of this study is to determine the impact of intensity modulated proton therapty (IMPT) optimization techniques on the proton dose comparison of commercially available magnetic resonance for calculating attenuation (MRCA T) images, a synthetic computed tomography CT (sCT) based on magnetic resonance imaging (MRI) scan against the CT images and find out the optimization technique which creates plans with the least dose differences against the regular CT image sets.Material and methodsRegular CT data sets and sCT image sets were obtained for 10 prostate patients for the study. Six plans were created using six distinct IMPT optimization techniques including multi-field optimization (MFO), single field uniform dose (SFUD) optimization, and robust optimization (RO) in CT image sets. These plans were copied to MRCA T, sCT datasets and doses were computed. Doses from CT and MRCA T data sets were compared for each patient using 2D dose distribution display, dose volume histograms (DVH), homogeneity index (HI), conformation number (CN) and 3D gamma analysis. A two tailed t-test was conducted on HI and CN with 5% significance level with a null hypothesis for CT and sCT image sets.ResultsAnalysis of ten CT and sCT image sets with different IMPT optimization techniques shows that a few of the techniques show significant differences between plans for a few evaluation parameters. Isodose lines, DVH, HI, CN and t-test analysis shows that robust optimizations with 2% range error incorporated results in plans, when re-computed in sCT image sets results in the least dose differences against CT plans compared to other optimization techniques. The second best optimization technique with the least dose differences was robust optimization with 5% range error.ConclusionThis study affirmatively demonstrates the impact of IMPT optimization techniques on synthetic CT image sets dose comparison against CT images and determines the robust optimization with 2% range error as the optimization technique which gives the least dose difference when compared to CT plans.     

Evaluating the accuracy of geometrical distortion correction of magnetic resonance images for use in intracranial brain tumor radiotherapy

AimDetermine the 1) effectiveness of correction for gradient-non-linearity and susceptibility effects on both QUASAR GRID^3D and CIRS phantoms; and 2) the magnitude and location of regions of residual distortion before and after correction.BackgroundUsing magnetic resonance imaging (MRI) as a primary dataset for radiotherapy planning requires correction for geometrical distortion and non-uniform intensity.Materials and MethodsPhantom Study: MRI, computed tomography (CT) and cone beam CT images of QUASAR GRID^3D and CIRS head phantoms were acquired. Patient Study: Ten patients were MRI-scanned for stereotactic radiosurgery treatment. Correction algorithm: Two magnitude and one phase difference image were acquired to create a field map. A MATLAB program was used to calculate geometrical distortion in the frequency encoding direction, and 3D interpolation was applied to resize it to match 3D T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images. MPRAGE images were warped according to the interpolated field map in the frequency encoding direction. The corrected and uncorrected MRI images were fused, deformable registered, and a difference distortion map generated.ResultsMaximum deviation improvements: GRID^3D, 0.27 mm y-direction, 0.07 mm z-direction, 0.23 mm x-direction. CIRS, 0.34 mm, 0.1 mm and 0.09 mm at 20-, 40- and 60-mm diameters from the isocenter. Patient data show corrections from 0.2 to 1.2 mm, based on location. The most-distorted areas are around air cavities, e.g. sinuses.ConclusionsThe phantom data show the validity of our fast distortion correction algorithm. Patient-specific data are acquired in <2 min and analyzed and available for planning in less than a minute.     

Validation of dose distribution computation on sCT images generated from MRI scans by Philips MRCAT

