Definition of the margin of major coronary artery bifurcations during radiotherapy with electrocardiograph-gated 4D-CT

Effect of Calcification Based on Computer-Aided System on CT-Fractional Flow Reserve in Diagnosis of Coronary Artery Lesion

This study was to analyze the diagnostic value of coronary computed tomography angiography (CCTA) and fractional flow reserve (FFR) based on computer-aided diagnosis (CAD) system for coronary lesions and the possible impact of calcification. 80 patients who underwent CCTA and FFR examination in hospital were selected as the subjects. The FFR value of 0.8 was used as the dividing line and divided into the ischemic group (FFR ≤ 0.8) and nonischemic group (FFR > 0.8). The basic data and imaging characteristics of patients were analyzed. The maximum diameter stenosis rate (MDS %), maximum area stenosis rate (MAS %), and napkin ring sign (NRS) in the ischemic group were significantly lower than those in the nonischemic group (P < 0.05). Remodeling index (RI) and eccentric index (EI) compared with the nonischemic group had no significant difference (P > 0.05). The total plaque volume (TPV), total plaque burden (TPB), calcified plaque volume (CPV), lipid plaque volume (LPV), and lipid plaque burden (LPB) in the ischemic group were significantly different from those in the non-ischemic group (P < 0.05). MAS % had the largest area under curve (AUC) for the diagnosis of coronary myocardial ischemia (0.74), followed by MDS % (0.69) and LPV (0.68). CT-FFR had high diagnostic sensitivity, specificity, accuracy, truncation value, and AUC area data for patients in the ischemic group and nonischemic group. The diagnostic sensitivity, specificity, accuracy, cutoff value, and AUC area data of CT-FFR were higher in the ischemic group (89.93%, 92.07%, 95.84%, 60.51%, 0.932) and nonischemic group (93.75%, 90.88%, 96.24%, 58.22%, 0.944), but there were no significant differences between the two groups (P > 0.05). In summary, CT-FFR based on CAD system has high accuracy in evaluating myocardial ischemia caused by coronary artery stenosis, and within a certain range of calcification scores, calcification does not affect the diagnostic accuracy of CT-FFR.

Study of Spinal Cord Substructure Expansion Margin in Esophageal Cancer

Purpose:

To analyze the setup errors and residual errors of different spinal cord parts in esophageal cancer patients and to explore the necessity of spinal cord segmental expansion.

Methods and Materials:

Sixty cases of esophageal cancer were included with 20 patients subdivided into the following groups: neck, chest and abdomen as per the treatment site. The patients underwent intensity modulated radiation therapy (IMRT) between 2017 and 2019. Thermoplastic mask or vacuum bag were utilized for immobilization of different groups. CTVision (Siemens CT-On-Rail system) was used to acquire pre-treatment CT, and 20 consecutive pre-treatment CT datasets were collected for data analysis for each case. Images were exported to MIM (MIM Software Inc.) for processing and data analysis. Dice coefficient, maximum Hausdorff distance and centroid coordinate values between the spinal cord contours in the pre-treatment CTs and the planning CT were calculated and extracted. The contour expansion margin value is calculated as M_PRV = 1.3 ∑ _total + 0.5 σ _total, where ∑ _total and σ _total are the systematic and random error, respectively.

Results:

For neck, chest, abdominal segments of the spinal cord, the mean Dice coefficients (± SD) are 0.73 ± 0.06, 0.80 ± 0.06, 0.82 ± 0.06, the maximum Hausdorff distance residual error (± SD) are 4.46 ± 0.55, 3.49 ± 0.53, 3.46 ± 0.69 mm, and the mean centroid coordinate residual error (± SD) are 2.40 ± 0.53, 1.66 ± 0.47, 2.14 ± 0.95 mm, respectively. The calculated margin using residual centroid method in medial-lateral (ML), anterior-posterior (AP), and cranial-caudal (CC) direction of spinal cord in neck, chest, abdominal segments are 3.86, 5.37, 6.36 mm, 3.45, 3.83, 4.51 mm, 4.05, 4.83, 7.06 mm, respectively, and the calculated margin using residual Hausdorff method are 3.10, 5.33 and 6.15 mm, 3.30, 3.77, 4.61 mm, 3.35, 4.76, 6.87 mm, respectively.

Conclusion:

The setup errors and residual errors are different in each segment of the spinal cord. Different margins expansion should be applied to different segment of spinal cord.

