MIM Maestro Research Articles

Dosimetric comparison of ZAP-X, Gamma Knife, and CyberKnife stereotactic radiosurgery for single brain metastasis

PurposeTo evaluate the dosimetric characteristics of ZAP-X stereotactic radiosurgery (SRS) for single brain metastasis by comparing with two mature SRS platforms.MethodsThirteen patients with single brain metastasis treated with CyberKnife (CK) G4 were selected retrospectively. The prescription dose for the planning target volume (PTV) was 18–24 Gy for 1–3 fractions. The PTV volume ranged from 0.44 to 11.52 cc.Treatment plans of thirteen patients were replanned using the ZAP-X plan system and the Gamma Knife (GK) ICON plan system with the same prescription dose and organs at risk (OARs) constraints. The prescription dose of PTV was normalized to 70% for both ZAP-X and CK, while it was 50% for GK. The dosimetric parameters of three groups included the plan characteristics (CI, GI, GSI, beams, MUs, treatment time), PTV (D2, D95, D98, Dmin, Dmean, Coverage), brain tissue (volume of 100%-10% prescription dose irradiation V100%-V10%, Dmean) and other OARs (Dmax, Dmean),all of these were compared and evaluated. All data were read and analyzed with MIM Maestro. One-way ANOVA or a multisample Friedman rank sum test was performed, where p < 0.05 indicated significant differences.ResultsThe CI of GK was significantly lower than that of ZAP-X and CK. Regarding the mean value, ZAP-X had a lower GI and higher GSI, but there was no significant difference among the three groups. The MUs of ZAP-X were significantly lower than those of CK, and the mean value of the treatment time of ZAP-X was significantly shorter than that of CK. For PTV, the D95, D98, and target coverage of CK were higher, while the mean of Dmin of GK was significantly lower than that of CK and ZAP-X. For brain tissue, ZAP-X showed a smaller volume from V100% to V20%; the statistical results of V60% and V50% showed a difference between ZAP-X and GK, while the V40% and V30% showed a significant difference between ZAP-X and the other two groups; V10% and Dmean indicated that GK was better. Excluding the Dmax of the brainstem, right optic nerve and optic chiasm, the mean value of all other OARs was less than 1 Gy. For the brainstem, GK and ZAP-X had better protection, especially at the maximum dose.ConclusionFor the SRS treating single brain metastasis, all three treatment devices, ZAP-X system, CyberKnife G4 system, and GammaKnife system, could meet clinical treatment requirements. The newly platform ZAP-X could provide a high-quality plan equivalent to or even better than CyberKnife and Gamma Knife, with ZAP-X presenting a certain dose advantage, especially with a more conformal dose distribution and better protection for brain tissue. As the ZAP-X systems get continuous improvements and upgrades, they may become a new SRS platform for the treatment of brain metastasis.

Mapping the Anatomical Distribution of MRI-Identified Locoregional Recurrence following Robotic-Assisted Laparoscopic Prostatectomy for Prostate Adenocarcinoma