AimTo evaluate calculation of treatment plans based on synthetic-CT (sCT) images generated from MRI.BackgroundBecause of better soft tissue contrast, MR images are used in addition to CT images for radiotherapy planning. However, registration of CT and MR images or repositioning between scanning sessions introduce systematic errors, hence suggestions for MRI-only therapy. The lack of information on electron density necessary for dose calculation leads to sCT (synthetic CT) generation. This work presents a comparison of dose distribution calculated on standard CT and sCT.Materials and methods10 prostate patients were included in this study. CT and MR images were collected for each patient and then water equivalent (WE) and MRCAT images were generated. The radiation plans were optimized on CT and then recalculated on MRCAT and WE data. 2D gamma analysis was also performed.ResultsThe mean differences in the majority of investigated DVH points were in order of 1% up to 10%, including both MRCAT and WE dose distributions. Mean gamma pass for acceptance criteria 1%/1 mm were greater than 82.5%. Prescribed doses for target volumes and acceptable doses for organs at risk were met in almost all cases.ConclusionsThe dose calculation accuracy on MRCAT was not significantly compromised in the majority of clinical relevant DVH points. The introduction of MRCAT into practise would eliminate systematic errors, increase patients’ comfort and reduce treatment expenses. Institutions interested in MRCAT commissioning must, however, consider changes to established workflow.     

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

PurposeIn radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation. This work reviewed deep learning (DL) sCT generation methods and their associated image and dose evaluation, in the context of MRI-based dose calculation.MethodsWe searched the PubMed and ScienceDirect electronic databases from January 2010 to March 2021. For each paper, several items were screened and compiled in figures and tables.ResultsThis review included 57 studies. The DL methods were either generator-only based (45% of the reviewed studies), or generative adversarial network (GAN) architecture and its variants (55% of the reviewed studies). The brain and pelvis were the most commonly investigated anatomical localizations (39% and 28% of the reviewed studies, respectively), and more rarely, the head-and-neck (H&N) (15%), abdomen (10%), liver (5%) or breast (3%). All the studies performed an image evaluation of sCTs with a diversity of metrics, with only 36 studies performing dosimetric evaluations of sCT.ConclusionsThe median mean absolute errors were around 76 HU for the brain and H&N sCTs and 40 HU for the pelvis sCTs. For the brain, the mean dose difference between the sCT and the reference CT was <2%. For the H&N and pelvis, the mean dose difference was below 1% in most of the studies. Recent GAN architectures have advantages compared to generator-only, but no superiority was found in term of image or dose sCT uncertainties. Key challenges of DL-based sCT generation methods from MRI in radiotherapy is the management of movement for abdominal and thoracic localizations, the standardization of sCT evaluation, and the investigation of multicenter impacts.     

Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning

AimThe aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning.BackgroundDespite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan.Materials and methodsIn this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated.ResultsThe MAE of a range of 45–55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4% based on the polar coordinate feature and proposed polynomial regression model.ConclusionThe proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria.     

Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

PurposeImage-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions.MethodsUsing a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D₉₈, D₅₀ and D₂ of the target volume) and 3D-gamma (3%/3mm) analysis were performed.ResultsThe mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 ± 8.3% (seminal vesicles) and 94.9 ± 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases.ConclusionsThe presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCT-based ART is fulfilled.     

Accuracy of X-Ray and Magnetic Resonance Imaging in Defining the Tumor Margin in Primary Bone Sarcoma

BackgroundLimb-salvage surgery for primary bone sarcomas are preceded by X-ray and MRI for surgical planning. However, the accuracy of X-ray and MRI predicted margins are not well described. Our study examined these questions: (1) How accurately do X-ray and MRI margin measurements reflect the true margin on pathology reports? (2) Do X-ray or MRI margin measurements have smaller differences compared to pathology reports? (3) How many X-ray or MRI margin measurement differences were greater than 1 cm, 2 cm, and 3 cm from pathology reports? (4) Is there an X-ray or MRI view that consistently results in a smaller difference from pathology reports?MethodsThis retrospective chart review examined patients with primary bone sarcoma treated with limb-salvage surgery. Reviewers used electronic measurement tools to determine margins from X-ray or MRI based on the resection length of the pathologic specimen. Mean differences of margin measurements to pathology reports were calculated. We determined outliers of imaging margin measurements at 1 cm, 2 cm, and 3 cm differences to pathology reports.ResultsIn the total cohort of 39 patients, the mean difference of X-ray and MRI margins compared to pathology reports were 1.09 cm (st dev 0.79 cm) and 0.71 cm (st dev 0.70 cm), respectively. MRI margin measurements had smaller differences compared to pathology reports than X-ray in 32 of 38 cases (84%) with complete imaging. X-ray outliers at 1 cm, 2 cm, and 3 cm differences were 36, 14 and 2 respectively for 70 margin measurements and MRI outliers at 1 cm, 2 cm, and 3 cm differences were 17, 6, and 0 respectively for 66 margin measurements. The views with the smallest difference were anterior-posterior X-rays and MRI views with the closest predicted margin.ConclusionElectronic MRI margin measurements with the closest predicted margin provided the smallest differences with pathology reports and are therefore the most accurate for preoperative planning. When there is adequate residual diaphysis for reconstructive fixation, surgeons should plan for a 3 cm bone margin using MRI measurements to ensure complete removal of the intramedullary extent of sarcoma.Level of Evidence: IV     

