Plastimatch—An Open-Source Software for Radiotherapy Imaging

Shackleford, James; Kandasamy, Nagarajan; Sharp, Gregory C.

doi:10.1016/b978-0-12-407741-6.00006-2

Cited by 24 publications

(25 citation statements)

References 12 publications

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“…A patient CT dataset that best represented the average anatomy of the cohort served as common template and mapping target. Non-rigid registration was performed with the 3DSlicer SlicerRT Plastimatch B-spline deformable registration module ( 17 , 19 , 20 ). After a first rigid registration step (subsampling 2 × 2 × 1, maximum of 100 iterations), a 3-stage B-Spline deformable registration (stage 1: subsampling 2,2,1, grid 25 mm, regularization 0.01, landmark penalty 0.005, maximum iterations 100, stage 2: subsampling 2,2,1, grid 10 mm, regularization 0.01, landmark penalty 0.005, maximum iterations 100, stage 3: subsampling 1,1,1, grid 2 mm, regularization 0.01, landmark penalty 0.005, maximum iterations 100) with Mean Squared Error as cost function empirically provided the best results and was used in all cases.…”

Section: Methodsmentioning

confidence: 99%

Section: Mapping Analysismentioning

confidence: 99%

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The Distribution of Pelvic Nodal Metastases in Prostate Cancer Reveals Potential to Advance and Personalize Pelvic Radiotherapy

et al. 2021

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BackgroundTraditional clinical target volume (CTV) definition for pelvic radiotherapy in prostate cancer consists of large volumes being treated with homogeneous doses without fully utilizing information on the probability of microscopic involvement to guide target volume design and prescription dose distribution.MethodsWe analyzed patterns of nodal involvement in 75 patients that received RT for pelvic and paraaortic lymph node metastases (LNs) from prostate cancer in regard to the new NRG-CTV recommendation. Non-rigid registration-based LN mapping and weighted three-dimensional kernel density estimation were used to visualize the average probability distribution for nodal metastases. As independent approach, the mean relative proportion of LNs observed for each level was determined manually and NRG and non-NRG levels were evaluated for frequency of involvement. Computer-automated distance measurements were used to compare LN distances in individual patients to the spatial proximity of nodal metastases at a cohort level.Results34.7% of patients had pelvic LNs outside NRG-consensus, of which perirectal was most common (25.3% of all patients) followed by left common iliac nodes near the left psoas major (6.7%). A substantial portion of patients (13.3%) had nodes at the posterior edge of the NRG obturator level. Observer-independent mapping consistently visualized high-probability hotspots outside NRG-consensus in the perirectal and left common iliac regions. Affected nodes in individual patients occurred in highly significantly closer proximity than at cohort-level (mean distance, 6.6 cm vs. 8.7 cm, p < 0.001).ConclusionsBased on this analysis, the common iliac level should extend to the left psoas major and obturator levels should extend posteriorly 5 mm beyond the obturator internus. Incomplete coverage by the NRG-consensus was mostly because of perirectal involvement. We introduce three-dimensional kernel density estimation after non-rigid registration-based mapping for the analysis of recurrence data in radiotherapy. This technique provides an estimate of the underlying probability distribution of nodal involvement and may help in addressing institution- or subgroup-specific differences. Nodal metastases in individual patients occurred in highly significantly closer proximity than at a cohort-level, which supports that personalized target volumes could be reduced in size compared to a “one-size-fits-all” approach and is an important basis for further investigation into individualized field designs.

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Section: Methodsmentioning

confidence: 99%

Section: Mapping Analysismentioning

confidence: 99%

The Distribution of Pelvic Nodal Metastases in Prostate Cancer Reveals Potential to Advance and Personalize Pelvic Radiotherapy

et al. 2021

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“…All contours were initially stored in the DICOM RT‐struct format, which represents structures as point clouds. The segmentations were converted into binary masks using plastimatch 25 with nearest neighbors interpolation, in order to be suitable for the subsequent neural network training. The image‐binary mask pairs were cropped/padded around the PTV center to a size of

220 \times 220 \times 220

pixels, which in all but one case, covered all structures of interest with a substantial margin.…”

Section: Methodsmentioning

confidence: 99%

“…The images were acquired using the clinical balanced steady-state free-precession (bSSFP) sequence resulting in a T2 * /T1 image contrast, and had a resolution of 1.5 mm × 1.5 mm × 1.5 mm or 1.5 mm × 1.5 mm × 3 mm. 12 The latter were resampled to 1.5 mm × 1.5 mm × 1.5 mm in the scope of this study, using the plastimatch convert 25 function with nearest neighbor interpolation. All patients were treated following a similar workflow (Figure 1), which consisted of an initial offline planning phase and irradiation in 5-33 fractions.…”

