UNASSIGNED: Four-dimensional magnetic resonance imaging (4DMRI) has gained interest as an alternative to the current standard for motion management four-dimensional tomography (4DCT) in abdominal radiotherapy treatment planning (RTP). This review aims to assess the 4DMRI literature in abdomen, focusing on technical considerations and the validity of using 4DMRI for patients within radiotherapy protocols.
UNASSIGNED: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was performed across the Medline, Embase, Scopus, and Web of Science databases, covering all years up to December 31, 2023. The studies were grouped into two categories: 4DMRI reconstructed from 3DMRI acquisition; and 4DMRI reconstructed from multi-slice 2DMRI acquisition.
UNASSIGNED: A total of 39 studies met the inclusion criteria and were analysed to provide key findings. Key findings were 4DMRI had the potential to improve abdominal RTP for patients by providing accurate tumour definition and motion assessment compared to 4DCT. 4DMRI reconstructed from 3DMRI acquisition showed promise as a feasible approach for motion management in abdominal RTP regarding spatial resolution. Currently,the slice thickness achieved on 4DMRI reconstructed from multi-slice 2DMRI acquisitions was unsuitable for clinical purposes. Lastly, the current barriers for clinical implementation of 4DMRI were the limited availability of validated commercial solutions and the lack of larger cohort comparative studies to 4DCT for target delineation and plan optimisation.
UNASSIGNED: 4DMRI showed potential improvements in abdominal RTP, but standards and guidelines for the use of 4DMRI in radiotherapy were required to demonstrate clinical benefits.

BACKGROUND: This study validated the accuracy of the acromion marker cluster (AMC) and scapula spinal marker cluster (SSMC) methods compared with upright four-dimensional computed tomography (4DCT) analysis.
METHODS: Sixteen shoulders of eight healthy males underwent AMC and SSMC assessments. Active shoulder elevation was tracked using upright 4DCT and optical motion capture system. The scapulothoracic and glenohumeral rotation angles calculated from AMC and SSMC were compared with 4DCT. Additionally, the motion of these marker clusters on the skin with shoulder elevation was evaluated.
RESULTS: The average differences between AMC and 4DCT during 10°-140° of humerothoracic elevation were - 2.2° ± 7.5° in scapulothoracic upward rotation, 14.0° ± 7.4° in internal rotation, 6.5° ± 7.5° in posterior tilting, 3.7° ± 8.1° in glenohumeral elevation, - 8.3° ± 10.7° in external rotation, and - 8.6° ± 8.9° in anterior plane of elevation. The difference between AMC and 4DCT was significant at 120° of humerothoracic elevation in scapulothoracic upward rotation, 50° in internal rotation, 90° in posterior tilting, 120° in glenohumeral elevation, 100° in external rotation, and 100° in anterior plane of elevation. However, the average differences between SSMC and 4DCT were - 7.5 ± 7.7° in scapulothoracic upward rotation, 2.0° ± 7.0° in internal rotation, 2.3° ± 7.2° in posterior tilting, 8.8° ± 7.9° in glenohumeral elevation, 2.0° ± 9.1° in external rotation, and 1.9° ± 10.1° in anterior plane of elevation. The difference between SSMC and 4DCT was significant at 50° of humerothoracic elevation in scapulothoracic upward rotation and 60° in glenohumeral elevation, with no significant differences observed in other rotations. Skin motion was significantly smaller in AMC (28.7 ± 4.0 mm) than SSMC (38.6 ± 5.8 mm). Although there was smaller skin motion in AMC, SSMC exhibited smaller differences in scapulothoracic internal rotation, posterior tilting, glenohumeral external rotation, and anterior plane of elevation compared to 4DCT.
CONCLUSIONS: This study demonstrates that AMC is more accurate for assessing scapulothoracic upward rotation and glenohumeral elevation, while SSMC is preferable for evaluating scapulothoracic internal rotation, posterior tilting, glenohumeral external rotation, and anterior plane of elevation, with smaller differences compared to 4DCT.

