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.     

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.     

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.     

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.     

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.     

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 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.     

Enhancement of Hounsfield unit distribution in cone-beam CT images for adaptive radiation therapy: Evaluation of a hybrid correction approach

PurposeThis study aims to evaluate the accuracy of a hybrid approach combining the histogram matching (HM) and the multilevel threshold (MLT) to correct the Hounsfield Unit (HU) distribution in cone-beam CT (CBCT) images.Methods and MaterialsCBCT images acquired for ten prostate cancer patients were processed by matching their histograms to those of deformed planning CT (pCT) images obtained after applying a deformable registration (DR) process. Then, HU values corresponding to five tissue types in the pCT were assigned to the obtained CBCT images (CBCT_HM-MLT). Finally, the CBCT_HM-MLT images were compared to the deformed pCT visually and using different statistical metrics.ResultsThe visual assessment and the profiles comparison showed that the high discrepancies in the CBCT images were significantly reduced when using the proposed approach. Furthermore, the correlation values indicated that the CBCT_HM-MLT were in good agreement with the deformed pCT with correlation values ranging from 0.9893 to 0.9962. In addition, the root mean squared error (RMSE) over the entire volume was reduced from 64.15 ± 9.50 to 51.20 ± 6.76 HU. Similarly, the mean absolute error in specific tissue classes was significantly reduced especially in the soft tissue-air interfaces. These results confirmed that applying MLT after HM worked better than using only HM for which the correlation values were ranging from 0.9878 to 0.9955 and the RMSE was 55.95 ± 10.43 HU.ConclusionEvaluation of the proposed approach showed that the HM + MLT correction can improve the HU distribution in the CBCT images and generate corrected images in good agreement with the pCT.     

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.     

Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques

IntroductionMedical images are usually affected by biological and physical artifacts or noise, which reduces image quality and hence poses difficulties in visual analysis, interpretation and thus requires higher doses and increased radiographs repetition rate.ObjectivesThis study aims at assessing image quality during CT abdomen and brain examinations using filtering techniques as well as estimating the radiogenic risk associated with CT abdomen and brain examinations.Materials and MethodsThe data were collected from the Radiology Department at Royal Care International (RCI) Hospital, Khartoum, Sudan. The study included 100 abdominal CT images and 100 brain CT images selected from adult patients. Filters applied are namely: Mean filter, Gaussian filter, Median filter and Minimum filter. In this study, image quality after denoising is measured based on the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM).ResultsThe results show that the images quality parameters become higher after applications of filters. Median filter showed improved image quality as interpreted by the measured parameters: PSNR and SSIM, and it is thus considered as a better filter for removing the noise from all other applied filters.DiscussionThe noise removed by the different filters applied to the CT images resulted in enhancing high quality images thereby effectively revealing the important details of the images without increasing the patients’ risks from higher doses.ConclusionsFiltering and image reconstruction techniques not only reduce the dose and thus the radiation risks, but also enhances high quality imaging which allows better diagnosis.     

Combined PET/CT for IMRT treatment planning of NSCLC: Contrast-enhanced CT images for Monte Carlo dose calculation

PurposeCombined PET/CT imaging has been proposed as an integral part of radiotherapy treatment planning (TP). Contrast-enhanced CT (ceCT) images are frequently acquired as part of the PET/CT examination to support target delineation. The aim of this dosimetric planning study was to investigate the error introduced by using a ceCT for intensity modulated radiotherapy (IMRT) TP with Monte Carlo dose calculation for non-small cell lung cancer (NSCLC).Material and methodsNine patients with NSCLC prior to chemo-RT were included in this retrospective study. For each patient non-enhanced, low-dose CT (neCT), ceCT and [¹⁸F]-FDG-PET emission data were acquired within a single examination. Manual contouring and TP were performed on the ceCT. An additional set of independent target volumes was auto-segmented in PET images. Dose distributions were recalculated on the neCT. Differences in dosimetric parameters were evaluated.ResultsDose differences in PTV and lungs were small for all patients. The maximum difference in all PTVs when using ceCT images for dose calculation was ?2.1%, whereas the mean difference was less than ?1.7%. Maximum differences in the lungs ranged from ?1.8% to 2.1% (mean: ?0.1%). In four patients an underestimation of the maximum spinal cord dose between 2% and 3.2% was observed, but treatment plans remained clinically acceptable.ConclusionsMonte Carlo based IMRT planning for NSCLC patients using ceCT allows for correct dose calculation. A direct comparison to neCT-based treatment plans revealed only small dose differences. Therefore, ceCT-based TP is clinically safe as long as the maximum acceptable dose to organs at risk is not approached.     