The pattern of locoregional recurrence specifically after robotic-assisted laparoscopic prostatectomy (RALP) for prostate adenocarcinoma is still unknown. In this study, we reviewed pelvic magnetic resonance images (MRI) after postoperative biochemical recurrence (BCR) and drew a map of locoregional recurrence to support evidence of determining the optimal target volume of salvage radiotherapy in a post-RALP BCR scenario. We have retrospectively searched 7,583 prostate adenocarcinoma patients who have received RALP in our institution between January 2010 and December 2021, and found a pool of highly selected patients with gross tumor recurrence confirmed by post-BCR pelvic MRIs and did not have other histories of malignancy. BCR was defined as the serum PSA more or equal to 0.2 ng/mL, or three consecutive increases. Patients with serum PSA nadir more or equal to 0.2 ng/mL on the 90th postoperative day (POD 90) were excluded to guarantee successful tumor removal. We have reviewed serum PSA levels using R codes, MRI and pathological reports using Excel, and descriptive statistics using SPSS 25. The gross lesions were contoured on the correlating MRIs using MIM Maestro 7.1. The RT structure DICOM files were merged into a map using MATLAB 2022b. In addition, we have conducted Fisher exacted test, Mann-Whitney U test, and logistic regression to identify risk factors for regional recurrence. A total of 173 patients were identified with locoregional recurrence from post-BCR pelvic MRIs, and 139 (80.3%) patients were in the high-risk group or very-high-risk group according to the NCCN guidelines: 57 (32.9%) patients with histological grade group 5, 50 (28.9%) patients with initial PSA over 20 ng/mL, 114 (65.9%) patients with extracapsular extension, 55 (31.8%) patients with seminal vesicle invasion, and 15 (9%) patients with pN1. The median follow-up was 4.7 (IQR 2.8-6.9) years for pelvic MRIs and 5.8 (IQR 4.0-8.6) years for serum PSA. The BCF survival was median of 10.7 (IQR 4.6-19.1) months, and the locoregional recurrence-free survival was median of 24.6 (IQR 9.7-49.4) months for this subgroup of patients. At first locoregional recurrence, 148 (85.5%) patients were local only, 20 (11.6%) patients were regional only, and 5 (2.9%) patients were both local and regional. Out of the 25 patients with regional recurrence, the incidence of gross tumor recurrence differed by nodal sites: 3 (12%) in perirectal space, 5 (20%) in internal iliac, 7 (28%) in obturator, 13 (52%) in external iliac, and 6 (24%) in common iliac lymph nodes. We have found 173 patients and were able to map reliable gross tumor recurrence sites after RALP and confirmed by pelvic MRIs following BCR. The map supports evidence of using the existing consensus pelvic clinical target volume of salvage radiotherapy, in a post-RALP BCR scenario.

Optimized Atlas-Based Auto-Segmentation of Bony Structures from Whole-Body Computed Tomography

To develop and test a method for fully automated segmentation of bony structures from whole-body CT and evaluate its performance compared to manual segmentation METHODS AND MATERIALS;: We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation method with a post-processing module (ABSPP) in MIM MAESTRO software. 52 CT scans comprised the training set to build the atlas library and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement (MDA), and relative volume errors (ΔV) between ABSPP and atlas-based segmentation with no post-processing (ABSNPP), with manual segmentation as the reference (gold standard). RESULTS;: The ABSPP method resulted in significantly improved segmentation accuracy (DSC range: 0.85 to 0.98) compared to the ABSNPP method (DSC range: 0.55 to 0.87) (p<0.001). MDA results also indicated high agreement between ABSPP and manual reference delineations (range: 0.11-1.56 mm), which was significantly improved compared to ABSNPP (range: 1.00-2.34 mm) for for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared to ABSNPP for most bony structures. We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared to manual delineation. The integrated post-processing module significantly improved workflow performance.

Impact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom.

The purpose of this study was to evaluate the deformable image registration (DIR) accuracy using various CT scan parameters with deformable thorax phantom. Our developed deformable thorax phantom (Dephan, Chiyoda Technol Corp, Tokyo, Japan) was used. The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180mm, which simulates the chest wall. The inner phantom consists of deformable, 20mm thick disk-shaped sponges with 48 Lucite beads and 48 nylon cross-wires which simulate the vascular and bronchial bifurcations of the lung. Peak-exhale and peak-inhale images of the deformable phantom were acquired using a CT scanner (Aquilion LB, TOSHIBA). To evaluate the impact of CT scan parameters on DIR accuracy, we used the four tube voltages (80, 100, 120, and 135kV) and six reconstruction algorithms (FC11, FC13, FC15, FC41, FC44, and FC52). Intensity-based DIR was performed between the two images using MIM Maestro (MIM software, Cleveland, USA). Fiducial markers (beads and cross-wires) based target registration error (TRE) was used for quantitative evaluation of DIR. In case with different tube voltages, the range of average TRE were 4.44-5.69mm (reconstruction algorithm: FC13). In case with different reconstruction algorithms, the range of average TRE were 4.26-4.59mm (tube voltage: 120kV). The TRE were differed by up to 3.0mm (3.96-6.96mm) depending on the combination of tube voltage and reconstruction algorithm. Our result indicated that CT scan parameters had moderate impact of TRE, especially for reconstruction algorithms for the deformable thorax phantom.

Dosimetric impacts of cone-beam computed tomography (CBCT)-based anatomic changes in intensity-modulated radiotherapy for cervical cancer.

