Assessment of thoracic vasculature in patients with central bronchogenic carcinoma by unenhanced magnetic resonance angiography: comparison between 2D free-breathing TrueFISP, 2D breath-hold TrueFISP and 3D respiratory-triggered SPACE

The age and gender-dependent changes in pulmonary arterial flow distribution: A cardiac magnetic resonance flow study

OBJECTIVE

To investigate whether the pulmonary artery blood flow distribution in the normal population changes depending on age and gender using the Cardiac Magnetic Resonance Phase Contrast Imaging method.

MATERIALS AND METHODS

A total of 62 healthy volunteers aged 20-60 years were included in the study between September 2020 and November 2021. Participants were first divided into two groups according to their age (group 120-40 years old, group 2: 40-60 years old). Then, each group was divided into two according to gender (groups 1A, 1B and groups 2A, 2B). After routine MR sequences of mediastinum and heart (True Fast Imaging with Steady State Free Precession, short axis for heart (SA), 4-chamber, 2-chamber and 3-chamber CINE sequence). Phase Contrast imaging was performed to Main Pulmonary Artery (MPA), right PA and left PA. On the phase contrast (PC) images obtained, flow velocity, forward flow, backward flow and net forward flow volume and gradient were calculated for each artery. With these data, the distribution ratio for both pulmonary arteries was calculated. Measurements were made simultaneously by two radiologists.

RESULTS

In phase contrast sequence evaluation; No statistically significant correlation was found between RPA/LPA distribution ratio and Group 1 (p > 0.05). A moderate and positive correlation was found statistically between RPA/LPA distribution ratio and Group 2 (r:0.42, p:0.04). No statistically significant correlation was found between the RPA/LPA distribution ratio and the gender variable (p:0.59).

CONCLUSION

The distribution ratio of pulmonary blood flow to both lungs in healthy volunteers can be measured using PC-CMR without the need for invasive procedures. Knowing the normal distribution ratios according to age in the population can be used for the early diagnosis of pathological conditions.

Value of Non-Contrast-Enhanced Vessel Wall MR Imaging in Assessing Vascular Invasion of Retroperitoneal Tumors

BACKGROUND

Due to their location and growth patterns, retroperitoneal tumors often involve the surrounding blood vessels. Clinical decisions on a proper treatment depend on the information on this condition. Evaluation of blood vessels using non-contrast-enhanced vessel wall MRI may provide noninvasive assessment of the extent of tumor invasion to assist clinical decision-making.

PURPOSE

To investigate the performance and potential of non-contrast-enhanced vessel wall MRI in evaluating the degree of vessel wall invasion of retroperitoneal tumors.

STUDY TYPE

Prospective.

POPULATION

Thirty-seven participants (mean age: 60.59 ± 11.77 years, 59% male) with retroperitoneal tumors close to vessels based on their diagnostic computer tomography.

FIELD STRENGTH/SEQUENCES

3 T; vessel wall MRI sequences: two-dimensional T2-weighted MultiVane XD turbo spin-echo (2D-T2-MVXD-TSE) and three-dimensional T1-weighted motion sensitized driven equilibrium fat suppression turbo spin-echo (3D-T1-MSDE-TSE) sequences; conventional MRI sequences: T2-weighted fat suppression turbo spin-echo (T2-FS-TSE), T2-weighted turbo spin-echo (T2-TSE), modified Dixon T1-weighted fast field echo (T1-mDixon-FFE), and diffusion-weighted echo planar imaging (DWI-EPI) sequences.

ASSESSMENT

All patients underwent preoperative imaging using both non-contrast conventional and vessel wall MRI sequences. Images obtained from conventional and vessel wall MRI sequences were evaluated independently by three junior radiologists (3 and 2 years of experience in reading MRI) and reviewed by one senior radiologist (25 years of experience in reading MRI) to assess the degree of vessel wall invasion. MRI were validated results from the clinical standard diagnosis based on surgical confirmation or histopathological reports. Interobserver agreement was determined based on the reports from three readers with similar years of experiences. Intraobserver variability was assessed based on categorizing and recategorizing the vessels of 37 patients 1 month apart.

STATISTICAL TESTS

Intra-class correlation efficient (ICC), Chi-square test, McNemar test, area under the receiver-operating characteristic curve (AUC), Delong test, P < 0.05 was considered significant.

