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PURPOSE: CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT. METHODS: Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms. RESULTS: For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94 ± 20HU. Gamma values mean ± std (min-max) were 98.9% ± 0.9% (97.1%-100%) and 97.7% ± 1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively. CONCLUSION: MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning.

Objective. Kilovoltage computed tomography (kVCT) is the cornerstone of radiotherapy treatment planning for delineating tissues and towards dose calculation. For the former, kVCT provides excellent contrast and signal-to-noise ratio. For the latter, kVCT may have greater uncertainty in determining relative electron density (ρe) and proton stopping power ratio (SPR). Conversely, megavoltage CT (MVCT) may result in superior dose calculation accuracy. The purpose of this work was to convert kVCT HU to MVCT HU using deep learning to obtain higher accuracyρeand SPR.Approach. Tissue-mimicking phantoms were created to compare kVCT- and MVCT-determinedρeand SPR to physical measurements. Using 100 head-and-neck datasets, an unpaired deep learning model was trained to learn the relationship between kVCTs and MVCTs, creating synthetic MVCTs (sMVCTs). Similarity metrics were calculated between kVCTs, sMVCTs, and MVCTs in 20 test datasets. An anthropomorphic head phantom containing bone-mimicking material with known composition was scanned to provide an independent determination ofρeand SPR accuracy by sMVCT.Main results. In tissue-mimicking bone,ρeerrors were 2.20% versus 0.19% and SPR errors were 4.38% versus 0.22%, for kVCT versus MVCT, respectively. Compared to MVCT,in vivomean difference (MD) values were 11 and 327 HU for kVCT and 2 and 3 HU for sMVCT in soft tissue and bone, respectively.ρeMD decreased from 1.3% to 0.35% in soft tissue and 2.9% to 0.13% in bone, for kVCT and sMVCT, respectively. SPR MD decreased from 1.8% to 0.24% in soft tissue and 6.8% to 0.16% in bone, for kVCT and sMVCT, respectively. Relative to physical measurements,ρeand SPR error in anthropomorphic bone decreased from 7.50% and 7.48% for kVCT to

PurposeMegavoltage computed tomography (MVCT) has been implemented on many radiation therapy treatment machines as a tomographic imaging modality that allows for three-dimensional visualization and localization of patient anatomy. Yet MVCT images exhibit lower contrast and greater noise than its kilovoltage CT (kVCT) counterpart. In this work, we sought to improve these disadvantages of MVCT images through an image-to-image-based machine learning transformation of MVCT and kVCT images. We demonstrated that by learning the style of kVCT images, MVCT images can be converted into high-quality synthetic kVCT (skVCT) images with higher contrast and lower noise, when compared to the original MVCT.MethodsKilovoltage CT and MVCT images of 120 head and neck (H&N) cancer patients treated on an Accuray TomoHD system were retrospectively analyzed in this study. A cycle-consistent generative adversarial network (CycleGAN) machine learning, a variant of the generative adversarial network (GAN), was used to learn Hounsfield Unit (HU) transformations from MVCT to kVCT images, creating skVCT images. A formal mathematical proof is given describing the interplay between function sensitivity and input noise and how it applies to the error variance of a high-capacity function trained with noisy input data. Finally, we show how skVCT shares distributional similarity to kVCT for various macro-structures found in the body.ResultsSignal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were improved in skVCT images relative to the original MVCT images and were consistent with kVCT images. Specifically, skVCT CNR for muscle-fat, bone-fat, and bone-muscle improved to 14.8 ± 0.4, 122.7 ± 22.6, and 107.9 ± 22.4 compared with 1.6 ± 0.3, 7.6 ± 1.9, and 6.0 ± 1.7, respectively, in the original MVCT images and was more consistent with kVCT CNR values of 15.2 ± 0.8, 124.9 ± 27.0, and 109.7 ± 26.5, respectively. Noise was significantly reduced in skVCT images with SNR values improving by roughly an order of magnitude and consistent with kVCT SNR values. Axial slice mean (S-ME) and mean absolute error (S-MAE) agreement between kVCT and MVCT/skVCT improved, on average, from -16.0 and 109.1 HU to 8.4 and 76.9 HU, respectively.ConclusionsA kVCT-like qualitative aid was generated from input MVCT data through a CycleGAN instance. This qualitative aid, skVCT, was robust toward embedded metallic material, dramatically improves HU alignment from MVCT, and appears perceptually similar to kVCT with SNR and CNR values equivalent to that of kVCT images.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V100 and V120 of the PTV, V60 of the rectum, and heterogeneity index.

