Linac- and CyberKnife-based MRI-only treatment planning of prostate SBRT using an optimized synthetic CT calibration curve.

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.

Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network.

BACKGROUND: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation. PURPOSE: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning. METHODS: MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs). RESULTS: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively. CONCLUSIONS: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning.

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 <1% for both MVCT and sMVCT.Significance. Deep learning can be used to map kVCT to sMVCT, suggesting higher accuracyρeand SPR is achievable with sMVCT versus kVCT.

Hyperpolarized Metabolic MRI-Acquisition, Reconstruction, and Analysis Methods.

Hyperpolarized metabolic MRI with 13C-labeled agents has emerged as a powerful technique for in vivo assessments of real-time metabolism that can be used across scales of cells, tissue slices, animal models, and human subjects. Hyperpolarized contrast agents have unique properties compared to conventional MRI scanning and MRI contrast agents that require specialized imaging methods. Hyperpolarized contrast agents have a limited amount of available signal, irreversible decay back to thermal equilibrium, bolus injection and perfusion kinetics, cellular uptake and metabolic conversion kinetics, and frequency shifts between metabolites. This article describes state-of-the-art methods for hyperpolarized metabolic MRI, summarizing data acquisition, reconstruction, and analysis methods in order to guide the design and execution of studies.

Technical Note: A methodology for improved accuracy in stopping power estimation using MRI and CT.

PURPOSE: Proton therapy is becoming an increasingly popular cancer treatment modality due to the proton's physical advantage in that it deposits the majority of its energy at the distal end of its track where the tumor is located. The proton range in a material is determined from the stopping power ratio (SPR) of the material. However, SPR is typically estimated based on a computed tomography (CT) scan which can lead to range estimation errors due to the difference in x-ray and proton interactions in matter, which can preclude the ability to utilize protons to their full potential. Applications of magnetic resonance imaging (MRI) in radiotherapy have increased over the past decade and using MRI to calculate SPR directly could provide numerous advantages. The purpose of this study was to develop a practical implementation of a novel multimodal imaging method for estimating SPR and compare the results of this method to physical measurements in which values were computed directly using tissue substitute materials fabricated to mimic skin, muscle, adipose, and spongiosa bone. METHODS: For both the multimodal imaging method and physical measurements, SPR was calculated using the Bethe-Bloch equation from values of relative electron density and mean ionization potential determined for each tissue. Parameters used to estimate SPR using the multimodal imaging method were extracted from Dixon water-only and (1 H) proton density-weighted zero echo time MRI sequences and CT, with both kVCT and MVCT used separately to evaluate the performance of each. For comparison, SPR was also computed from kVCT using the stoichiometric method, the current clinical standard. RESULTS: Results showed that our multimodal imaging approach using MRI with either kVCT or MVCT was in close agreement to SPR calculated from physical measurements for the four tissue substitutes evaluated. Using MRI and MVCT, SPR values estimated using our method were within 1% of physical measurements and were more accurate than the stoichiometric method for the tissue types studied. CONCLUSIONS: We have demonstrated the methodology for improved estimation of SPR using the proposed multimodal imaging framework.

PET/MRI: Where might it replace PET/CT?

Simultaneous positron emission tomography and MRI (PET/MRI) is a technology that combines the anatomic and quantitative strengths of MR imaging with physiologic information obtained from PET. PET and computed tomography (PET/CT) performed in a single scanning session is an established technology already in widespread and accepted use worldwide. Given the higher cost and complexity of operating and interpreting the studies obtained on a PET/MRI system, there has been question as to which patients would benefit most from imaging with PET/MRI versus PET/CT. In this article, we compare PET/MRI with PET/CT, detail the applications for which PET/MRI has shown promise and discuss impediments to future adoption. It is our hope that future work will prove the benefit of PET/MRI to specific groups of patients, initially those in which PET/CT and MRI are already performed, leveraging simultaneity and allowing for greater degrees of multiparametric evaluation. LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:1247-1262.

Ultrashort echo time MRI of cortical bone at 7 tesla field strength: a feasibility study.

PURPOSE: To implement and examine the feasibility of a three-dimensional (3D) ultrashort TE (UTE) sequence on a 7 Tesla (T) clinical MR scanner in comparison with 3T MRI at high isotropic resolution. MATERIALS AND METHODS: Using an in-house built saddle coil at both field strengths we have imaged mid-diaphysial sections of five fresh cadaveric specimens of the distal tibia. An additional in vivo scan was performed at 7 Tesla using a quadrature knee coil. RESULTS: Using the same type of saddle coil at both field strengths, a significant increase in SNR at 7T compared with 3T (factor 1.7) was found. Significantly shorter T2* values were found at the higher field strength (T2* = 552.2 ± 126 µs at 7T versus T2* = 1163 ± 391 µs at 3T). CONCLUSION: UHF MRI at 7T has great potential for imaging tissues with short T2.