To evaluate the effects of dose to tumors and organs at risk (OARs) on inter-fractional anatomic changes. We evaluated nine patients with cervical cancer treated with intensity-modulated radiotherapy (IMRT) (45 Gy in 25 fractions) using kV cone-beam computed tomography (CBCT) image guidance once or twice a week before treatment. For each patient, the original plan on the computed tomography (CT) image was copied to merged images, and then the fractional doses were calculated. Subsequently, deformable accumulated doses were obtained by summing the fractional absolute doses into a single dose in MIM Maestro software. The volume changes in the target and OARs were compared between the original CT and merged CBCT images, and the differences in the fractional and accumulated doses were also evaluated. Sixty-nine merged CBCT images were obtained and analyzed in this study. For the target areas, the volume changes in the clinical target volume (CTV) and planning target volume (PTV) reached -18.05% and -24.11% at most, respectively. The fractional D2% of the CTV and PTV was generally higher than the original plans, and the accumulated deviations were 2.27%±0.82% (P<0.01) and 2.42%±1.28% (P<0.01), respectively. The fractional D98% of the PTV was underdosed up to 18.28% for 78% of patients, and the accumulated deviations were -2.06% to -17.29% (P<0.05). For the OARs, the bladder volume changes were the most dramatic, reducing up to 93.60%. The fractional Dmean and D2cc of the bladder were generally higher than the original plans, and there were significant differences in their accumulated values (P<0.05). There was no obvious trend of rectal volume change with -69.65% to 74.20%. The rectum Dmean and D2cc of the accumulated were not significantly different from the planned dose (P>0.05). For patients with cervical cancer, the changes in bladder and rectal volume were greater than in the target volume. Although the volume changes in the bladder and rectum had no significant effect on D98% of the CTV and PTV, they had a significant effect on their own D2cc and the D2% of the CTV and PTV. More attention should be paid to the volume changes in the bladder and rectum in clinical work.

Evaluation of dose accumulation methods and workflows utilising cone beam computed tomography images.

Various adaptive radiation therapy (ART) methods have emerged, with little consensus amongst the literature as to which is most appropriate. This study aimed to compare dose mapping (DM) versus Monte Carlo recalculation (MCR), using cone beam computed tomography (CBCT) images when utilised in automated ART dose accumulation workflows in the MIM Maestro software package. The treatment plans for 38 cancer patients (19 prostate and 19 head and neck cases) were used to perform DM or MCR retrospectively upon CBCTs acquired during treatment, which were then deformably registered to the planning CT (DR-pCT) to facilitate dose accumulation. Dose-volume and region-of-interest data were extracted for the planning target volumes and organs at risk. Intraclass correlation (ICC) values and Bland-Altman plots were utilised to compare DM versus MCR doses on the CBCT images as well as CBCT versus DR-pCT doses. When comparing DM and MCR on CBCTs, the differences across dose level mean dose differences were mostly within a ±5% level of agreement based on the Bland-Altman plots, with over 67% of ICC values over 0.9 and indicative of good correlation. When these distributions were deformed back to the planning CT, the agreement was reduced considerably, with larger differences (exceeding ±5%) resulting from workflow-related issues. The results emphasise the need to consider and make adaptations to minimise the effect of workflows on algorithm performance. Manual user intervention, refined departmental protocols and further developments to the MIM Maestro software will enhance the use of this tool.

Evaluation Exploration of Atlas-Based and Deep Learning-Based Automatic Contouring for Nasopharyngeal Carcinoma.