BACKGROUND: In single-isocenter multitarget stereotactic body radiotherapy (SBRT), geometric miss risks arise from uncertainties in intertarget position. However, its assessment is inadequate, and may be interfered by the reconstructed tumor position errors (RPEs) during simulated CT and cone beam CT (CBCT) acquisition. This study aimed to quantify intertarget position variations and assess factors influencing it.
METHODS: We analyzed data from 14 patients with 100 tumor pairs treated with single-isocenter SBRT. Intertarget position variation was measured using 4D-CT simulation to assess the intertarget position variations (ΔD) during routine treatment process. Additionally, a homologous 4D-CBCT simulation provided RPE-free comparison to determine the impact of RPEs, and isolating purely tumor motion induced ΔD to evaluate potential contributing factors.
RESULTS: The median ΔD was 4.3 mm (4D-CT) and 3.4 mm (4D-CBCT). Variations exceeding 5 mm and 10 mm were observed in 31.1% and 5.5% (4D-CT) and 20.4% and 3.4% (4D-CBCT) of fractions, respectively. RPEs necessitated an additional 1-2 mm safety margin. Intertarget distance and breathing amplitude variability showed weak correlations with variation (Rs = 0.33 and 0.31). The ΔD differed significantly by locations (upper vs. lower lobe and right vs. Left lung). Notably, left lung tumor pairs exhibited the highest risk.
CONCLUSIONS: This study provide a reliable way to assess intertarget position variation by using both 4D-CT and 4D-CBCT simulation. Consequently, single-isocenter SBRT for multiple lung tumors carries high risk of geometric miss. Tumor motion and RPE constitute a substantial portion of intertarget position variation, requiring correspondent strategies to minimize the intertarget uncertainties.

UNASSIGNED: This prospective study tested the diagnostic accuracy, and absolute agreement with MRI of a low-dose CT protocol for left ventricular ejection fraction (LVEF) measurement. Furthermore we assessed its potential for combining it with Chest-Abdomen-Pelvis CT (CAP-CT) for a one-stop examination.
UNASSIGNED: Eighty-two patients underwent helical low-dose CT. Cardiac magnetic resonance imaging (MRI) was the reference standard. In fifty patients, CAP-CT was performed concurrently, using a modified injection protocol. In these, LVEF was measured with radioisotope cardiography (MUGA). Patients >18 years, without contrast media or MRI contraindications, were included. Bias was measured with Bland-Altman analysis, classification accuracy with Receiver Operating Characteristics, and inter-reader agreement with Intra-Class Correlation Coefficient (ICC). Correlation was examined using Pearson\'s correlation coefficients. CAP image quality was compared to previous scans with visual grading characteristics.
UNASSIGNED: The mean CT dose-length-product (DLP) was 51.8 mGycm, for an estimated effective dose of 1.4 mSv, compared to 5.7 mSv for MUGA. CT LVEF bias was between 2 % and 10 %, overestimating end-diastolic volume. When corrected for bias, sensitivity and specificity of 100 and 98.5 % for classifying reduced LVEF (50 % MRI value) was achieved. ICC for MUGA was significantly lower than MRI and CT. Distinction of renal medulla and cortex was reduced in the CAP scan, but proportion of diagnostic scans was not significantly different from standard protocol.
UNASSIGNED: When corrected for inter-modality bias, CT classifies patients with reduced LVEF with high accuracy at a quarter of MUGA dose and can be combined with CAP-CT without loss of diagnostic quality.

Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time-resolved information. To overcome this limitation, 4D-CBCT was developed to incorporate a time dimension in the imaging to account for the patient\'s motion during the acquisitions. For example, respiration-correlated 4D-CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D-CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D-CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x-ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low-dose, and high-quality 4D-CBCT. This paper aims to review the technical developments surrounding 4D-CBCT comprehensively. It will explore conventional algorithms and recent deep learning-based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D-CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D-CBCT\'s capabilities and potential to enhance radiation therapy.

In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient\'s DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.

