Stereotactic ablative radiotherapy (SABR) is susceptible to challenges for tumours affected by intrafraction organ motion. This study aims to investigate the effect of breathing characteristics and plan complexity on the interplay effect.

A patient-specific interplay effect evaluation was performed using in-house software with an alpha version of the treatment planning verification software Verisoft (PTW-Freiburg, Germany) on VMAT plans. The OCTAVIUS 4D phantom was used to acquire the static dose distribution, and the simulation approach was utilised to generate the moving dose distribution. The influence of plan complexity, PTV size, number of breaths, and motion amplitudes on the interplay effect were examined. The dose distribution of two extreme phases—end-inhale and end-exhale—was considered using the gamma criteria of 2%/2 mm for the interplay effect evaluation.

A strong correlation was found between the motion amplitude (p < 0.001) and the NBs (p < 0.001) with the gamma-passing rate. No correlation was found between the gamma-passing rate and the PTV size or plan complexity.

The simulation tool allowed the analysis of a large number of breathing traces, demonstrating how free-breathing patients, suspected of high interplay, could be selected for other motion management solutions. The simulated cases showed strong interplay effects for long breathing periods with extended motion amplitudes in a small group of patients.

T1 mapping is a recently developed imaging analysis method that allows quantitative assessment of myocardial T1 values obtained using MRI. In children, MRI is performed under free-breathing. Thus, it is important to know the changes in T1 values between free-breathing and breath-holding. This study aimed to compare the myocardial T1 mapping during breath-holding and free-breathing.

Thirteen patients and eight healthy volunteers underwent cardiac MRI, and T1 values obtained during breath-holding and free-breathing were examined and compared. Statistical differences were determined using the paired t-test.

The mean T1 values during breath-holding were 1211.1 ± 39.0 ms, 1209.7 ± 37.4 ms, and 1228.9 ± 52.5 ms in the basal, mid, and apical regions, respectively, while the mean T1 values during free-breathing were 1165.1 ± 69.0 ms, 1103.7 ± 55.8 ms, and 1112.0 ± 81.5 ms in the basal, mid, and apical regions, respectively. The T1 values were lower during free-breathing than during breath-holding in almost all segments (basal: p = 0.008, mid: p < 0.001, apical: p < 0.001). The mean T1 values in each cross section were 3.1, 7.8, and 7.7% lower during free-breathing than during breath-holding in the basal, mid, and apical regions, respectively.

We found that myocardial T1 values during free-breathing were about 3–8% lower in all cross sections than those during breath-holding. In free-breathing, it may be difficult to assess myocardial T1 values, except in the basal region, because of underestimation; thus, the findings should be interpreted with caution, especially in children.

Lung tumours, especially those in the lower lobes, can move a lot during respiration; this motion needs to be accounted for during radiotherapy. In cases where 4D CT simulation scans are not performed, the current protocol at our centre is to apply a generic (internal motion + setup) margin of 0·70 cm in the axial plane and 1·20 cm in the longitudinal plane to all lung tumours, regardless of location. We analyse the tumour motions of a cohort of our local patients and categorise them into different locations in the lung. We seek to assess the adequacy of the current margins and to derive a more accurate set of standard margins which are specific for lung tumour locations.

All cases of lung tumours treated with stereotactic ablative radiotherapy between 2012 and 2016 were identified retrospectively and 4D CT scan data analysed. These tumours were grouped into the following locations: upper zone (UZ), middle zone (MZ) and lower zone (LZ). The treatment planning system was used to generate the displacements of the centre of mass of the tumours in the right–left, anterior–posterior and superior–inferior axes; these were compared with the current generic margins. Median displacements were calculated for each axis in each location. New planning target volume (PTV) margins were derived by summing the median displacement, median absolute deviation (MAD) and 0·5 cm (for setup error).

Sixty-three cases were eligible for analyses. Motion in the superior–inferior direction was the greatest for all tumour locations, ranging from a median of 0·17 cm (MAD 0·12 cm) in UZ to 0·77 cm (MAD 0·27 cm) in LZ. Median tumour displacements in the anterior–posterior and right–left axes were similar for all locations, <0·30 and 0·20 cm, respectively. The current generic margins were adequate for only one-third of the cases in this study. A new PTV margin of 2·10 cm in the superior–inferior axis may be required for LZ tumours, while an additional 1–2 mm should be added to the current radial margins.

The current generic margins are inadequate for the majority of cases. Tumour motion is the greatest in LZ in the superior–inferior axis. Motion mitigation strategies are essential for large LZ tumours.

The purpose of this study is to investigate quantitatively the correlation of displacement vector fields (DVFs) from different deformable image registration (DIR) algorithms to register images from helical computed tomography (HCT), axial computed tomography (ACT) and cone beam computed tomography (CBCT) with motion parameters.

CT images obtained from scanning of the mobile phantom were registered with the stationary CT images using four DIR algorithms from the DIRART software: Demons, Fast-Demons, Horn–Schunck and Lucas–Kanade. HCT, ACT and CBCT imaging techniques were used to image a mobile phantom, which included three targets with different sizes (small, medium and large) that were manufactured from a water-equivalent material and embedded in low-density foam to simulate lung lesions. The phantom was moved with controlled cyclic motion patterns where a range of motion amplitudes (0–20 mm) and frequencies (0·125–0·5 Hz) were used.

The DVF obtained from different algorithms correlated well with motion amplitudes applied on the mobile phantom for CBCT and HCT, where the maximal DVF increased linearly with the motion amplitudes of the mobile phantom. In ACT, the DVF correlated less with motion amplitudes where motion-induced strong image artefacts and the DIR algorithms were not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from Fast-Demons deviated strongly from other algorithms at large motion amplitudes.

The local DVFs provide direct quantitative values for the actual internal tumour shifts that can be used to determine margins for the internal target volume that consider tumour motion during treatment planning. Furthermore, the DVF distributions can be used to extract motion parameters such as motion amplitude that can be extracted from the maximal or minimal DVF calculated by the different DIR algorithms and used in the management of the patient motion.

To quantify the effect of breathing motion on post-mastectomy radiotherapy with three-dimensional (3D) tangents and intensity-modulated radiotherapy (IMRT)

Patients trained for breath-hold underwent routine free breathing (FB) computed tomography (CT) simulation for radiotherapy as well as additional CT scans with breath held at the end of normal inspiration (NI scan) and expiration (NE scan) for study. The FB scan was used to develop both tangents and IMRT plans. To simulate breathing, each plan was copied and applied on NI and NE scans. The respiratory parameters of the patients as well as the dosimetric data with both the plans were analysed.

Breathing motion resulted in mean fall in target coverage (V95) with IMRT by more than 5% when compared with tangents, and this effect significantly correlated with higher tidal volume. There was also a decrease in the mean target minimal dose by 20–25% with IMRT when compared with 10–12% with tangents, attributable to breathing motion. However, the cardiac dose crossed the limit (V25<10%) with breathing in the 3D tangents plan.

Dosimetric coverage of the chest wall is sensitive to breathing motion for the IMRT technique when compared with standard tangents, especially in patients with large tidal volume.