External validation of the Memorial Sloan Kettering Cancer Center preoperative nomogram predicting lymph node invasion in a cohort of high-grade prostate cancer patients.

BACKGROUND: Commonly used preoperative nomograms predicting clinical and pathological outcomes in prostate cancer (PCa) patients have not been yet validated in high-grade only PCa patients. Our objective is to perform an external validation of the Memorial Sloan Kettering Cancer Center (MSKCC) preoperative nomogram as a predictor of lymph node invasion (LNI) in a cohort of high-grade PCa patients. METHODS: We included patients with high-grade PCa (Gleason ≥8) treated at our institution between 2011 and 2020 with radical prostatectomy and pelvic lymph node dissection without receiving neoadjuvant or adjuvant therapy. The area under the curve (AUC) of the receiver operator characteristic (ROC) was used to quantify the accuracy of the model to predict LNI. A calibration plot was used to evaluate the model's precision, and a decision curve analysis was computed to evaluate the net benefit associated with its use. This study was approved by our institution's ethics board. RESULTS: A total of 242 patients with a median age of 66 (60-71) years were included. LNI was observed in 70 (29%) patients with a mean of 16 (median = 15; range = 2-42) resected nodes. The MSKCC nomogram discriminative accuracy, as evaluated by the AUC-ROC was 79.0% (CI: [0.727-0.853]). CONCLUSION: The MSKCC preoperative nomogram is a good predictor of LNI and a useful tool associated with net clinical benefit in this patient population.

Commissioning and implementing a Quality Assurance program for dedicated radiation oncology MRI scanners.

PURPOSE: ACR and AAPM task group's guidelines addressing commissioning for dedicated MR simulators were recently published. The goal of the current paper is to present the authors' 2-year experience regarding the commissioning and introduction of a QA program based on these guidelines and an associated automated workflow. METHODS: All mandatory commissioning tests suggested by AAPM report 284 were performed and results are reported for two MRI scanners (MAGNETOM Sola and Aera). Visual inspection, vendor clinical or service platform, third-party software, or in-house python-based code were used. Automated QA and data analysis was performed via vendor, in-house or third-party software. QATrack+ was used for QA data logging and storage. 3D geometric distortion, B0 inhomogeneity, EPI, and parallel imaging performance were evaluated. RESULTS: Contrasting with AAPM report 284 recommendations, homogeneity and RF tests were performed monthly. The QA program allowed us to detect major failures over time (shimming, gradient calibration and RF interference). Automated QA, data analysis, and logging allowed fast ACR analysis daily and monthly QA to be performed in 3 h. On the Sola, the average distortion is 1 mm for imaging radii of 250 mm or less. For radii of up to 200 mm, the maximum, average (standard deviation) distortion is 1.2  and 0.4 mm (0.3 mm). Aera values are roughly double the Sola for radii up to 200 mm. EPI geometric distortion, ghosting ratio, and long-term stability were found to be under the maximum recommended values. Parallel imaging SNR ratio was stable and close to the theoretical value (ideal g-factor). No major failures were detected during commissioning. CONCLUSION: An automated workflow and enhanced QA program allowed to automatically track machine and environmental changes over time and to detect periodic failures and errors that might otherwise have gone unnoticed. The Sola is more geometrically accurate, with a more homogenous B0 field than the Aera.

MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network.

PURPOSE: This study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time. METHODS: We used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Content, style, and L1 losses were used for training the generator to preserve the texture and structure information of the MRI and CT images. RESULTS: The MedFusionGAN successfully generates fused images with MRI soft-tissue and CT bone contrast. The results of the MedFusionGAN were quantitatively and qualitatively compared with seven traditional and eight deep learning (DL) state-of-the-art methods. Qualitatively, our method fused the source images with the highest spatial resolution without adding the image artifacts. We reported nine quantitative metrics to quantify the preservation of structural similarity, contrast, distortion level, and image edges in fused images. Our method outperformed both traditional and DL methods on six out of nine metrics. And it got the second performance rank for three and two quantitative metrics when compared with traditional and DL methods, respectively. To compare soft-tissue contrast, intensity profile along tumor and tumor contours of the fusion methods were evaluated. MedFusionGAN provides a more consistent, better intensity profile, and a better segmentation performance. CONCLUSIONS: The proposed end-to-end unsupervised method successfully fused MRI and CT images. The fused image could improve targets and OARs delineation, which is an important aspect of radiotherapy treatment planning.