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.     

Convolutional neural network-based automatic liver delineation on contrast-enhanced and non-contrast-enhanced CT images for radiotherapy planning

AimThis study evaluated a convolutional neural network (CNN) for automatically delineating the liver on contrast-enhanced or non-contrast-enhanced CT, making comparisons with a commercial automated technique (MIM Maestro®).BackgroundIntensity-modulated radiation therapy requires careful labor-intensive planning involving delineation of the target and organs on CT or MR images to ensure delivery of the effective dose to the target while avoiding organs at risk.Materials and MethodsContrast-enhanced planning CT images from 101 pancreatic cancer cases and accompanying mask images showing manually-delineated liver contours were used to train the CNN to segment the liver. The trained CNN then performed liver segmentation on a further 20 contrast-enhanced and 15 non-contrastenhanced CT image sets, producing three-dimensional mask images of the liver.ResultsFor both contrast-enhanced and non-contrast-enhanced images, the mean Dice similarity coefficients between CNN segmentations and ground-truth manual segmentations were significantly higher than those between ground-truth and MIM Maestro software (p < 0.001). Although mean CT values of the liver were higher on contrast-enhanced than on non-contrast-enhanced CT, there were no significant differences in the Hausdorff distances of the CNN segmentations, indicating that the CNN could successfully segment the liver on both image types, despite being trained only on contrast-enhanced images.ConclusionsOur results suggest that a CNN can perform highly accurate automated delineation of the liver on CT images, irrespective of whether the CT images are contrast-enhanced or not.     

Generation of clinical 177Lu SPECT/CT images based on Monte Carlo simulation with GATE

PurposePatient-specific dosimetry in MRT relies on quantitative imaging, pharmacokinetic assessment and absorbed dose calculation. The DosiTest project was initiated to evaluate the uncertainties associated with each step of the clinical dosimetry workflow through a virtual multicentric clinical trial. This work presents the generation of simulated clinical SPECT datasets based on GATE Monte Carlo modelling with its corresponding experimental CT image, which can subsequently be processed by commercial image workstations.MethodsThis study considers a therapy cycle of 6.85 GBq ¹⁷⁷Lu-labelled DOTATATE derived from an IAEA-Coordinated Research Project (E23005) on “Dosimetry in Radiopharmaceutical therapy for personalised patient treatment”. Patient images were acquired on a GE Infinia-Hawkeye 4 gamma camera using a medium energy (ME) collimator. Simulated SPECT projections were generated based on experimental time points and validated against experimental SPECT projections using flattened profiles and gamma index. The simulated projections were then incorporated into the patient SPECT/CT DICOM envelopes for processing and their reconstruction within a commercial image workstation.ResultsGamma index passing rate (2% − 1 pixel criteria) between 95 and 98% and average gamma between 0.28 and 0.35 among different time points revealed high similarity between simulated and experimental images. Image reconstruction of the simulated projections was successful on HERMES and Xeleris workstations, a major step forward for the initiation of a multicentric virtual clinical dosimetry trial based on simulated SPECT/CT images.ConclusionsRealistic ¹⁷⁷Lu patient SPECT projections were generated in GATE. These modelled datasets will be circulated to different clinical departments to perform dosimetry in order to assess the uncertainties in the entire dosimetric chain.     

A role for magnetic susceptibility in synthetic computed tomography

PurposeRadiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping.MethodsVolumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans.ResultsThe skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU.ConclusionSusceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.     

Ages of giant panda can be accurately predicted using facial images and machine learning

To forecast giant panda (Ailuropoda melanoleuca) population dynamics in the wild, it is crucial to comprehend their age distribution. Traditional methods for estimating the age of panda are costly, time-consuming, and inaccurate. Additionally, these methods only forecast an age group rather than a real age, and lack a uniform standard. However, advances in deep learning and computer vision have given rise to fresh approaches to this problem. Classification models can be improved by using ordinal regression, which uses ordinal correlations across ages to reduce the non-stationary nature of aging tasks. In this study, we collected 8002 images from 272 pandas in various environments, whose ages ranged from 0 to 38. We applied a five-fold subject-exclusive (SE) protocol to train seven Convolutional Neural Networks (CNN) based on ordinal regression. Experiments were conducted on the Panda Age Dataset (PAD Full) and the Lite Panda Age Dataset (PAD Lite). The results were very encouraging and achieved a Mean Absolute Error (MAE) of 2.51 and 2.41, respectively. Our findings demonstrate that this new tool can noninvasively predict the age of giant pandas in captivity and the wild. Continued development of computer vision technology will drive progress in ecology and conservation.     