PurposeThe purpose of this study was to evaluate and explore the difference between an atlas-based and deep learning (DL)-based auto-segmentation scheme for organs at risk (OARs) of nasopharyngeal carcinoma cases to provide valuable help for clinical practice.Methods120 nasopharyngeal carcinoma cases were established in the MIM Maestro (atlas) database and trained by a DL-based model (AccuContour®), and another 20 nasopharyngeal carcinoma cases were randomly selected outside the atlas database. The experienced physicians contoured 14 OARs from 20 patients based on the published consensus guidelines, and these were defined as the reference volumes (Vref). Meanwhile, these OARs were auto-contoured using an atlas-based model, a pre-built DL-based model, and an on-site trained DL-based model. These volumes were named Vatlas, VDL-pre-built, and VDL-trained, respectively. The similarities between Vatlas, VDL-pre-built, VDL-trained, and Vref were assessed using the Dice similarity coefficient (DSC), Jaccard coefficient (JAC), maximum Hausdorff distance (HDmax), and deviation of centroid (DC) methods. A one-way ANOVA test was carried out to show the differences (between each two of them).ResultsThe results of the three methods were almost similar for the brainstem and eyes. For inner ears and temporomandibular joints, the results of the pre-built DL-based model are the worst, as well as the results of atlas-based auto-segmentation for the lens. For the segmentation of optic nerves, the trained DL-based model shows the best performance (p < 0.05). For the contouring of the oral cavity, the DSC value of VDL-pre-built is the smallest, and VDL-trained is the most significant (p < 0.05). For the parotid glands, the DSC of Vatlas is the minimum (about 0.80 or so), and VDL-pre-built and VDL-trained are slightly larger (about 0.82 or so). In addition to the oral cavity, parotid glands, and the brainstem, the maximum Hausdorff distances of the other organs are below 0.5 cm using the trained DL-based segmentation model. The trained DL-based segmentation method behaves well in the contouring of all the organs that the maximum average deviation of the centroid is no more than 0.3 cm.ConclusionThe trained DL-based segmentation performs significantly better than atlas-based segmentation for nasopharyngeal carcinoma, especially for the OARs with small volumes. Although some delineation results still need further modification, auto-segmentation methods improve the work efficiency and provide a level of help for clinical work.

Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images.

PurposeThis retrospective work aims to evaluate the possible impact on intra‐ and inter‐observer variability, contouring time, and contour accuracy of introducing a pelvis computed tomography (CT) auto‐segmentation tool in radiotherapy planning workflow.MethodsTests were carried out on five structures (bladder, rectum, pelvic lymph‐nodes, and femoral heads) of six previously treated subjects, enrolling five radiation oncologists (ROs) to manually re‐contour and edit auto‐contours generated with a male pelvis CT atlas created with the commercial software MIM MAESTRO. The ROs first delineated manual contours (M). Then they modified the auto‐contours, producing automatic‐modified (AM) contours. The procedure was repeated to evaluate intra‐observer variability, producing M1, M2, AM1, and AM2 contour sets (each comprising 5 structures × 6 test patients × 5 ROs = 150 contours), for a total of 600 contours. Potential time savings was evaluated by comparing contouring and editing times. Structure contours were compared to a reference standard by means of Dice similarity coefficient (DSC) and mean distance to agreement (MDA), to assess intra‐ and inter‐observer variability. To exclude any automation bias, ROs evaluated both M and AM sets as “clinically acceptable” or “to be corrected” in a blind test.ResultsComparing AM to M sets, a significant reduction of both inter‐observer variability (p < 0.001) and contouring time (‐45% whole pelvis, p < 0.001) was obtained. Intra‐observer variability reduction was significant only for bladder and femoral heads (p < 0.001). The statistical test showed no significant bias.ConclusionOur atlas‐based workflow proved to be effective for clinical practice as it can improve contour reproducibility and generate time savings. Based on these findings, institutions are encouraged to implement their auto‐segmentation method.

Diagnostic Application and Systematic Evaluation of Image Registration Software in External Radiotherapy

Purpose: Analyze the clinical application of MIM maestro in cancer radiotherapy and evaluate the advantage of the software compare to the clinical applied tools. Materials and Methods: Potentially relevant studies published were identified through a pubmed and web of science search using words “MIM Maestro,” “Atlas,” “image registration,” “dose accumulation,” “irradiation.” Combinations of words were also searched as were bibliographies of downloaded papers in order to avoid missing relevant publications. Results: In many patients with cancer radiotherapy, multiple types of images are demanded, MIM Maestro is a multi-modality image information processing system for radiotherapy. Contour atlas and image registration among dose accumulation and individual fractions is beneficial for radiotherapy. Overall 34 papers were enrolled for analysis. The MIM appears to provide excellent clinical applications such as the function of contour altas, image fusion and registration, dose accumulation in radiotherapy compared to the other software. Conclusions: The regular optimization of radiotherapy technology and the development of image technology, improve the clinical efficiency. The current paper give a systematic review of MIM Maestro multi-modality image processing software.