Comparison of different deep learning architectures for synthetic CT generation from MR images

PurposeAmong the different available methods for synthetic CT generation from MR images for the task of MR-guided radiation planning, the deep learning algorithms have and do outperform their conventional counterparts. In this study, we investigated the performance of some most popular deep learning architectures including eCNN, U-Net, GAN, V-Net, and Res-Net for the task of sCT generation. As a baseline, an atlas-based method is implemented to which the results of the deep learning-based model are compared.MethodsA dataset consisting of 20 co-registered MR-CT pairs of the male pelvis is applied to assess the different sCT production methods' performance. The mean error (ME), mean absolute error (MAE), Pearson correlation coefficient (PCC), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics were computed between the estimated sCT and the ground truth (reference) CT images.ResultsThe visual inspection revealed that the sCTs produced by eCNN, V-Net, and ResNet, unlike the other methods, were less noisy and greatly resemble the ground truth CT image. In the whole pelvis region, the eCNN yielded the lowest MAE (26.03 ± 8.85 HU) and ME (0.82 ± 7.06 HU), and the highest PCC metrics were yielded by the eCNN (0.93 ± 0.05) and ResNet (0.91 ± 0.02) methods. The ResNet model had the highest PSNR of 29.38 ± 1.75 among all models. In terms of the Dice similarity coefficient, the eCNN method revealed superior performance in major tissue identification (air, bone, and soft tissue).ConclusionsAll in all, the eCNN and ResNet deep learning methods revealed acceptable performance with clinically tolerable quantification errors.     

Geometric inaccuracy and co-registration errors for CT,DynaCT and MRI images used in robotic stereotactic radiosurgery treatment planning

PurposeTo measure the combined errors due to geometric inaccuracy and image co-registration on secondary images (dynamic CT angiography (dCTA), 3D DynaCT angiography (DynaCTA), and magnetic resonance images (MRI)) that are routinely used to aid in target delineation and planning for stereotactic radiosurgery (SRS).MethodsThree phantoms (one commercial and two in-house built) and two different analysis approaches (commercial and MATLAB based) were used to quantify the magnitude of geometric image distortion and co-registration errors for different imaging modalities within CyberKnife’s MultiPlan treatment planning software. For each phantom, the combined errors were reported as a mean target registration error (TRE). The mean TRE’s for different intramodality imaging parameters (e.g., mAs, kVp, and phantom set-ups) and for dCTA, DynaCTA, and MRI systems were measured.ResultsOnly X-ray based imaging can be performed with the commercial phantom, and the mean TRE ± standard deviation values were large compared to the in-house analysis using MATLAB. With the 3D printed phantom, even drastic changes in treatment planning CT imaging protocols did not greatly influence the mean TRE (<0.5 mm for a 1 mm slice thickness CT). For all imaging modalities, the largest mean TRE was found on DynaCT, followed by T2-weighted MR images (albeit all <1 mm).ConclusionsThe user may overestimate the mean TRE if the commercial phantom and MultiPlan were used solely. The 3D printed phantom design is a sensitive and suitable quality assurance tool for measuring 3D geometric inaccuracy and co-registration errors across all imaging modalities.     