Purpose/objectivesFor lung stereotactic body radiation therapy (SBRT), real-time tumor tracking (RTT) allows for less radiation to normal lung compared to the internal target volume (ITV) method of respiratory motion management. To quantify the advantage of RTT, we examined the difference in radiation pneumonitis risk between these two techniques using a normal tissue complication probability (NTCP) model.Materials/method20 lung SBRT treatment plans using RTT were replanned with the ITV method using respiratory motion information from a 4D-CT image acquired at the original simulation. Risk of symptomatic radiation pneumonitis was calculated for both plans using a previously derived NTCP model. Features available before treatment planning that identified significant increase in NTCP with ITV versus RTT plans were identified.ResultsPrescription dose to the planning target volume (PTV) ranged from 22 to 60 Gy in 1-5 fractions. The median tumor diameter was 3.5 cm (range 2.1-5.5 cm) with a median volume of 14.5 mL (range 3.6-59.9 mL). The median increase in PTV volume from RTT to ITV plans was 17.1 mL (range 3.5-72.4 mL), and the median increase in PTV/lung volume ratio was 0.46% (range 0.13-1.98%). Mean lung dose and percentage dose-volumes were significantly higher in ITV plans at all levels tested. The median NTCP was 5.1% for RTT plans and 8.9% for ITV plans, with a median difference of 1.9% (range 0.4-25.5%, pairwise P < 0.001). Increases in NTCP between plans were best predicted by increases in PTV volume and PTV/lung volume ratio.ConclusionsThe use of RTT decreased the risk of radiation pneumonitis in all plans. However, for most patients the risk reduction was minimal. Differences in plan PTV volume and PTV/lung volume ratio may identify patients who would benefit from RTT technique before completing treatment planning.

This study compares conventional and robotic intensity modulated radiation therapy (IMRT) plans for prostate boost treatments and provides clinical insight into the strengths and weaknesses of each. The potential for dose escalation with robotic IMRT is further investigated using the "critical volume tolerance" method proposed by Roach et al. Three clinically acceptable treatment plans were generated for 10 prostate boost patients: (1) a robotic IMRT plan using fixed cones, (2) a robotic IMRT plan using the Iris variable aperture collimator, and (3) a conventional linac based IMRT (c-IMRT) plan. Target coverage, critical structure doses, homogeneity, conformity, dose fall-off, and treatment time, were compared across plans. The average bladder and rectum V75 was 17.1%, 20.0%, and 21.4%, and 8.5%, 11.9%, and 14.1% for the Iris, fixed, and c-IMRT plans, respectively. On average the conformity index (nCI) was 1.20, 1.30, and 1.46 for the Iris, fixed, and c-IMRT plans. Differences between the Iris and the c-IMRT plans were statistically significant for the bladder V75 (P= .016), rectum V75 (P= .0013), and average nCI (P =.002). Dose to normal tissue in terms of R50 was 4.30, 5.87, and 8.37 for the Iris, fixed and c-IMRT plans, respectively, with statistically significant differences between the Iris and c-IMRT (P = .0013) and the fixed and c-IMRT (P = .001) plans. In general, the robotic IMRT plans generated using the Iris were significantly better compared to c-IMRT plans, and showed average dose gains of up to 34% for a critical rectal volume of 5%.