Patient-specific geometrical distortion corrections of MRI images improve dosimetric planning accuracy of vestibular schwannoma treated with gamma knife stereotactic radiosurgery.

PURPOSE: To investigate the impact of MRI patient-specific geometrical distortion (PSD) on the quality of Gamma Knife stereotactic radiosurgery (GK-SRS) plans of the vestibular schwannoma (VS) tumors. METHODS AND MATERIALS: Three open access datasets including the MPI-Leipzig Mind-Brain-Body (318 patients), the slow event-related fMRI designs dataset (62 patients), and the VS dataset (242 patients) were used. We used first two datasets to train a 3D convolution network to predict the distortion map of third dataset that were then used to calculate and correct the PSD. GK-SRS plans of VS dataset were used to evaluate dose distribution of PSD-corrected MRI images. GK-SRS prescription dose of VS cases was 12 Gy. Geometric and dosimetric discrepancies were assessed between the dose distributions and contours before and after the PSD corrections. Geometry indices were center of the contours, Dice coefficient (DC), Hausdorff distance (HD), and dosimetric indices were D µ ${D_\mu }$ , D m a x ${D_{max}}$ , D m i n ${D_{min}}$ , and D 95 % ${D_{95{\mathrm{\% }}}}$ doses, target coverage (TC), Paddick's conformity index (PCI), Paddick's gradient index (GI), and homogeneity index (HI). RESULTS: Geometric distortions of about 1.2 mm were observed at the air-tissue interfaces at the air canal and nasal cavity borders. Average center of the targets was significantly distorted along the frequency encoding direction after the PSD-correction. Average DC and HD metrics were 0.90 and 2.13 mm. Average D µ ${D_\mu }$ , D 95 % , ${D_{95{\mathrm{\% ,}}}}$ and D m i n ${D_{min}}$ in Gy significantly increased after PSD correction from 16.85 to 17.25, 12.30 to 12.77, and from 8.98 to 9.92. D m a x ${D_{max}}$ did not significantly change after the correction. Average TC and PCI significantly increased from 0.97 to 0.98, and 0.94 to 0.96. Average GI decreased significantly from 2.24 to 2.15 after PSD correction. However, HI did not significantly change after the correction. CONCLUSION: The proposed method could predict and correct the PSD that indicates the importance of PSD correction before GK-SRS plans of the VS patients.

Simulating imaging-based tomographic systems using optical design software for resolving 3D structures of translucent media.

Imaging-based tomography is emerging as the technique of choice for resolving 3D structures of translucent media, in particular for applications in external beam radiation therapy and combustion diagnostics. However, designing experimental prototypes is time-consuming and costly, and is carried out without the certainty of the imaging optics being optimal. In this paper, we present an optical-design-software-based method that enables end-to-end simulation imaging-based tomography systems. The method, developed using the real ray tracing features of Zemax OpticStudio, was validated in the context of 3D scintillation dosimetry, where multiple imaging systems are used to image the 3D light pattern emitted within an irradiated cubic plastic scintillator volume. The flexibility of the workflow enabled the assessment and comparison of the tomographic performance of standard and focused plenoptic cameras for the reconstruction of a clinical radiation dose distribution. The versatility of the proposed method offers the potential to ease the developmental and optimization process of imaging systems used in volumetric emission computed tomography applications.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.

BACKGROUND: Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. OBJECTIVE: The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). METHODS: Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. RESULTS: All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. CONCLUSION: The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Response to Re: Estimating and reducing dose received by cardiac devices for patients undergoing radiotherapy.

In reply to Dimitris N. Mihailidis regarding our manuscript entitled "Estimating and reducing dose received by cardiac devices for patients undergoing radiotherapy".

Technical Note: Out-of-field dose measurement at near surface with plastic scintillator detector.