Quantitative Susceptibility Mapping Differentiates between Blood Depositions and Calcifications in Patients with Glioblastoma

Objectives

The application of susceptibility weighted imaging (SWI) in brain tumor imaging is mainly used to assess tumor-related “susceptibility based signals” (SBS). The origin of SBS in glioblastoma is still unknown, potentially representing calcifications or blood depositions. Reliable differentiation between both entities may be important to evaluate treatment response and to identify glioblastoma with oligodendroglial components that are supposed to present calcifications. Since calcifications and blood deposits are difficult to differentiate using conventional MRI, we investigated whether a new post-processing approach, quantitative susceptibility mapping (QSM), is able to distinguish between both entities reliably.

Materials and Methods

SWI, FLAIR, and T1-w images were acquired from 46 patients with glioblastoma (14 newly diagnosed, 24 treated with radiochemotherapy, 8 treated with radiochemotherapy and additional anti-angiogenic medication). Susceptibility maps were calculated from SWI data. All glioblastoma were evaluated for the appearance of hypointense or hyperintense correlates of SBS on the susceptibility maps.

Results

43 of 46 glioblastoma presented only hyperintense intratumoral SBS on susceptibility maps, indicating blood deposits. Additional hypointense correlates of tumor-related SBS on susceptibility maps, indicating calcification, were identified in 2 patients being treated with radiochemotherapy and in one patient being treated with additional anti-angiogenic medication. Histopathologic reports revealed an oligodendroglial component in one patient that presented calcifications on susceptibility maps.

Conclusions

QSM provides a quantitative, local MRI contrast, which reliably differentiates between blood deposits and calcifications. Thus, quantitative susceptibility mapping appears promising to identify rare variants of glioblastoma with oligodendroglial components non-invasively and may allow monitoring the role of calcification in the context of different therapy regimes.     

Segmentation of parotid glands from registered CT and MR images

PurposeTo develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.MethodsMagnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.ResultsUsing the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.ConclusionsAutomatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.     

Clinical implementation of a Monte Carlo based treatment plan QA platform for validation of Cyberknife and Tomotherapy treatments

PurposeThe main focus of the current paper is the clinical implementation of a Monte Carlo based platform for treatment plan validation for Tomotherapy and Cyberknife, without adding additional tasks to the dosimetry department.MethodsThe Monte Carlo platform consists of C++ classes for the actual functionality and a web based GUI that allows accessing the system using a web browser. Calculations are based on BEAMnrc/DOSXYZnrc and/or GATE and are performed automatically after exporting the dicom data from the treatment planning system. For Cyberknife treatments of moving targets, the log files saved during the treatment (position of robot, internal fiducials and external markers) can be used in combination with the 4D planning CT to reconstruct the actually delivered dose. The Monte Carlo platform is also used for calculation on MRI images, using pseudo-CT conversion.ResultsFor Tomotherapy treatments we obtain an excellent agreement (within 2%) for almost all cases. However, we have been able to detect a problem regarding the CT Hounsfield units definition of the Toshiba Large Bore CT when using a large reconstruction diameter. For Cyberknife treatments we obtain an excellent agreement with the Monte Carlo algorithm of the treatment planning system. For some extreme cases, when treating small lung lesions in low density lung tissue, small differences are obtained due to the different cut-off energy of the secondary electrons.ConclusionsA Monte Carlo based treatment plan validation tool has successfully been implemented in clinical routine and is used to systematically validate all Cyberknife and Tomotherapy plans.     