Purpose: To evaluate the dosimetric effects of intrafraction baseline shifts combined with rotational errors on Four-dimensional computed tomography-guided stereotactic body radiotherapy for multiple liver metastases (MLMs). Methods: A total of 10 patients with MLM (2 or 3 lesions) were selected for this retrospective study. Baseline shift errors of 0.5, 1.0, and 2.0 mm; and rotational errors of 0.5°, 1°, and 1.5°, were simulated about all axes. All of the baseline shifts and rotation errors were simulated around the planned isocenter using a matrix transformation of 6° of freedom. The coverage degradation of baseline shifts and rotational errors were analyzed according to the dose to 95% of the planning target volume (D95) and the volume covered by 95% of the prescribed dose (V95), and related changes in gross tumor volume were also analyzed. Results: At the rotation error of 0.5° and the baseline offset of less than 0.5 mm, the D95 and V95 values of all targets were >95%. For rotational errors of 1.0° (combined with all baseline shift errors), 36.3% of targets had D95 and V95 values of <95%. Coverage worsened substantially when the baseline shift errors were increased to 1.0 mm. D95 and V95 values were >95% for about 77.3% of the targets. Only 11.4% of the D95 and V95 values were >95% when the baseline shift errors were increased to 2.0 mm. When the rotational error was increased to 1.5° and baseline shift errors increased to 1.0 mm, the D95 and V95 values were >95% in only 3 cases. Conclusions: The multivariate regression model analysis in this study showed that the coverage of the target decreased further with reduced target volume, increasing the baseline drift, the rotation error, and the distance to the target.

OBJECTIVE: Today, respiratory movement can be monitored and recorded with different methods during a simulation on a four-dimensional (4D) computed tomography (CT) device to be used in radiotherapy planning. A synchronized respiratory monitoring system (RPM) with an externally equipped device is one of these methods. Another method is to create 4D images of the patient\'s breathing phases without the need for extra equipment, with an anatomy-based software program integrated into the CT device. Our aim is to compare the RPM system and the software system (Deviceless) which are two different respiratory monitoring methods used in tracking moving targets during 4D-CT imaging and to assess their clinical usability.
METHODS: Ten patients who underwent paraaortic nodal irradiation were enrolled. The simulation was performed using intravenous contrast material on a 4D-CT device with both respiratory monitoring methods. The right/left kidneys and renal arteries were chosen as references to evaluate abdominal organ movement. It was then manually contoured one by one on both sets of images. The images were compared volumetrically and geometrically after rigid reconstruction. The similarity between the contours was determined by the Dice index. Wilcoxon test was used for statistical comparisons.
RESULTS: The motion of the kidneys in all three directions was found to be 0.0 cm in both methods. The shifts in the right/left renal arteries were submillimetric. The Dice index showed a high similarity in both kidney and renal artery contours.
CONCLUSIONS: In our study, no difference was found between RPM and Deviceless systems used for tracking and detection of moving targets during simulation in 4D-CT. Both methods can be used safely for radiotherapy planning according to the available possibilities in the clinic.

OBJECTIVE: To evaluate the effectiveness of abdominal compression (AC) as a respiratory motion management method for the heart and stomach during stereotactic arrhythmia radioablation (STAR).
METHODS: 4D computed tomography (4DCT) scans of patients imaged with AC or without AC (free-breathing: FB) were obtained from ventricular-tachycardia (VT) (n = 3), lung cancer (n = 18), and liver cancer (n = 18) patients. Patients treated for VT were imaged both FB and with AC. Lung and liver patients were imaged once with FB or with AC, respectively. The heart, left ventricle (LV), LV components (LVCs), and stomach were contoured on each phase of the 4DCTs. Centre of mass (COM) translations in the left/right (LR), ant/post (AP), and sup/inf (SI) directions were measured for each structure. Minimum distances between LVCs and the stomach over the respiratory cycle were also measured on each 4DCT phase. Mann-Whitney U-tests were performed between AC and FB datasets with a significance of α = 0.05.
RESULTS: No statistical difference (all p values were >0.05) was found in COM translations between FB and AC patient datasets for all contoured cardiac structures. A reduction in COM translation with AC relative to FB was patient, direction, and structure specific for the three VT patients. A significant decrease in the AP range of motion of the stomach was observed under AC compared to FB. No statistical difference was found between minimum distances to the stomach and LVCs between FB and AC.
CONCLUSIONS: AC was not a consistent motion management method for STAR, nor does not uniformly affect the separation distance between LVCs and the stomach. If AC is employed in future STAR protocols, the motion of the target volume and its relative distance to the stomach should be compared on two 4DCTs: one while the patient is FB and one under AC.

OBJECTIVE: To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration.
METHODS: Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset.
RESULTS: The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively.
CONCLUSIONS: A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion.
CONCLUSIONS: The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.