Out-of-field dose depends on multiple factors, making peripheral dosimetry com-plex. Only a few dosimeters have the required features for measuring peripheral dose. Plastic scintillator dosimeters (PSDs) offer numerous dosimetric advantages as required for out-of-field dosimetry. The purpose of this study is to determine the potential of using PSD as a surface peripheral dosimeter. Measurements were performed with a parallel-plate ion chamber, a small volume ion chamber, and with a PSD. Lateral-dose measurements (LDM) at 0.5 cm depth and depth-dose curve (PDD) were made and compared to the dose calculation provided by a treatment planning system (TPS). This study shows that a PSD can measure a dose as low as 0.51 ± 0.17 cGy for photon beam and 0.58 ± 0.20 cGy for electron beam with a difference of 0.2 and 0.1 cGy compared to a parallel-plate ion chamber. This study demonstrates the potential of using PSD as an out-of-field dosimeter since measure-ments with PSD avoid averaging over a too-large depth, at 1 mm diameter, and can make precise measurement at very low dose. Also, electronic equilibrium is easier to reach with PSD due to its small sensitive volume and its water equivalence.

Estimating and reducing dose received by cardiac devices for patients undergoing radiotherapy.

The objectives of this project are to quantify the dose reduction effect provided by a lead shield for patients with cardiac implantable electronic devices (CIED) during a clinically realistic radiation treatment on phantom and to provide a simple model of dose estimation to predict dose received by CIED in a wide range of situations. The shield used in this project is composed of a lead sheet wrapped in thermoplastic. Dose measurements were made with a plastic scintillation detector (PSD). The phantom was treated with ten different plans. Three of these cases were treated with intensity-modulated radiation therapy (IMRT) and the others received standard 3D conformal radiation therapy (3D CRT). Lateral dose measurement for photon fields was made to establish a dose prediction model. On average, the use of the lead shield reduced the dose to CIEDs by 19% ± 13%. Dose reduction was most important for breast cases, with a mean reduction of 31% ± 15%. In three cases, the total dose reduction was more than 25 cGy over the complete treatment. For the three IMRT cases, the mean dose reduction was 11% ± 9%. On average, the difference between the TPS prediction and the measurement was 71%, while it was only 14% for the dose prediction model. It was demonstrated that a lead shield can be efficiently used for reducing doses to CIED with a wide range of clinical plans. In patients treated with IMRT modality treatment, the shielding should be used only for those with more than two anterior fields over seven fields. In the case of 3D CRT patients, the shielding should be used for those with a dose on the CIED higher than 50 cGy and with a reduction of dose higher than 10 cGy. The dose prediction model developed in this study can be an easy way to have a better estimation of the out-of-field dose than the TPS.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM).

BACKGROUND: High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processing algorithms. PURPOSE: This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types. MATERIALS AND METHODS: This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step t $t$ of the diffusion process, and the other conditioning on both t $t$ and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value  < 0.05 $ < 0.05$ was considered statistically significant. RESULTS: Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values < 0.05 $< 0.05$ ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values < 0.05 $< 0.05$ ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values < 0.05 $< 0.05$ ) and had comparable performances compared with the supervised model (p-values > 0.05 $> 0.05$ ) to remove real motion artifacts. CONCLUSIONS: The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisition times.

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN.

Objective.This study developed an unsupervised motion artifact reduction method for magnetic resonance imaging (MRI) images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images.Approach.The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients'in-vivodatasets. An ablation was performed to evaluate the model's compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey's HSD tests.Main results.On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07 ± 0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93 ± 0.04), PSNR (30.63 ± 4.96), and VIF (0.45 ± 0.05) values, along with comparable MS-SSIM (0.96 ± 0.31). Similarly, our method outperformed comparative models in removingin-vivomotion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82 ± 0.52, 1.88 ± 0.71, and 1.02 ± 0.14 (p-values≪0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels.Significance.Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

High resolution 2D dose measurement device based on a few long scintillating fibers and tomographic reconstruction.