Predictive Value of Calcium Test for Preoperative Diagnosis of Medullary Thyroid Carcinoma in Patients With Moderately Elevated Basal Calcitonin

ObjectiveMedullary thyroid carcinoma (MTC) can be very aggressive, and early diagnosis is based on routine measurement of serum calcitonin (CT) and RET genetic testing for hereditary forms. Basal serum CT (bCT) concentrations are useful in the early detection of MTC, although it is still unclear whether they can also be used for the differential diagnosis between MTC and C-cell hyperplasia (CCH). Since false-positive results can be obtained with the basal measurement of CT, a provocative test to evaluate stimulated CT (sCT) is often needed. The objective of this study was to investigate the utility of a calcium gluconate test for CT in distinguishing MTC from CCH, a precancerous condition in hereditary forms of MTCs but with unclear significance in sporadic MTCs.MethodsA total of 74 patients underwent the calcium loading test before thyroidectomy, and bCT and sCT levels were compared with histologic results by receiver operating characteristic plot analyses.ResultsA peak CT level of 388.4 pg/mL after stimulation with calcium gluconate was able to significantly distinguish patients with MTC from those with CCH and those without C-cell pathology, with 81.8% sensitivity and 36.5% specificity. A bCT level of 16.1 pg/mL was able to distinguish between these 2 groups of patients with a sensitivity of 90%.ConclusionHigh-dose calcium test is an effective procedure that can be applied for differential diagnosis of MTC and CCH. Reference ranges for calcium sCT levels and CT thresholds in different groups of patients have been identified.     

Optimizing MRI sequences and images for MRI-based stereotactic radiosurgery treatment planning

AimDevelopment of MRI sequences and processing methods for the production of images appropriate for direct use in stereotactic radiosurgery (SRS) treatment planning.BackgroundMRI is useful in SRS treatment planning, especially for patients with brain lesions or anatomical targets that are poorly distinguished by CT, but its use requires further refinement. This methodology seeks to optimize MRI sequences to generate distortion-free and clinically relevant MR images for MRI-only SRS treatment planning.Materials and methodsWe used commercially available SRS MRI-guided radiotherapy phantoms and eight patients to optimize sequences for patient imaging. Workflow involved the choice of correct MRI sequence(s), optimization of the sequence parameters, evaluation of image quality (artifact free and clinically relevant), measurement of geometrical distortion, and evaluation of the accuracy of our offline correction algorithm.ResultsCT images showed a maximum deviation of 1.3 mm and minimum deviation of 0.4 mm from true fiducial position for SRS coordinate definition. Interestingly, uncorrected MR images showed maximum deviation of 1.2 mm and minimum of 0.4 mm, comparable to CT images used for SRS coordinate definition. After geometrical correction, we observed a maximum deviation of 1.1 mm and minimum deviation of only 0.3 mm.ConclusionOur optimized MRI pulse sequences and image correction technique show promising results; MR images produced under these conditions are appropriate for direct use in SRS treatment planning.     

Evaluating the usefulness of the direct density reconstruction algorithm for intensity modulated and passively scattered proton therapy: Validation using an anthropomorphic phantom

PurposeAccurate calculation of the proton beam range inside a patient is an important topic in proton therapy. In recent times, a computed tomography (CT) image reconstruction algorithm was developed for treatment planning to reduce the impact of the variation of the CT number with changes in imaging conditions. In this study, we investigated the usefulness of this new reconstruction algorithm (DirectDensity™: DD) in proton therapy based on its comparison with filtered back projection (FBP).MethodsWe evaluated the effects of variations in the X-ray tube potential and target size on the FBP- and DD-image values and investigated the usefulness of the DD algorithm based on the range variations and dosimetric quantity variations.ResultsFor X-ray tube potential variations, the range variation in the case of FBP was up to 12.5 mm (20.8%), whereas that of DD was up to 3.3 mm (5.6%). Meanwhile, for target size variations, the range variation in the case of FBP was up to 2.2 mm (2.5%), whereas that of DD was up to 0.9 mm (1.4%). Moreover, the variations observed in the case of DD were smaller than those of FBP for all dosimetric quantities.ConclusionThe dose distributions obtained using DD were more robust against variations in the CT imaging conditions (X-ray tube potential and target size) than those obtained using FBP, and the range variations were often less than the dose calculation grid (2 mm). Therefore, the DD algorithm is effective in a robust workflow and reduces uncertainty in range calculations.     