Dosimetric Study of Adaptive Proton Pencil-Beam Scanning Treatment Plans Using CBCT-Based Synthetic CTs Generated From Commercial Image Information Systems

<h3>Purpose/Objective(s)</h3> Evaluate the accuracy of using CBCT-based synthetic CTs to trigger adaptive planning in pencil-beam scanning proton therapy. The CBCT-based synthetic CTs can decrease the number of necessary verification CT scans during treatment and trigger earlier adaptive planning for more effective treatment. <h3>Materials/Methods</h3> A retrospective study of 20 patients across 3 different sites: 10 head and neck, 5 breast, and 5 lung cases were studied. MIM Maestro 7.0.4 and Velocity 4.1 were used to generate synthetic CTs from CBCTs. The verification plans calculated on both the synthetic CT (synCT) and the verification CT (verCT) performed on the same day were compared in a treatment planning system. Dose metrics from each synCT plan generated are subtracted from the corresponding dose metrics from the same day verCT plan. Outliers with greater than 10% deviation on D95% were excluded from the analysis. <h3>Results</h3> The CTV D95% is less than 2% different between the synCT and verCT plans, and 75% of Velocity patients and 55% of MIM patients falls within 95% confidence levels when comparing target D95%. The average difference from verCT D95 was 1.6% for Velocity and -0.88% for MIM. Serial OAR Dmax in synCT plans average deviation from verCT was less than 1Gy on average. Parallel OAR volumetric and mean dose in the synCT plans were consistent with the verCT plans with deviations on average less than 1% by volume and 0.5Gy mean. Maximum doses have significant variations ranged from -7.7Gy to +4.4Gy for the target and -11.76Gy to +9.83Gy for OARs. The D2% of treatment volume in synCT plans showed was consistent with less than 1% deviation from the verCT plans. <h3>Conclusion</h3> Both systems can automatically generate synthetic CTs from CBCTs. The dosimetric similarities of synCT plans compared with verCT plans were higher for treatment sites with less heterogeneities such as breast patients when compared with head and neck patients. Outliers can happen when there are significant anatomy changes which result in inaccurate synthetic CTs due to inadequate image deformation or HU corrections. Users should pay extra attention to tissue discrepancies while evaluating the images. The Dmax for critical structures can vary greatly between synCT and verCT, so a small volumetric analysis is recommended. Both systems can be used to reduce verification CT scans while a patient is on treatment and can improve the adaptive plan workflow. Synthetic CT generation can be a more effective way to discover significant dosimetric differences caused by changes in patient anatomy, and therefore a more efficient path to adaptive treatment.

Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

BackgroundContour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.MethodsTwenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies.ResultsAmong patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations.ConclusionsIn terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.

In Vivo 3-D Dose Verification Using PET/CT Images After Carbon-Ion Radiation Therapy.

ObjectiveTo investigate the usefulness of positron emission tomography (PET) images obtained after carbon-ion irradiation for dose verification in carbon-ion radiotherapy.Methods and MaterialsAn anthropomorphic head phantom was used in this study. Three cubes with volumes of 1, 4, and 10 ml were contoured as targets in the phantom CT through a treatment planning system. Treatment plans with six prescriptions from 2.5 to 10 Gy (2.5, 3, 5, 6, 8, and 10 Gy effective dose) were designed and delivered by 90° fixed carbon-ion beams, respectively. After irradiation of the phantom, a PET/CT scan was performed to fuse the treatment-planning CT image with the PET/CT image. The relationship between target volume and the standard uptake value (SUV) in PET/CT was evaluated for corresponding plan prescription. The MIM Maestro software was used for the image fusion and data analysis.ResultsSUV in the target had an approximate linear relationship with the effective dose. The same effective dose could generate a roughly equal SUV for different target volumes (p < 0.05).ConclusionsIt is feasible to verify the actual 3-D dose distribution of carbon-ion radiotherapy by the approach in this study.

Methodological approach to create an atlas using a commercial auto-contouring software.