The margin of internal risk volume on atrial septal and ventricular septal based on electrocardiograph gating 4DCT

Background

The aim of this study was to quantify the margin of internal risk volume (IRV) on the atrial septum (AS) and ventricular septum (VS) based on electrocardiograph gating (ECG-gating) 4DCT.

Methods

Twenty patients were enrolled and received an ECG-gating 4DCT scan performed in breath-hold, and CT images were reconstructed at 5% intervals of the cardiac cycle for a total of 20 phases (0-95%). The contouring of the AS and VS were delineated in each phase, and the displacements and margin of the AS and VS were calculated. We fused the total of the AS and VS (0-95% phase), which were recorded as AS₂₀ and VS₂₀. The margins were applied to the AS and VS in every phase and revised according to the cover rate of AS₂₀ and VS₂₀.

Results

(I) The margins of the AS and VS according to displacements in the left-right, cranio-caudal, and antero-posterior direction were 3 mm, 3 mm, and 3 mm; and 3 mm, 3 mm, and 2 mm, respectively. (II) The volume of AS₂₀ was (11.80±3.72) cm³, which was 2.9 times larger than the maximum volume of the AS. The volume of VS₂₀ was (60.45±12.92) cm³, which was 1.6 times larger than the maximum volume of the VS. (III) The emendatory margins of the AS and VS in the left-right, cranio-caudal, and antero-posterior direction were 7 mm, 10 mm, and 7 mm; and 5 mm, 3 mm, and 4 mm, respectively. The emendatory margins were added to the AS and VS, and the coverage rates were (95.88±3.29)% and (95.24±2.54)%, respectively.

Conclusions

The margin of IRV on the AS and VS could cover the movement of AS and VS induced by heartbeat in the left-right, cranio-caudal, and antero-posterior direction respectively during thoracic radiotherapy.

Quantifying inter-fraction cardiac substructure displacement during radiotherapy via magnetic resonance imaging guidance

Purpose

Emerging evidence suggests cardiac substructures are highly radiosensitive during radiation therapy for cancer treatment. However, variability in substructure position after tumor localization has not been well characterized. This study quantifies inter-fraction displacement and planning organ at risk volumes (PRVs) of substructures by leveraging the excellent soft tissue contrast of magnetic resonance imaging (MRI).

Methods

Eighteen retrospectively evaluated patients underwent radiotherapy for intrathoracic tumors with a 0.35 T MRI-guided linear accelerator. Imaging was acquired at a 17–25 s breath-hold (resolution 1.5 × 1.5 × 3 mm³). Three to four daily MRIs per patient (n = 71) were rigidly registered to the planning MRI-simulation based on tumor matching. Deep learning or atlas-based segmentation propagated 13 substructures (e.g., chambers, coronary arteries, great vessels) to daily MRIs and were verified by two radiation oncologists. Daily centroid displacements from MRI-simulation were quantified and PRVs were calculated.

Results

Across substructures, inter-fraction displacements for 14% in the left–right, 18% in the anterior-posterior, and 21% of fractions in the superior-inferior were > 5 mm. Due to lack of breath-hold compliance, ~4% of all structures shifted > 10 mm in any axis. For the chambers, median displacements were 1.8, 1.9, and 2.2 mm in the left–right, anterior-posterior, and superior-inferior axis, respectively. Great vessels demonstrated larger displacements (> 3 mm) in the superior-inferior axis (43% of shifts) and were only 25% (left–right) and 29% (anterior-posterior) elsewhere. PRVs from 3 to 5 mm were determined as anisotropic substructure-specific margins.

Conclusions

This exploratory work derived substructure-specific safety margins to ensure highly effective cardiac sparing. Findings require validation in a larger cohort for robust margin derivation and for applications in prospective clinical trials.

A Patient-Specific 3Dt Coronary Artery Motion Modeling Method Using Hierarchical Deformation with Electrocardiogram. SENSORS (BASEL, SWITZERLAND) 2020;20:E5680. [PMID: 33027998 PMCID: PMC7582594 DOI: 10.3390/s20195680]

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting postoperative procedures and processes. In particular, if shape information can be provided in real-time using electrocardiogram (ECG) signal information, the corresponding heart movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose a 3D+t cardiac coronary artery model which is rendered in real-time, according to the ECG signal, where hierarchical cage-based deformation modeling is used to generate the mesh deformation used during the procedure. We match the blood vessel's lumen obtained from the ECG-gated 3D+t CT angiography taken at multiple cardiac phases, in order to derive the optimal deformation. Splines for 3D deformation control points are used to continuously represent the obtained deformation in the multi-view, according to the ECG signal. To verify the proposed method, we compare the manually segmented lumen and the results of the proposed method for eight patients. The average distance and dice coefficient between the two models were 0.543 mm and 0.735, respectively. The required time for registration of the 3D coronary artery model was 23.53 s/model. The rendering speed to derive the model, after generating the 3D+t model, was faster than 120 FPS.

