Chest Magnetic Resonance Imaging Decreases Inter-observer Variability of Gross Target Volume for Lung Tumors

Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis

Purpose

To compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis.

Method

A retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All positioning images were transferred to Eclipse treatment planning systems (TPSs). Six MRI sequences (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) were registered with positioning CT. Five radiation oncologists delineated the tumor boundary to obtain the gross tumor volume (GTV). Conformity index (CI) and dice coefficient (DC) were used to measure differences among observers.

Results

The differences in delineation mean volumes, CI, and DC among CT and MRIs were significant. Multiple comparisons were made between MRI sequences and CT. Among them, DWI, T2WI, and T1WI+C 10 minutes sequences were statistically significant with CT in mean volumes, DC, and CI. The mean volume of DWI, T2WI, and T1WI+C 10 minutes sequence in the target area is significantly smaller than that on the CT sequence, but the consistency is higher than that of CT sequences.

Conclusions

The recognition of atelectasis by MRI was better than that by CT, which could reduce interobserver variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis.

Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for Lung radiotherapy

PURPOSE

To compare gross tumour volume (GTV) delineation of lung cancer on magnetic resonance imaging (MRI) and positron emission tomography (PET) versus computed tomography (CT) and PET.

METHODS

Three experienced thoracic radiation oncologists delineated GTVs on twenty-six patients with lung cancer, based on CT registered to PET, T2-weighted MRI registered to PET and T1-weighted MRI registered with PET. All observers underwent education on reviewing T1 and T2 images along with guidance on window and level setup. Interobserver and intermodality variation was performed based ondice similarity coefficient (DSC), Hausdorff distance (HD), and average Hausdorff distance (AvgHD) metrics. To compute interobserver variability (IOV) a simultaneous truth and performance level estimation (STAPLE) volume for each image modality was used as reference volume. For intermodality analysis, each observers CT based primary and nodal GTV was used as reference volume.

RESULTS

A mean DSC of 0.9 across all observers for primary GTV (GTVp) and a DSC of > 0.7 for nodal GTV (GTVn) was demonstrated for IOV. Mean T2 and T1 GTVp and GTVn were smaller than CT GTVp and GTVn but the difference in volume between modalities was not statistically significant. Significant difference (p<0.01) for GTVp and GTVn was found between T2 and T1 GTVp and GTVn compared to CT GTVp and GTVn based on DSC metrics. Large variation in volume similarity was noted based on HD of up-to 5.4cm for observer volumes compared to STAPLE volume.

CONCLUSION

Interobserver variability in GTV delineation was similar for MRI and PET versus CT and PET. The significant difference between MRI compared to CT delineated volumes needs to be further explored.

MRI and CBCT for lymph node identification and registration in patients with NSCLC undergoing radical radiotherapy

PURPOSE

This study compared MRI to CBCT for the identification and registration of lymph nodes (LN) in patients with locally advanced (LA)-NSCLC, to assess the suitability of targeting LNs in future MR-image guided radiotherapy (MRgRT) workflows.

METHOD

Radiotherapy radiographers carried out Visual Grading Analysis (VGA) assessment of image quality, LN registration and graded their confidence in registration for each of the 24 LNs on CBCT and two MR sequences, MR1 (T2w Turbo Spin Echo) and MR2 (T1w DIXON water only image).

RESULTS

Pre-registration image quality assessment revealed MR1 and MR2 as significantly superior to CBCT in terms of image quality (p ≤ 0.01). No significant differences were noted in interobserver variability for LN registration between CBCT, MR1 and MR2. Observers were more confident in their MR registrations compared to their CBCT based LN registrations (p ≤ 0.02).

SUMMARY

Interobserver setup correction variability was not found to be significantly different between CBCT and MR. Image quality and registration confidence were found to be superior for MRI sequences. This is a promising step towards MR-guided radiotherapy for the treatment of LA-NSCLC.

Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula

BACKGROUND AND PURPOSE

4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times.

METHODS

Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study.

RESULTS

For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images.

CONCLUSION

Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.

The Value of PETRA in Pulmonary Nodules of <3 cm Among Patients With Lung Cancer

Objective

This study aimed to evaluate the visibility of different subgroups of lung nodules of <3 cm using the pointwise encoding time reduction with radial acquisition (PETRA) sequence on 3T magnetic resonance imaging (MRI) in comparison with that obtained using low-dose computed tomography (LDCT).

Methods

The appropriate detection rate was calculated for each of the different subgroups of lung nodules of <3 cm. The mean diameter of each detected nodule was determined. The detection rates and diameters of the lung nodules detected by MRI with the PETRA sequence were compared with those detected by computed tomography (CT). The sensitivity of detection for the different subgroups of pulmonary nodules was determined based on the location, size, type of nodules and morphologic characteristics. Agreement of nodule characteristics between CT and MRI were assessed by intraclass correlation coefficient (ICC) and Kappa test.

Results

The CT scans detected 256 lung nodules, comprising 99 solid nodules (SNs) and 157 subsolid nodules with a mean nodule diameter of 8.3 mm. For the SNs, the MRI detected 30/47 nodules of <6 mm in diameter and 52/52 nodules of ≥6 mm in diameter. For the subsolid nodules, the MRI detected 30/51 nodules of <6 mm in diameter and 102/106 nodules of ≥6 mm in diameter. The PETRA sequence returned a high detection rate (84%). The detection rates of SN, ground glass nodules, and PSN were 82%, 72%, and 94%, respectively. For nodules with a diameter of >6 mm, the sensitivity of the PETRA sequence reached 97%, with a higher rate for nodules located in the upper lung fields than those in the middle and lower lung fields. Strong agreement was found between the CT and PETRA results (correlation coefficients = 0.97).

Conclusion

The PETRA technique had high sensitivity for different type of nodule detection and enabled accurate assessment of their diameter and morphologic characteristics. It may be an effective alternative to CT as a tool for screening and follow up pulmonary nodules.