Section: Databasementioning

confidence: 99%

Patient‐specific transfer learning for auto‐segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi‐centric evaluation

et al. 2022

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Background: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustments allow to capture anatomical changes, improve target volume coverage, and reduce the risk of side effects. The introduction of automatic segmentation techniques could help to further improve the online adaptive workflow by shortening the re-contouring time and reducing intra-and inter-observer variability. In fractionated RT, prior knowledge, such as planning images and manual expert contours, is usually available before irradiation, but not used by current artificial intelligence-based autocontouring approaches. Purpose: The goal of this study was to train convolutional neural networks (CNNs) for automatic segmentation of bladder, rectum (organs at risk, OARs), and clinical target volume (CTV) for prostate cancer patients treated at 0.35 T MR-Linacs. Furthermore, we tested the CNNs generalization on data from independent facilities and compared them with the MR-Linac treatment planning system (TPS) propagated structures currently used in clinics. Finally, expert planning delineations were utilized for patient-(PS) and facility-specific (FS) transfer learning to improve auto-segmentation of CTV and OARs on fraction images. Methods: In this study, data from fractionated treatments at 0.35 T MR-Linacs were leveraged to develop a 3D U-Net-based automatic segmentation. Cohort C1 had 73 planning images and cohort C2 had 19 planning and 240 fraction images. The baseline models (BMs) were trained solely on C1 planning data using 53 MRIs for training and 10 for validation. To assess their accuracy, the models were tested on three data subsets: (i) 10 C1 planning images not used for training, (ii) 19 C2 planning, and (iii) 240 C2 fraction images. BMs also served as a starting point for FS and PS transfer learning, where the planning images from C2 were used for network parameter fine tuning. The segmentation output of the different trained models was compared against expert ground truth by means of geometric metrics. Moreover, a trained physician graded the network segmentations as well as the segmentations propagated by the clinical TPS.

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“…Therefore, the second set of plans are for the synthesized TB-10MV photons. The dose for the two sets of plans was calculated using the AAA algorithm at a dose grid of 1×1×1 mm 3 , and the 3D dose matrices were exported in DICOM format for 3D Gamma analysis using the Plastimatch toolkit (18). Since both dose matrices share the same coordinate system from the water phantom in TPS, the DTA criteria was set at a low value of 1 mm.…”

Section: Validation In the Water Phantommentioning

confidence: 99%

Does radiation therapy need more than two photon energies from Linac?

Zhang

Zhou²,

Liu³

et al. 2022

Front. Oncol.

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PurposeModern Linacs are equipped with multiple photon energies for radiation therapy, and proper energy is chosen for each case based on tumor characteristics and patient anatomy. The aim of this study is to investigate whether it is necessary to have more than two photons energies.MethodsThe principle of photon energy synthesis is presented. It is shown that a photon beam of any intermediate energy (Esyn) can be synthesized from a linear combination of a low energy (Elow) and a high energy (Ehigh). The principle is validated on a wide range of scenarios: different intermediate photon energies on the same Linac; between Linacs from the same manufacturer or different manufacturers; open and wedge beams; and extensive photon energies available from published reference data. In addition, 3D dose distributions in water phantom are compared using Gamma analysis. The method is further demonstrated in clinical cases of various tumor sites and multiple treatment modalities. Experimental measurements are performed for IMRT plans and they are analyzed using the standard clinical protocol.ResultsThe synthesis coefficients vary with energy and field size. The root mean square error (RMSE) is within 1.1% for open and wedge fields. Excellent agreement was observed for British Journal of Radiology (BJR) data with an average RMSE of 0.11%. The 3D Gamma analysis shows a good match for all field sizes in the water phantom and all treatment modalities for the five clinical cases. The minimum gamma passing rate of 95.7% was achieved at 1%/1mm criteria for two measured dose distributions of IMRT plans.ConclusionA Linac with two photon energies is capable of producing dosimetrically equivalent plans of any energy in-between through the photon energy synthesis, supporting the notion that there is no need to equip more than two photon energies on each Linac. This can significantly reduce the cost of equipment for radiation therapy.

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Plastimatch—An Open-Source Software for Radiotherapy Imaging

Cited by 24 publications

References 12 publications

The Distribution of Pelvic Nodal Metastases in Prostate Cancer Reveals Potential to Advance and Personalize Pelvic Radiotherapy

The Distribution of Pelvic Nodal Metastases in Prostate Cancer Reveals Potential to Advance and Personalize Pelvic Radiotherapy

Patient‐specific transfer learning for auto‐segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi‐centric evaluation

Does radiation therapy need more than two photon energies from Linac?

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