PURPOSE: Patient-specific QA of highly conformal radiotherapy treatments are usually conducted using 2D or 3D dosimetry of the incident dose distribution in a water-equivalent phantom. However, dosimeters typically used for this task usually lack in either spatial resolution or dose accuracy. The purpose of this work is to develop and validate a novel type of high resolution 2D dosimeter based on the tomographic reconstruction of the dose projections obtained using long scintillating fibers for the quality assurance of modern radiotherapy techniques such as IMRT. METHODS: Fifty parallel scintillating fibers were aligned in a 30 cm diameter cylindrical masonite phantom with a 95 cm source-to-surface distance and a 100 cm source-to-fibers distance. The fibers were disposed so that the effective detection area of the scintillating fibers was a 20 cm diameter disk. Both ends of each scintillating fiber were coupled to clear optical fibers to enable light collection by a single CCD camera. Seven IMRT segments and two square fields were acquired using 18 projections over a 170° rotation of the device. Computation of the dose integrals was made for each scintillating fiber using the irradiation of known rectangular reference fields. Dose reconstructions were conducted using a total-variation minimization iterative reconstruction algorithm. Eight monitor units were programmed for each projection and the reconstructed dose grid pixel resolution was set to 1 × 1 mm(2). RESULTS: 3%∕3 mm gamma tests conducted between the reconstructed IMRT dose distributions and the dose calculated with the treatment planning system Pinnacle(3) were on average successful for 99.6% of the dose pixels with a predicted dose of at least 10% of the maximum dose. The dose profiles for both square fields and IMRT segments agreed within 2% to the dose calculated with Pinnacle(3) except in high dose gradient regions, and were comparable to the dose measured using an ionization chamber array (IBA MatriXX) and radiographic films (Kodak XV2). CONCLUSIONS: Using tomographic reconstruction on the projections acquired with rotating scintillating fibers, we were able to perform water-equivalent 2D dosimetry of square fields and IMRT segments with acceptable accuracy and high spatial resolution. The underlying concept of tomographic dosimetry and the small number of fibers needed to reconstruct a given 2D dose distribution offer new dosimetric possibilities, both applicable to 2D and 3D dosimetry.

Characterization of lung tumors motion baseline using cone-beam computed tomography.

PURPOSE: To characterize the interfractional variability in lung tumor volume, position, and tumor boundaries. METHODS: Cone-beam computed tomography (CBCT) scans were acquired weekly during the course of treatment for 34 lung cancer patients (1-20 scans) with large tumors. Spatial registration based on bones was performed between contoured planning CT and CBCT. Gross tumor volume (GTV) on each CBCT was then contoured. Tumor volume, centroid, and boundaries variability were quantified. A commercial deformable registration software was tested and results were compared to manual contours. RESULTS: Mean volume reduction was 41 ± 32% (p < 0.001) after an average time of 51 days. Tumor centroid drifts were 0.03, 0.14, and -0.13 cm in right-left (RL), anterior-posterior (AP), and superior-inferior (SI) directions with standard deviations of 0.55, 0.50, and 0.51 cm. GTV boundaries displacements were -0.27, -0.14, and -0.16 cm with standard deviations of 0.64, 0.57, and 0.59 cm in RL, AP, and SI directions. Relative error between deformed and manual contours with the commercial deformable registration software rose up exponentially with the GTV decrease. CONCLUSIONS: GTV size changes for large lung tumors are similar to those for standard tumors. Magnitude absolute values of displacement vector for centroid and boundaries shifts show that there is not a preferred direction for the drifts but shrinkage.

Dosimetric performance and array assessment of plastic scintillation detectors for stereotactic radiosurgery quality assurance.