Machine log file-based dose verification using novel iterative CBCT reconstruction algorithm in commercial software during volumetric modulated arc therapy for prostate cancer patients

PurposeTo evaluate the utility of the use of iterative cone-beam computed tomography (CBCT) for machine log file-based dose verification during volumetric modulated arc therapy (VMAT) for prostate cancer patients.MethodsAll CBCT acquisition data were used to reconstruct images with the Feldkamp-Davis-Kress algorithm (FDK-CBCT) and the novel iterative algorithm (iCBCT). The Hounsfield unit (HU)-electron density curves for CBCT images were created using the Advanced Electron Density Phantom. The I’mRT and anthropomorphic phantoms were irradiated with VMAT after CBCT registration. Subsequently, fourteen prostate cancer patients received VMAT after CBCT registration. Machine log files and both CBCT images were exported to the PerFRACTION software, and a 3D patient dose was reconstructed. Mean dose for planning target volume (PTV), the bladder, and rectum and the 3D gamma analysis were evaluated.ResultsFor the phantom studies, the variation of HU values was observed at the central position surrounding the bones in FDK-CBCT. There were almost no changes in the difference of doses at the isocenter between measurement and reconstructed dose for planning CT (pCT), FDK-CBCT, and iCBCT. Mean dose differences of PTV, rectum, and bladder between iCBCT and pCT were approximately 2% lower than those between FDK-CBCT and pCT. For the clinical study, average gamma analysis for 2%/2 mm was 98.22% ± 1.07 and 98.81% ± 1.25% in FDK-CBCT and iCBCT, respectively.ConclusionsA similar machine log file-based dose verification accuracy is obtained for FDK-CBCT and iCBCT during VMAT for prostate cancer patients.     

Zero echo time MRI-only treatment planning for radiation therapy of brain tumors after resection

Using magnetic resonance imaging (MRI) as the sole imaging modality for patient modeling in radiation therapy (RT) is a challenging task due to the need to derive electron density information from MRI and construct a so-called pseudo-computed tomography (pCT) image. We have previously published a new method to derive pCT images from head T1-weighted (T1-w) MR images using a single-atlas propagation scheme followed by a post hoc correction of the mapped CT numbers using local intensity information. The purpose of this study was to investigate the performance of our method with head zero echo time (ZTE) MR images. To evaluate results, the mean absolute error in bins of 20 HU was calculated with respect to the true planning CT scan of the patient. We demonstrated that applying our method using ZTE MR images instead of T1-w improved the correctness of the pCT in case of bone resection surgery prior to RT (that is, an example of large anatomical difference between the atlas and the patient).     

Spatial resolution compensation by adjusting the reconstruction kernels for iterative reconstruction images of computed tomography

PurposeHybrid iterative reconstruction (IR) is useful to reduce noise in computed tomography (CT) images. However, it often decreases the spatial resolution. The ability of high spatial resolution kernels (harder kernels) to compensate for the decrease in the spatial resolution of hybrid IRs was investigated.MethodsAn elliptic cylindrical phantom simulating an adult abdomen was used. Two types of rod-shaped objects with ~330 and ~130 HU were inserted to simulate contrasts of arteries in CT angiography. Two multi-slice CT systems were used to scan the phantoms with 120 kVp and scan doses of 20 and 10 mGy. The task transfer functions (TTFs) were measured from the circular edges of the rod images. The noise power spectrum (NPS) was measured from the images of the water-only section. The CT images were reconstructed using a filtered back projection (FBP) with baseline kernels and two levels of hybrid IRs with harder kernels. The profiles of the clinical images across the aortic dissection flaps were measured to evaluate actual spatial resolutions.ResultsThe TTF degradation of each hybrid IR was recovered by the harder kernels, whereas the noise reduction effect was retained, for both the 20 and 10 mGy. The profiles of the dissection flaps for the FBP were maintained by using the harder kernels. Even with the best combination of hybrid IR and harder kernel, the noise level at 10 mGy was not reduced to the level of FBP at 20 mGy, suggesting no capability of a 50% dose reduction while maintaining noise.