Stereotactic body radiotherapy for prostate cancer is rapidly growing in popularity. Stereotactic body radiotherapy plans mimic those of high-dose rate brachytherapy, with tight margins and inhomogeneous dose distributions. The impact of interfraction anatomical changes on the dose received by organs at risk under these conditions has not been well documented. To estimate anatomical variation during stereotactic body radiotherapy, 10 patients were identified who received a prostate boost using robotic stereotactic body radiotherapy after completing 25 fractions of pelvic radiotherapy with daily megavoltage computed tomography. Rectal and bladder volumes were delineated on each megavoltage computed tomography, and the stereotactic body radiotherapy boost plan was registered to each megavoltage computed tomography image using a point-based rigid registration with 3 fiducial markers placed in the prostate. The volume of rectum and bladder receiving 75% of the prescription dose (V75%) was measured for each megavoltage computed tomography. The rectal V75% from the daily megavoltage computed tomographies was significantly greater than the planned V75% (median increase of 0.93 cm3, P < .001), whereas the bladder V75% on megavoltage computed tomography was not significantly changed (median decrease of -0.12 cm3, P = .57). Although daily prostate rotation was significantly correlated with bladder V75% (Spearman ρ = .21, P = .023), there was no association between rotation and rectal V75% or between prostate deformation and either rectal or bladder V75%. Planning organ-at-risk volume-based replanning techniques using either a 6-mm isotropic expansion of the plan rectal contour or a 1-cm expansion from the planning target volume in the superior and posterior directions demonstrated significantly improved rectal V75% on daily megavoltage computed tomographies compared to the original stereotactic body radiotherapy plan, without compromising plan quality. Thus, despite tight margins and full translational and rotational corrections provided by robotic stereotactic body radiotherapy, we find that interfraction anatomical variations can lead to a substantial increase in delivered rectal doses during prostate stereotactic body radiotherapy. A planning organ-at-risk volume-based approach to treatment planning may help mitigate the impact of daily organ motion and reduce the risk of rectal toxicity.

The purpose of this study was to evaluate the performance of a commercially available CyberKnife system with a multileaf collimator (CK-MLC) for stereotactic body radiotherapy (SBRT) and standard fractionated intensity-modulated radiotherapy (IMRT) applications. Ten prostate and ten intracranial cases were planned for the CK-MLC. Half of these cases were compared with clinically approved SBRT plans generated for the CyberKnife with circular collimators, and the other half were compared with clinically approved standard fractionated IMRT plans generated for conventional linacs. The plans were compared on target coverage, conformity, homogeneity, dose to organs at risk (OAR), low dose to the surrounding tissue, total monitor units (MU), and treatment time. CK-MLC plans generated for the SBRT cases achieved more homogeneous dose to the target than the CK plans with the circular collimators, for equivalent coverage, conformity, and dose to OARs. Total monitor units were reduced by 40% to 70% and treatment time was reduced by half. The CK-MLC plans generated for the standard fractionated cases achieved prescription isodose lines between 86% and 93%, which was 2%-3% below the plans generated for conventional linacs. Compared to standard IMRT plans, the total MU were up to three times greater for the prostate (whole pelvis) plans and up to 1.4 times greater for the intracranial plans. Average treatment time was 25 min for the whole pelvis plans and 19 min for the intracranial cases. The CK-MLC system provides significant improvements in treatment time and target homogeneity compared to the CK system with circular collimators, while maintaining high conformity and dose sparing to critical organs. Standard fractionated plans for large target volumes (>100 cm3) were generated that achieved high prescription isodose levels. The CK-MLC system provides more efficient SRS and SBRT treatments and, in select clinical cases, might be a potential alternative for standard fractionated treatments. PACS numbers: 87.56.nk, 87.56.bd.

Treatment plans for prostate cancer patients undergoing stereotactic body radiation therapy (SBRT) are often challenging due to the proximity of organs at risk. Today, there are no objective criteria to determine whether an optimal treatment plan has been achieved, and physicians rely on their personal experience to evaluate the plan's quality. In this study, we propose a method for determining rectal and bladder dose constraints achievable for a given patient's anatomy. We expect that this method will improve the overall plan quality and consistency, and facilitate comparison of clinical outcomes across different institutions. The 3D proximity of the organs at risk to the target is quantified by means of the expansion-intersection volume (EIV), which is defined as the intersection volume between the target and the organ at risk expanded by 5 mm. We determine a relationship between EIV and relevant dosimetric parameters, such as the volume of bladder and rectum receiving 75% of the prescription dose (V75%). This relationship can be used to establish institution-specific criteria to guide the treatment planning and evaluation process. A database of 25 prostate patients treated with CyberKnife SBRT is used to validate this approach. There is a linear correlation between EIV and V75% of bladder and rectum, confirming that the dose delivered to rectum and bladder increases with increasing extension and proximity of these organs to the target. This information can be used during the planning stage to facilitate the plan optimization process, and to standardize plan quality and consistency. We have developed a method for determining customized dose constraints for prostate patients treated with robotic SBRT. Although the results are technology specific and based on the experience of a single institution, we expect that the application of this method by other institutions will result in improved standardization of clinical practice.