PURPOSE: To compare the performance of plastic scintillation detectors (PSD) for quality assurance (QA) in stereotactic radiosurgery conditions to a microion-chamber (IC), Gafchromic EBT2 films, 60 008 shielded photon diode (SD) and unshielded diodes (UD), and assess a new 2D crosshair array prototype adapted to small field dosimetry. METHODS: The PSD consists of a 1 mm diameter by 1 mm long scintillating fiber (BCF-60, Saint-Gobain, Inc.) coupled to a polymethyl-methacrylate optical fiber (Eska premier, Mitsubishi Rayon Co., Ltd., Tokyo, Japan). Output factors (S(c,p)) for apertures used in radiosurgery ranging from 4 to 40 mm in diameter have been measured. The PSD crosshair array (PSDCA) is a water equivalent device made up of 49 PSDs contained in a 1.63 cm radius area. Dose profiles measurements were taken for radiosurgery fields using the PSDCA and were compared to other dosimeters. Moreover, a typical stereotactic radiosurgery treatment using four noncoplanar arcs was delivered on a spherical phantom in which UD, IC, or PSD was placed. Using the Xknife planning system (Integra Radionics Burlington, MA), 15 Gy was prescribed at the isocenter, where each detector was positioned. RESULTS: Output Factors measured by the PSD have a mean difference of 1.3% with Gafchromic EBT2 when normalized to a 10 × 10 cm(2) field, and 1.0% when compared with UD measurements normalized to the 35 mm diameter cone. Dose profiles taken with the PSD crosshair array agreed with other single detectors dose profiles in spite of the presence of the 49 PSDs. Gamma values comparing 1D dose profiles obtained with PSD crosshair array with Gafchromic EBT2 and UD measured profiles shows 98.3% and 100.0%, respectively, of detector passing the gamma acceptance criteria of 0.3 mm and 2%. The dose measured by the PSD for a complete stereotactic radiosurgery treatment is comparable to the planned dose corrected for its SD-based S(c,p) within 1.4% and 0.7% for 5 and 35 mm diameter cone, respectively. Furthermore, volume averaging of the IC can be observed for the 5 mm aperture where it differs by as much as 9.1% compared to the PSD measurement. The angular dependency of the UD is also observed, unveiled by an under-response around 2.5% of both 5 and 35 mm apertures. CONCLUSIONS: Output Factors and dose profiles measurements performed, respectively, with the PSD and the PSDCA were in agreement with those obtained with the UD and EBT2 films. For stereotactic radiosurgery treatment verification, the PSD gives accurate results compared to the planning system and the IC once the latter is corrected to compensate for the averaging effect of the IC. The PSD provides precise results when used as a single detector or in a dense array, resulting in a great potential for stereotactic radiosurgery QA measurements.

Validating plastic scintillation detectors for photon dosimetry in the radiologic energy range.

PURPOSE: Photon dosimetry in the kilovolt (kV) energy range represents a major challenge for diagnostic and interventional radiology and superficial therapy. Plastic scintillation detectors (PSDs) are potentially good candidates for this task. This study proposes a simple way to obtain accurate correction factors to compensate for the response of PSDs to photon energies between 80 and 150 kVp. The performance of PSDs is also investigated to determine their potential usefulness in the diagnostic energy range. METHODS: A 1-mm-diameter, 10-mm-long PSD was irradiated by a Therapax SXT 150 unit using five different beam qualities made of tube potentials ranging from 80 to 150 kVp and filtration thickness ranging from 0.8 to 0.2 mmAl + 1.0 mmCu. The light emitted by the detector was collected using an 8-m-long optical fiber and a polychromatic photodiode, which converted the scintillation photons to an electrical current. The PSD response was compared with the reference free air dose rate measured with a calibrated Farmer NE2571 ionization chamber. PSD measurements were corrected using spectra-weighted corrections, accounting for mass energy-absorption coefficient differences between the sensitive volumes of the ionization chamber and the PSD, as suggested by large cavity theory (LCT). Beam spectra were obtained from x-ray simulation software and validated experimentally using a CdTe spectrometer. Correction factors were also obtained using Monte Carlo (MC) simulations. Percent depth dose (PDD) measurements were compensated for beam hardening using the LCT correction method. These PDD measurements were compared with uncorrected PSD data, PDD measurements obtained using Gafchromic films, Monte Carlo simulations, and previous data. RESULTS: For each beam quality used, the authors observed an increase of the energy response with effective energy when no correction was applied to the PSD response. Using the LCT correction, the PSD response was almost energy independent, with a residual 2.1% coefficient of variation (COV) over the 80-150-kVp energy range. Monte Carlo corrections reduced the COV to 1.4% over this energy range. All PDD measurements were in good agreement with one another except for the uncorrected PSD data, in which an over-response was observed with depth (13% at 10 cm with a 100 kVp beam), showing that beam hardening had a non-negligible effect on the PSD response. A correction based on LCT compensated very well for this effect, reducing the over-response to 3%. CONCLUSION: In the diagnostic energy range, PSDs show high-energy dependence, which can be corrected using spectra-weighted mass energy-absorption coefficients, showing no considerable sign of quenching between these energies. Correction factors obtained by Monte Carlo simulations confirm that the approximations made by LCT corrections are valid. Thus, PSDs could be useful for real-time dosimetry in radiology applications.

