Serial and parallel organ-at-risk-specific noncoplanar arc optimization for small versus large target volumes in liver SBRT.

Noncoplanar arc optimization has been shown to reduce OAR doses in SRS/SRT and has the potential to reduce doses to OARs in SBRT. Extracranial targets have additional considerations, including large OARs and, in the case of the liver, volume constraints on the healthy liver. Considering pathlengths through OARs that encompass target volumes may lead to specific dose reductions as in the encompassing healthy liver tissue. These optimizations must also leverage delivery efficiency and trajectory sampling to ensure ease of clinical translation. The purpose of this research is to generate optimized static-couch arcs that separately consider serial and parallel OARs and arc delivery efficiency, with a trajectory sampling metric, towards the aim of reducing dose to OARs and the surrounding healthy liver tissue. Separate BEV cost maps were created for parallel, and serial OARs by means of a fast ray-triangle intersection algorithm. An additional BEV cost map was created for the liver which, by definition, encompasses the liver tumors. The individual costs of these maps were summed and combined with the sampling metric for 100 000 random combinations of arc trajectories. A search algorithm was applied to find an arc trajectory solution that satisfied BEV cost and sampling optimization, while also ensuring an efficient delivery was possible with a low number of arcs. This method of arc selection was evaluated for 16 liver SBRT patients characterized by small and large target volumes. Comparisons were made with a clinical arc template of coplanar arcs. Dosimetric plan quality was evaluated using published guidelines and metrics from RTOG1112. Four of five plan quality metrics for the liver were significantly reduced when planned with optimized noncoplanar arcs. Median (range) reductions of the volumes receiving 10, 18, and 21 Gy were found of 140.4 (295.8) cc (p = 0.001), 28.2 (230.6) cc (p = 0.002) and 18.5 (155.5) cc (p = 0.04). A significant increase in median (range) dose to the right kidney of 0.2 ± 0.9 Gy (p = 0.03) was also found using optimized noncoplanar arcs, which was below the tolerance of 10 Gy for all cases. The average number of arcs chosen was 4 ± 1. Optimizing serial and parallel OARs separately during static couch noncoplanar arc selection significantly reduced the dose to the liver during SBRT using a moderate number of arcs.

SBRT for Liver Tumors: What the Interventional Radiologist Needs to Know.

This review summarizes the clinical evidence supporting the utilization of stereotactic body radiotherapy (SBRT) for liver tumors, including hepatocellular carcinoma, liver metastases, and cholangiocarcinoma. Emerging prospective evidence has demonstrated the benefit and low rates of toxicity across a broad range of clinical contexts. We provide an introduction for the interventional radiologist, with a discussion of underlying themes such as tumor dose-response, mitigation of liver toxicity, and the technical considerations relevant to performing liver SBRT. Ultimately, we recommend that SBRT should be routinely included in the armamentarium of locoregional therapies for liver malignancies, alongside those liver-directed therapies offered by interventional radiology.

Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver.

Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.

Transcatheter Arterial Chemoembolization Imaging Features in MR-Linac Radiation Therapy Planning for the Liver.

For MR-guided radiation therapy treatment planning, an MRI and CT of the intended treatment site are typically acquired. Patients' prior treatments or procedures can cause image artifacts in one or both scans, which may impact treatment planning or the radiation dose calculation. In this case report, a patient with several previous transcatheter arterial chemoembolization (TACE) procedures was planned for radiation therapy on a low-field MR-linac, and the impact of residual iodinated oil on the radiation dose calculation and MR-guided adaptive workflow was evaluated.