Automatic registration of MRI-based joint models to high-speed biplanar radiographs for precise quantification of in vivo anterior cruciate ligament deformation during gait

Understanding in vivo joint mechanics during dynamic activity is crucial for revealing mechanisms of injury and disease development. To this end, laboratories have utilized computed tomography (CT) to create 3-dimensional (3D) models of bone, which are then registered to high-speed biplanar radiographic data captured during movement in order to measure in vivo joint kinematics. In the present study, we describe a system for measuring dynamic joint mechanics using 3D surface models of the joint created from magnetic resonance imaging (MRI) registered to high-speed biplanar radiographs using a novel automatic registration algorithm. The use of MRI allows for modeling of both bony and soft tissue structures. Specifically, the attachment site footprints of the anterior cruciate ligament (ACL) on the femur and tibia can be modeled, allowing for measurement of dynamic ACL deformation. In the present study, we demonstrate the precision of this system by tracking the motion of a cadaveric porcine knee joint. We then utilize this system to quantify in vivo ACL deformation during gait in four healthy volunteers.     

Pin-photodiode array for the measurement of fan-beam energy and air kerma distributions of X-ray CT scanners

PurposePatient dose estimation in X-ray computed tomography (CT) is generally performed by Monte Carlo simulation of photon interactions within anthropomorphic or cylindrical phantoms. An accurate Monte Carlo simulation requires an understanding of the effects of the bow-tie filter equipped in a CT scanner, i.e. the change of X-ray energy and air kerma along the fan-beam arc of the CT scanner. To measure the effective energy and air kerma distributions, we devised a pin-photodiode array utilizing eight channels of X-ray sensors arranged at regular intervals along the fan-beam arc of the CT scanner.MethodsEach X-ray sensor consisted of two plate type of pin silicon photodiodes in tandem – front and rear photodiodes – and of a lead collimator, which only allowed X-rays to impinge vertically to the silicon surface of the photodiodes. The effective energy of the X-rays was calculated from the ratio of the output voltages of the photodiodes and the dose was calculated from the output voltage of the front photodiode using the energy and dose calibration curves respectively.ResultsThe pin-photodiode array allowed the calculation of X-ray effective energies and relative doses, at eight points simultaneously along the fan-beam arc of a CT scanner during a single rotation of the scanner.ConclusionsThe fan-beam energy and air kerma distributions of CT scanners can be effectively measured using this pin-photodiode array.     

隐匿性胫骨平台骨折MRI、CT检查的影像学表现及其诊断价值对比的回顾性研究

目的:研究对比隐匿性胫骨平台骨折(TPOF)磁共振成像(MRI)、电子计算机断层扫描(CT)检查的影像学表现及其诊断价值。方法:回顾性分析我院自2016年1月至2019年12月拟诊断为TPOF且X线检查表现为阴性的89例患者的临床资料,分别对所有受试者进行MRI、CT检查,且以手术检查为金标准,比较上述两种影像学检查手段诊断TPOF的效能。此外,比较MRI、CT检查诊断TPOF的表观扩散系数以及节段各向异性值以及对TPOF类型的检出率。结果:MRI检查诊断TPOF的灵敏度、特异度及准确度分别为98.61%、94.12%、97.75%,均高于CT检查的79.17%、64.71%、76.40%(均P<0.05)。MRI检查诊断TPOF的表观扩散系数高于CT检查,而节段各向异性值低于CT检查(均P<0.05)。MRI检查对骨皮质骨折的检出率低于CT检查,而对骨小梁骨折的检出率高于CT检查(均P<0.05)。结论:MRI检查诊断TPOF的价值高于CT检查,且在骨小梁骨折的检出率方面优于CT检查,但CT检查应用于骨皮质骨折的诊断价值更高。临床工作中可能通过联合MRI以及CT检查,继而达到提高TPOF检出率的目的。