In-phantom dose verification of prostate IMRT and VMAT deliveries using plastic scintillation detectors.

The goal of this work was to demonstrate the feasibility of using a plastic scintillation detector (PSD) incorporated into a prostate immobilization device to verify doses in vivo delivered during intensity-modulated radiation therapy (IMRT) and volumetric modulated-arc therapy (VMAT) for prostate cancer. The treatment plans for both modalities had been developed for a patient undergoing prostate radiation therapy. First, a study was performed to test the dependence, if any, of PSD accuracy on the number and type of calibration conditions. This study included PSD measurements of each treatment plan being delivered under quality assurance (QA) conditions using a rigid QA phantom. PSD results obtained under these conditions were compared to ionization chamber measurements. After an optimal set of calibration factors had been found, the PSD was combined with a commercial endorectal balloon used for rectal distension and prostate immobilization during external beam radiotherapy. This PSD-enhanced endorectal balloon was placed inside of a deformable anthropomorphic phantom designed to simulate male pelvic anatomy. PSD results obtained under these so-called "simulated treatment conditions" were compared to doses calculated by the treatment planning system (TPS). With the PSD still inserted in the pelvic phantom, each plan was delivered once again after applying a shift of 1 cm anterior to the original isocenter to simulate a treatment setup error.The mean total accumulated dose measured using the PSD differed the TPS-calculated doses by less than 1% for both treatment modalities simulated treatment conditions using the pelvic phantom. When the isocenter was shifted, the PSD results differed from the TPS calculations of mean dose by 1.2% (for IMRT) and 10.1% (for VMAT); in both cases, the doses were within the dose range calculated over the detector volume for these regions of steep dose gradient. Our results suggest that the system could benefit prostate cancer patient treatment by providing accurate in vivo dose reports during treatment and verify in real-time whether treatments are being delivered according to the prescribed plan.

Direct in-water radiation dose measurements using Cherenkov emission corrected signals from polarization imaging for a clinical radiotherapy application.

Cherenkov emission (CE) is a visible blueish light emitted in water mediums irradiated by most radiotherapy treatment beams. However, CE is produced anisotropically which currently imposes a geometrical constraint uncertainty for dose measurements. In this work, polarization imaging is proposed and described as a method enabling precise 2D dose measurements using CE. CE produced in a water tank is imaged from four polarization angles using a camera coupled to a rotating polarizer. Using Malus' law, the polarized component of CE is isolated and corrected with Monte Carlo calculated CE polar and azimuthal angular distributions. Projected dose measurements resulting from polarization-corrected CE are compared to equivalent radiochromic film measurements. Overall, agreement between polarized corrected CE signal and films measurements is found to be within 3%, for projected percent depth dose (PPDD) and profiles at the different tested energies ([Formula: see text]: 6 and [Formula: see text], e[Formula: see text]: 6 and 18[Formula: see text]). In comparison, raw Cherenkov emission presented deviations up 60% for electron beam PPDDs and 20% for photon beams PPDDs. Finally, a degree of linear polarization between 29% and 47% was measured for CE in comparison to [Formula: see text]% for scintillation. Hence, polarization imaging is found to be a promising and powerful method for improved radio-luminescent dose measurements with possible extensions to signal separation.

Accurate dose measurements using Cherenkov emission polarization imaging.