PurposeThe aim of this work was to establish a methodological approach for creation and optimization of an atlas for auto‐contouring, using the commercial software MIM MAESTRO (MIM Software Inc. Cleveland OH).MethodsA computed tomography (CT) male pelvis atlas was created and optimized to evaluate how different tools and options impact on the accuracy of automatic segmentation. Pelvic lymph nodes (PLN), rectum, bladder, and femurs of 55 subjects were reviewed for consistency by a senior consultant radiation oncologist with 15 yr of experience. Several atlas and workflow options were tuned to optimize the accuracy of auto‐contours. The deformable image registration (DIR), the finalization method, the k number of atlas best matching subjects, and several post‐processing options were studied. To test our atlas performances, automatic and reference manual contours of 20 test subjects were statistically compared based on dice similarity coefficient (DSC) and mean distance to agreement (MDA) indices. The effect of field of view (FOV) reduction on auto‐contouring time was also investigated.ResultsWith the optimized atlas and workflow, DSC and MDA median values of bladder, rectum, PLN, and femurs were 0.91 and 1.6 mm, 0.85 and 1.6 mm, 0.85 and 1.8 mm, and 0.96 and 0.5 mm, respectively. Auto‐contouring time was more than halved by strictly cropping the FOV of the subject to be contoured to the pelvic region.ConclusionA statistically significant improvement of auto‐contours accuracy was obtained using our atlas and optimized workflow instead of the MIM Software pelvic atlas.

The Dosimetric Feasibility of the 1.5T MR-Linac System for Esophageal Cancer: The Impact of the Magnetic Field

The MR-Linac (MRL) provides image guidance with high soft-tissue contrast. Esophageal cancer patients are a potential group to benefit from better guidance in the MRL. However, due to the presence of a transverse magnetic field (1.5T), electron return effect (ERE) may affect the target dose distribution, particularly at air-tissue interfaces. The purpose of the study was to investigate the suitability of treating esophageal cancer patients with the MRL and to characterize the dosimetric impact at air-tissue interfaces resulting primarily from the ERE. We prospectively enrolled 20 cases with ESCC into a cohort study which underwent re-planned using the research version of Monaco (V 5). All patients received 4DCT and three dimensional (3D) CT scans before Radiotherapy. Intensity modulated radiotherapy (IMRT) plans were designed on 3DCT images and 0% phase of the 4DCT images. In each case, we generated two types of plans: the original plan generated without a magnetic field; the optimized plan generated by a full optimization with a 1.5 T magnetic field. The prescribed dose was 60 Gy to the primary tumor (PTV60) and the esophageal wall contralateral (CE) to the tumor was contoured as an avoidance structure. All generated treatment plans were optimized for fulfilling their respective planning constraints. The dose distributions of the plans were imported into MIM Maestro and deformed to each phase to generate distributions for MIP and AIP phases. Most prominently, slight dose distortions at air-tissue-interfaces were observed in the presence of the magnetic field. Compared to non-magnetic field conditions, the dose of the magnetic field at air-tissue interfaces are slightly higher in some cases (1.8% of the tissue increased by 1.3 Gy on average). When considering breathe motion, 4DCT is more affected by magnetic field than 3DCT. The dose recalculation showed the presence of the 1.5 T magnetic field can slightly reduce PTV dose (D95). The low dose areas (V5, V10) of the lung sites are higher than the conventional Linac. The MRL results in considerable increases in Dmax on the esophageal wall in all cases. No significant differences between both plans were seen in terms of V20Gy of the lungs and V40Gy of the heart. The treatment of patients in the MRL using a conventional IMRT technique, at 1.5 T, implies an increase in esophageal wall and V5, V10 of the lung sites dose. The dosimetric impact of magnetic field at air-tissue boundaries is minor and does not negatively impact target conformity index. This opens possibilities for developing MR-guided esophageal cancer treatments in the MRL.

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

This 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®). Intensity-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. Contrast-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. For 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. Our 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.

Assessing cumulative dose distributions in combined external beam radiotherapy and intracavitary brachytherapy for cervical cancer by treatment planning based on deformable image registration.

