https://novaprd-lb.newcastle.edu.au/vital/access/manager/Index ${session.getAttribute("locale")} 5 Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:53776 Wed 28 Feb 2024 15:31:09 AEDT ]]> Pseudo-CT generation for MRI-only radiation therapy treatment planning: comparison among patch-based, atlas-based, and bulk density methods https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:34498 ref). Dosimetric endpoints were based on dose-volume histograms calculated from the CTref and the pCTs for various volumes of interest and on 3-dimensional gamma analyses. The PBM uncertainties were compared with those of the ABM and BDM. Results: The mean absolute error and mean error obtained from the PBM were 41.1 and –1.1 Hounsfield units. The PBM dose-volume histogram differences were 0.7% for prostate planning target volume V95%, 0.5% for rectum V70Gy, and 0.2% for bladder V50Gy. Compared with ABM and BDM, PBM provided significantly lower dose uncertainties for the prostate planning target volume (70-78 Gy), the rectum (8.5-29 Gy, 40-48 Gy, and 61-73 Gy), and the bladder (12-78 Gy). The PBM mean gamma pass rate (99.5%) was significantly higher than that of ABM (94.9%) or BDM (96.1%). Conclusions: The proposed PBM provides low uncertainties with dose planned on CTref. These uncertainties were smaller than those of ABM and BDM and are unlikely to be clinically significant.]]> Wed 23 Feb 2022 16:03:06 AEDT ]]> Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:52863 Mon 30 Oct 2023 10:01:55 AEDT ]]> Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:46578 2-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CTref) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CTref and pCT obtained by each method. Three-dimensional gamma indexes were analyzed. Results: Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CTref DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V95%, ≤0.5% for the rectum V70Gy, and ≤0.1% for the bladder V50Gy. The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM. Conclusions: Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time]]> Mon 28 Nov 2022 16:01:26 AEDT ]]> Pseudo-CT generation for MRI-only radiotherapy: comparative study between a generative adversarial network, a U-Net network, a patch-based, and an atlas based methods https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:38715 Mon 20 Nov 2023 15:50:10 AEDT ]]> Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:55076 Mon 08 Apr 2024 14:17:28 AEST ]]> Image synthesis for MRI-only radiotherapy treatment planning https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:46392 Fri 25 Nov 2022 13:59:19 AEDT ]]> Rectal and Urethro-Vesical Subregions for Toxicity Prediction After Prostate Cancer Radiation Therapy: Validation of Voxel-Based Models in an Independent Population https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:42286 Fri 17 Nov 2023 11:23:06 AEDT ]]> Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning https://novaprd-lb.newcastle.edu.au/vital/access/manager/Repository/uon:32135 Fri 04 May 2018 15:36:57 AEST ]]>