PURPOSE: Cherenkov radiation carries the potential of direct in-water dose measurements, but its precision is currently limited by a strong anisotropy. Taking advantage of polarization imaging, this work proposes a new approach for high-accuracy Cherenkov emission dose measurements. METHODS: Cherenkov radiation produced in a 15 × 15 × 20-cm3 water tank is imaged with a cooled charge-coupled device (CCD) camera from four polarizer transmission axes [0, 45, 90, 135°]. The water tank is positioned at the isocenter of a 5 × 5-cm2 , 6-, and 18-MV photon beam. Using Malus' law, the polarized portion of the signal is extracted. Corrections are applied to the polarized signal following azimuthal and polar Cherenkov emission angular distributions extracted from Monte Carlo simulations. Projected percent depth dose and beam profiles are measured and compared with the prediction from a treatment planning system (TPS). RESULTS: Corrected polarized signals on the central axis reduced deviations at depth (mean ± standard deviation) from 8% ± 5% to 0.8% ± 1% at 6 MV and 8% ± 7% to 1% ± 3% at 18 MV. For the profile measurement, differences between the corrected polarized signal and the TPS calculations are 1% ± 3% and 2% ± 3% on the central axis at 6 and 18 MV respectively. In these conditions, Cherenkov emission is shown to be partly polarized. CONCLUSIONS: This work proposes a novel polarization imaging approach enabling high-precision water-based dose measurements using the Cherenkov radiation. The method allows a correction of the Cherenkov emission anisotropy within 4% on the beam central axis and in depth.

On the proper use of structural similarity for the robust evaluation of medical image synthesis models.

PURPOSE: To propose good practices for using the structural similarity metric (SSIM) and reporting its value. SSIM is one of the most popular image quality metrics in use in the medical image synthesis community because of its alleged superiority over voxel-by-voxel measurements like the average error or the peak signal noise ratio (PSNR). It has seen massive adoption since its introduction, but its limitations are often overlooked. Notably, SSIM is designed to work on a strictly positive intensity scale, which is generally not the case in medical imaging. Common intensity scales such as the Houndsfield units (HU) contain negative numbers, and they can also be introduced by image normalization techniques such as the z-normalization. METHODS: We created a series of experiments to quantify the impact of negative values in the SSIM computation. Specifically, we trained a three-dimensional (3D) U-Net to synthesize T2-weighted MRI from T1-weighted MRI using the BRATS 2018 dataset. SSIM was computed on the synthetic images with a shifted dynamic range. Next, to evaluate the suitability of SSIM as a loss function on images with negative values, it was used as a loss function to synthesize z-normalized images. Finally, the difference between two-dimensional (2D) SSIM and 3D SSIM was investigated using multiple 2D U-Nets trained on different planes of the images. RESULTS: The impact of the misuse of the SSIM was quantified; it was established that it introduces a large downward bias in the computed SSIM. It also introduces a small random error that can change the relative ranking of models. The exact values for this bias and error depend on the quality and the intensity histogram of the synthetic images. Although small, the reported error is significant considering the small SSIM difference between state-of-the-art models. It was shown therefore that SSIM cannot be used as a loss function when images contain negative values due to major errors in the gradient calculation, resulting in under-performing models. 2D SSIM was also found to be overestimated in 2D image synthesis models when computed along the plane of synthesis, due to the discontinuities between slices that is typical of 2D synthesis methods. CONCLUSION: Various types of misuse of the SSIM were identified, and their impact was quantified. Based on the findings, this paper proposes good practices when using SSIM, such as reporting the average over the volume of the image containing tissue and appropriately defining the dynamic range.

Knowledge-based planning algorithm for lung SBRT with robust Bayesian stochastic frontier analysis and missing data management.

PURPOSE: A knowledge-based planning technique is developed based on Bayesian stochastic frontier analysis. A novel missing data management is applied in order to handle missing organs-at-risk and work with a complete dataset. METHODS: Geometric metrics are used to predict DVH metrics for lung SBRT with a retrospective database of 299 patients. In total, 16 DVH metrics were predicted for the main bronchus, heart, esophagus, spinal cord PRV, great vessels, and chest wall. The predictive model is tested on a test group of 50 patients. RESULTS: Mean difference between the observed and predicted values ranges between 1.5 ± 1.9 Gy and 4.9 ± 5.3 Gy for the spinal cord PRV D0.35cc and the main bronchus D0.035cc, respectively. CONCLUSIONS: The missing data model implanted in the predictive model is robust in the estimation of the parameters. Bayesian stochastic frontier analysis with missing data management can be used to predict DVH metrics for lung SBRT treatment planning.