BackgroundThis study aimed to validate the feasibility of deformable image registration (DIR) in assessing the cumulative dose distributions in combined external beam radiotherapy (EBRT) and intracavitary brachytherapy (ICBT) for cervical cancer.MethodsThis retrospective study included 23 patients with stage IIB disease treated with combined EBRT to the whole pelvis (50.4 Gy in 28 fractions) using an intensity-modulated radiotherapy technique with 6-MV X-ray, followed by three-dimensional (3D) ICBT (28 Gy in 4 fractions). Tumor gross target volume at diagnosis (GTV-Tinit), tumor gross target volume before brachytherapy, high-risk clinical target volume (HR-CTV), intermediate-risk clinical target volume (IR-CTV), and parametrium and organs at risk were recontoured on computed tomography images of EBRT and ICBT, respectively. The dose-volume parameters were also determined. The DIR results were reviewed using MIM Maestro (Reg Review) and modified by function (Reg Refine). To evaluate the accuracy of DIR, DIR-based cumulative dose-volume histogram (DVH) parameters and simple DVH parameter addition were compared using Wilcoxon rank-sum tests.ResultsThe cumulative dose distributions of EBRT and four ICBT sessions were successfully illustrated using DIR. The mean tumor diameters were 68.35 cm3 at diagnosis and 29.63 cm3 at ICBT initiation. The mean tumor regression was 56.6%. The median minimum dose covering 90% (D90) of HR-CTV, GTV-Tinit, IR-CTV, and parametrium were 69.58±4.94, 68.81±7.98, 59.28±3.78, and 60.97±1.1 Gyα/β=10, respectively, for DIR and 69.11±5.68, 68.49±8.62, 58.89±3.59, and 61±1.49 Gyα/β=10, respectively, with conventional simple DVH parameter addition.No statistically significant differences in dosimetric parameters were observed between the two methods.ConclusionsAlthough there were limitations in the DIR accuracy, DIR-based dose accumulation was significantly beneficial in visually showing the cumulative dose distribution in the target area to clinicians in combined radiotherapy for cervical cancer in routine clinical practice.

EVALUATING THE MIM MAESTRO SYSTEM'S CAPABILITIES IN THE PROCESS OF PREPARATION AND CONDUCTING REMOTE RADIOTHERAPY

The development of the mathematical model and software for deformable registration of tomographic images of various modalities made it possible to approach to solving a number of tasks such as automatic contouring, adaptive radiation therapy and evaluation of the patient's absorbed dose distributions. One of such the software systems implementing deformable registration is MIM and its module, focused on radiation therapy MIM Maestro. The mathematical basis of the MIM system is the deformable registration algorithms efficiency of which depended on the accuracy and speed of their operation. These parameters were estimated on the basis of deformable registration of CBCT data obtained on the TrueBeam linear accelerator in the process of implementing radiation therapy treatment plans, and initial CT data, on the basis of which the radiation treatment planning was carried out. A number of advantages of the MIM system (the ability to work in all major sections, predicting the shape of the contour) provide convenience and speed of the workflow, but do not allow abandoning the use of the Eclipse system due to its available possibilities related to features of radiation therapy (work with boluses, a library of therapeutic tables, appointment of material properties). Automatic atlas contouring based on deformable registration algorithms works at an acceptable speed, but requires manual editing of the result both when using the atlas supplied by the manufacturer with the MIM Maestro system and when using our own atlases created during the system testing. Thus, the MIM Maestro system, having a high potential of the deformable registration methods implemented in it, at the same time does not allow fully automating the process, but requires careful visual control and laborious manual correction of the result.

Evaluation of the effect of user-guided deformable image registration of thoracic images on registration accuracy among users

User-guided deformable image registration (DIR) has allowed users to actively participate in the DIR process and is expected to improve DIR accuracy. The purpose of this study was to evaluate the time required for and effect of user-guided DIR on registration accuracy for thoracic images among users. In this study, 4-dimensional computed tomographic images of 10 thoracic cancer patients were used. The dataset for these patients was provided by DIR-Lab (www.dir-lab.com) and included a coordinate list of anatomical landmarks (300 bronchial bifurcations). Four medical physicists from different institutions performed DIR between peak-inhale and peak-exhale images with/without the user-guided DIR tool, Reg Refine, implemented in MIM Maestro (MIM software, Cleveland, OH). DIR accuracy was quantified by using target registration errors (TREs) for 300 anatomical landmarks in each patient. The average TREs with user-guided DIR in the 10 images by the 4 medical physicists were 1.48, 1.80, 3.46, and 3.55 mm, respectively, whereas the TREs without user-guided DIR were 3.28, 3.45, 3.56, and 3.28 mm, respectively. The average times taken by the 4 physicists to use the user-guided DIR were 10.0, 6.7, 7.1, and 8.0 min, respectively. This study demonstrated that user-guided DIR can improve DIR accuracy and requires only a moderate amount of time (<10 min). However, 2 of the 4 users did not show much improvement in DIR accuracy, which indicated the necessity of training prior to use of user-guided DIR.