RESULTS

The accuracy of vessel wall MRI (91.96%, 95% CI: 85.43-95.71; 103 of 112) in detecting the degree of vessel wall invasion was significantly higher than that of conventional MRI (75%, 95% CI: 66.24-82.10; 84 of 112). The interobserver variability or reproducibility in categorization of the degree of vascular wall invasion was good in evaluating images from conventional and vessel wall MRI sequences (ICC = 0.821, 95% CI: 0.765-0.867 and ICC = 0.881, 95% CI: 0.842-0.913, respectively).

DATA CONCLUSION

Diagnosis of vessel wall invasion of retroperitoneal tumors and assessment of its severity can be improved by using non-contrast-enhanced vessel wall MRI.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 3.

Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study

BACKGROUND

Anthropomorphic phantoms are used in surgical planning and intervention. Ideal accuracy and high efficiency are prerequisites for its clinical application. We aimed to develop a fully automated artificial intelligence-based three-dimensional (3D) reconstruction system (AI system) to assist thoracic surgery and to determine its accuracy, efficiency, and safety for clinical use.

METHODS

This AI system was developed based on a 3D convolutional neural network (CNN) and optimized by gradient descent after training with 500 cases, achieving a Dice coefficient of 89.2%. Accuracy was verified by comparing virtual structures predicted by the AI system with anatomical structures of patients in retrospective (n = 113) and prospective cohorts (n = 139) who underwent lobectomy or segmentectomy at the Peking University Cancer Hospital. Operation time and blood loss were compared between the retrospective cohort (without AI assistance) and prospective cohort (with AI assistance) for safety evaluation. The time consumption for reconstruction and the quality score were compared between the AI system and manual reconstruction software (Mimics®) for efficiency validation. This study was registered at https://www.chictr.org.cn as ChiCTR2100050985.

FINDINGS

The AI system reconstructed 13,608 pulmonary segmental branches from retrospective and prospective cohorts, and 1573 branches of interest corresponding to phantoms were detectable during the operation for verification, achieving 100% and 97% accuracy for segmental bronchi, 97.2% and 99.1% for segmental arteries, and 93.2% and 98.8% for segmental veins, respectively. With the assistance of the AI system, the operation time was shortened by 24.5 min for lobectomy (p < 0.001) and 20 min for segmentectomy (p = 0.007). Compared to Mimics®, the AI system reduced the model reconstruction time by 14.2 min (p < 0.001), and it also outperformed Mimics® in model quality scores (p < 0.001).

INTERPRETATION

The AI system can accurately predict thoracic anatomical structures with higher efficiency than manual reconstruction software. Constant optimization and larger population validation are required.

FUNDING

This study was funded by the Beijing Natural Science Foundation (No. L222020) and other sources.

Operative Planning in Thoracic Surgery: A Pilot Study Comparing Imaging Techniques and Three-Dimensional Printing

BACKGROUND

Careful preoperative planning in thoracic surgery is essential for positive outcomes, especially in video-assisted thoracic surgery (VATS), where palpation and 3-dimensional (3D) imaging is restricted. This study evaluated the ability of different imaging techniques, such as computed tomography (CT) scanning, maximal intensity projection imaging, 3D reconstruction, and 3D printing, to define the anatomy of the hilar structures before anatomical lung resection.

METHODS

All patients undergoing elective lung resections by VATS for cancer under a single surgeon were identified over a 3-month period. The surgeon was asked to record the number of pulmonary artery branches supplying the lobe to be resected by using the preoperative CT scans, maximal intensity projection images, and 3D-reconstructed CT images. The lung hilum in 3 patients was printed. These were then compared with the intraoperative findings.

RESULTS

The preoperative imaging of 16 patients was analyzed. The lung hilum was printed in a further 3 patients. Although not statistically significant, the 3D prints of the hilum were the most accurate measurement, with a correlation of 0.92. CT, 3D-reconstructed CT, and maximal intensity projection images tended to underrecognize the number of arterial branches and therefore scored between 0.26 and 0.39 in absolute agreement with the number of arteries found at operation.

CONCLUSIONS

3D printing in the planning of thoracic surgery may suggest a benefit over contemporary available imaging modalities, and the use of 3D printing in practicing operations is being established.