Adoption of Expansion Margins to Reduce the Dose Received by the Coronary Arteries and the Risk of Cardiovascular Events in Lymphoma Patients

PURPOSE

Mediastinal radiation therapy (RT) in patients with lymphoma implies involuntary coronary artery (CA) exposure, resulting in an increased risk of coronary artery disease (CAD). Accurate delineation of CAs may spare them from higher RT doses. However, heart motion affects the estimation of the dose received by CAs. An expansion margin (planning organ at risk volume [PRV]), encompassing the nearby area where CAs displace, may compensate for these uncertainties, reducing CA dose and CAD risk. Our study aimed to evaluate if a planning process optimized on CA-specific PRVs, rather than just on CAs, could provide any dosimetric or clinical benefit.

METHODS AND MATERIALS

Forty patients receiving RT for mediastinal lymphomas were included. We contoured left main trunk, left anterior descending, left circumflex, and right coronary arteries. An isotropic PRV was then applied to all CAs, in accordance with literature data. A comparison was then performed by optimizing treatment plans either on CAs or on PRVs, to detect any difference in CA sparing in terms of maximum (D_max), median (D_med), and mean (D_mean) dose. We then investigated, through risk modeling, if any dosimetric benefit obtained with the PRV-related optimization process could translate to a lower risk of ischemic complications.

RESULTS

Plan optimization on PRVs demonstrated a significant dose reduction (range, 7%-9%) in D_max, D_med, and D_mean for the whole coronary tree, and even higher dose reductions when vessels were located 5- to 20-mm from PTV (range, 13%-15%), especially for left main trunk and left circumflex (range, 16%-21%). This translated to a mean risk reduction of developing CAD of 12% (P < .01), which increased to 17% when CAs were located 5- to 20-mm from PTV.

CONCLUSIONS

Integration of CA-related PRVs in the optimization process reduces the dose received by CAs and translates to a meaningful prevention of CAD risk in patients with lymphoma treated with mediastinal RT.

Cardiac substructure segmentation with deep learning for improved cardiac sparing

PURPOSE

Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input.

MATERIALS/METHODS

Thirty-two left-sided whole-breast cancer patients underwent cardiac T2 MRI and CT-simulation. A rigid cardiac-confined MR/CT registration enabled ground truth delineations of 12 substructures (chambers, great vessels (GVs), coronary arteries (CAs), etc.). Paired MRI/CT data (25 patients) were placed into separate image channels to train a three-dimensional (3D) neural network using the entire 3D image. Deep supervision and a Dice-weighted multi-class loss function were applied. Results were assessed pre/post augmentation and post-processing (3D conditional random field (CRF)). Results for 11 test CTs (seven unique patients) were compared to ground truth and a multi-atlas method (MA) via Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Wilcoxon signed-ranks tests. Three physicians evaluated clinical acceptance via consensus scoring (5-point scale).

RESULTS

The model stabilized in ~19 h (200 epochs, training error <0.001). Augmentation and CRF increased DSC 5.0 ± 7.9% and 1.2 ± 2.5%, across substructures, respectively. DL provided accurate segmentations for chambers (DSC = 0.88 ± 0.03), GVs (DSC = 0.85 ± 0.03), and pulmonary veins (DSC = 0.77 ± 0.04). Combined DSC for CAs was 0.50 ± 0.14. MDA across substructures was <2.0 mm (GV MDA = 1.24 ± 0.31 mm). No substructures had statistical volume differences (P > 0.05) to ground truth. In four cases, DL yielded left main CA contours, whereas MA segmentation failed, and provided improved consensus scores in 44/60 comparisons to MA. DL provided clinically acceptable segmentations for all graded patients for 3/4 chambers. DL contour generation took ~14 s per patient.

CONCLUSIONS

These promising results suggest DL poses major efficiency and accuracy gains for cardiac substructure segmentation offering high potential for rapid implementation into RTP for improved cardiac sparing.