Improvement Prediction on Contour Deformation Accuracy Using Deformable Image Registration Results Compared to Rigid Image Registration Results

The aim of this study was to analyze improvement prediction on contour deformation accuracy using deformable image registration (DIR) results compared to rigid image registration (RIR) results. Radiotherapy plans for 31 cases (seven head and neck cases, 10 chest cases, six abdomen cases and eight female pelvis cases) from the privately open database for DIR were used. These cases used at least two radiotherapy plans, and registration was performed using two plans, not only for one case but also for different cases. The DIR and RIR were performed using the DIR software MIM Maestro (MIM software Inc., Cleveland, USA). The registration results for the following organs were analyzed: eye balls, optic nerves, brain stem, spinal cord and right and left parotid glands for head and neck; right and left lungs for chest; liver and right and left kidneys for abdomen; and rectum and bladder for pelvis. Dice similarity coefficient (DSC) for the organs was calculated from the results of RIR and DIR. The improvement in the DSC was observed. DIR improved the DSC values by more than 0.2 for simple shapes, well-defined boundaries and large volumes such as eye balls, brain stem, lungs and liver. The minimum DSC for these organs was approximately 0.7. The improvement in DSC for the organs eye balls, brain stem, lungs and liver had ceiling values 0.95, 0.90, 1.0 and 1.0, respectively. DSC for the spinal cord, parotid gland, bladder and kidney also improved by DIR compared to RIR; however, DIR could not improve the DSC value for rectum compared to RIR because of a large difference in the position, shape and size due to stool and gas.

Comparison of the dose on specific 3DCT images and the accumulated dose for cardiac structures in esophageal tumors radiotherapy: whether specific 3DCT images can be used for dose assessment?

BackgroundCardiac activity could impact the accuracy of dose assessment for the heart, pericardium and left ventricular myocardium (LVM). The purpose of this study was to explore whether it is possible to perform dose assessment by contouring the cardiac structures on specific three-dimensional computed tomography (3DCT) images to reduce the impact of cardiac activity.MethodsElectrocardiograph-gated 4DCT (ECG-gated 4DCT) images of 22 patients in breath-hold were collected. MIM Maestro 6.8.2 (MIM) was used to reconstruct specific 3DCT images to obtain the Maximal intensity projection (MIP) image, Average intensity projection (AIP) image and Minimum intensity projection (Min-IP) image. The heart, pericardium and LVM were contoured in 20 phases of 4DCT images (0, 5%... 95%) and the MIP, AIP and Min-IP images. Then, a radiotherapy plan was designed at the 0% phase of the 4DCT images, and the dose was transplanted to all phases of 4DCT to acquire the dose on all phases, the accumulated dose of all phases was calculated using MIM. The dose on MIP, AIP and Min-IP images were also obtained by deformable registration of the dose. The mean dose (Dmean), V5, V10, V20, V30 and V40 for the heart, pericardium and LVM in MIP, AIP and Min-IP images were compared with the corresponding parameters after dose accumulation.ResultsThe mean values of the difference between the Dmean in the MIP image and the Dmean after accumulation for the heart, pericardium and LVM were all less than 1.50 Gy, and the dose difference for the pericardium and LVM was not statistically significant (p > 0.05). For dose-volume parameters, there was no statistically significant difference between V5, V10, and V20 of the heart and pericardium in MIP, AIP, and Min-IP images and those after accumulation (p > 0.05). For the LVM, only in the MIP image, the differences of V5, V10, V20, V30 and V40 were not significant compared to those after dose accumulation (p > 0.05).ConclusionsThere was a smallest difference for the dosimetry parameters of cardiac structures on MIP image compared to corresponding parameters after dose accumulation. Therefore, it is recommended to use the MIP image for the delineation and dose assessment of cardiac structures in clinical practice.