Treatment assessment of radiotherapy using MR functional quantitative imaging

Prospective evaluation of in vivo and phantom repeatability and reproducibility of diffusion-weighted MRI sequences on 1.5 T MRI-linear accelerator (MR-Linac) and MR simulator devices for head and neck cancers

INTRODUCTION

Diffusion-weighted imaging (DWI) on MRI-linear accelerator (MR-linac) systems can potentially be used for monitoring treatment response and adaptive radiotherapy in head and neck cancers (HNC) but requires extensive validation. We performed technical validation to compare six total DWI sequences on an MR-linac and MR simulator (MR sim) in patients, volunteers, and phantoms.

METHODS

Ten human papillomavirus-positive oropharyngeal cancer patients and ten healthy volunteers underwent DWI on a 1.5 T MR-linac with three DWI sequences: echo planar imaging (EPI), split acquisition of fast spin echo signals (SPLICE), and turbo spin echo (TSE). Volunteers were also imaged on a 1.5 T MR sim with three sequences: EPI, BLADE (vendor tradename), and readout segmentation of long variable echo trains (RESOLVE). Participants underwent two scan sessions per device and two repeats of each sequence per session. Repeatability and reproducibility within-subject coefficient of variation (wCV) of mean ADC were calculated for tumors and lymph nodes (patients) and parotid glands (volunteers). ADC bias, repeatability/reproducibility metrics, SNR, and geometric distortion were quantified using a phantom.

RESULTS

In vivo repeatability/reproducibility wCV for parotids were 5.41%/6.72%, 3.83%/8.80%, 5.66%/10.03%, 3.44%/5.70%, 5.04%/5.66%, 4.23%/7.36% for EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE. Repeatability/reproducibility wCV for EPIMR-linac, SPLICE, TSE were 9.64%/10.28%, 7.84%/8.96%, 7.60%/11.68% for tumors and 7.80%/9.95%, 7.23%/8.48%, 10.82%/10.44% for nodes. All sequences except TSE had phantom ADC biases within ± 0.1x10-3 mm2/s for most vials (EPIMR-linac, SPLICE, and BLADE had 2, 3, and 1 vials out of 13 with larger biases, respectively). SNR of b = 0 images was 87.3, 180.5, 161.3, 171.0, 171.9, 130.2 for EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE.

CONCLUSION

MR-linac DWI sequences demonstrated near-comparable performance to MR sim sequences and warrant further clinical validation for treatment response assessment in HNC.

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application

Purpose

To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.

Methods

Based on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.

Results

The proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted.

Conclusion

The developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer

Recently, the interest to integrate magnetic resonance imaging (MRI) in radiotherapy for prostate cancer has increased considerably. MRI can contribute in all steps of the radiotherapy workflow from diagnosis, staging, and target definition to treatment follow-up. Of particular interest is the ability of MRI to provide a wide range of functional measures. The complexity of MRI as an imaging modality combined with the growing interest of the application to prostate cancer radiotherapy, emphasize the need for dedicated education within the radiation oncology community. In this context, an overview of the most common as well as a few upcoming functional MR imaging techniques is presented: the basic methodology and measurement is described, the link between the functional measures and the underlying biology is established, and finally relevant applications of functional MRI useful for prostate cancer radiotherapy are given.

Assessment of concurrent stereotactic radiosurgery and bevacizumab treatment of recurrent malignant gliomas using multi-modality MRI imaging and radiomics analysis

PURPOSE

To assess the response and predict the overall survival (OS) of recurrent malignant gliomas (MG) patients treated with concurrent BVZ/SRS using multi-modality MRI imaging and radiomics analysis.Methods and materials: SRS was delivered in a single fraction (18/24Gy) or 25Gy in 5 fractions. BVZ was administered immediately before SRS and 2 weeks after. MRI scans were performed before SRS, 1 week and 2 months after SRS. The MR protocol included 2 anatomical (T1w and T2w) and 2 functional (dynamic contrast-enhanced DCE-MRI and diffusion weighted DW-MRI) modalities. Functional biomarkers including apparent diffusion coefficient (ADC), micro-vascular transfer constant Ktrans, brain blood flow F_B, and blood volume V_B were analyzed. Radiomics analysis was performed to extract imaging features from anatomical MRI images and functional biomarker maps. Wicoxon signed rank tests were performed to evaluate treatment-induced changes, and Mann-Whitney U tests were performed to compare the differences of treatment-induced changes between different patient groups. Selected biomarkers and radiomics features were used to predict the OS after treatment using Support Vector Regression (SVR) with leave-one-out cross validation (LOOCV).

RESULTS

Twelve patients with recurrent MG were studied. The median OS was 13.8 months post SRS. DCE results showed that Ktrans (p=0.035) and V_B (p=0.035) showed significant decrease 2 months after SRS, and F_B showed significant decrease as early as 1 week (p=0.017) after SRS. No functional parameters reflected statistically significant treatment response 1 week after SRS. A total of 888 radiomics features were extracted. 31/126 features demonstrated significant changes 1 week/2 months after SRS, respectively. 9 features' changes were significantly different between WHO Grade III vs IV patient groups, and 6 features' changes were found to be linearly correlated with OS. Using 5 selected features, 9 patients' survival time could be accurately predicted (Mean absolute error = 1.47 months, RMSE = 2.10 months).

CONCLUSION

The results of this work demonstrate the potential of combined radiomics analysis and functional MR imaging in quantitatively identifying early treatment response of concurrent SRS/BVZ.

Development of a Computerized 4-D MRI Phantom for Liver Motion Study

Purpose:

To develop a 4-dimensional computerized magnetic resonance imaging phantom with image textures extracted from real patient scans for liver motion studies.

Methods:

The proposed phantom was developed based on the current version of 4-dimensional extended cardiac-torso computerized phantom and a clinical magnetic resonance scan. Initially, the extended cardiac-torso phantom was voxelized in abdominal–chest region at the end of exhalation phase. Structures/tissues were classified into 4 categories: (1) Seven key textured organs, including liver, gallbladder, spleen, stomach, heart, kidneys, and pancreas, were mapped from a clinical T1-weighted liver magnetic resonance scan using deformable registration. (2) Large textured soft tissue volumes were simulated via an iterative pattern generation method using the same magnetic resonance scan. (3) Lung and intestine structures were generated by assigning uniform intensity with proper noise modeling. (4) Bony structures were generated by assigning the magnetic resonance values. A spherical hypointensity tumor was inserted into the liver. Other respiratory phases of the 4-dimensional phantom were generated using the backward deformation vector fields exported by the extended cardiac-torso program, except that bony structures were generated separately for each phase. A weighted image filtering process was utilized to improve the overall tissue smoothness at each phase.

Results:

Three 4-dimensional series with different motion amplitudes were generated. The developed motion phantom produced good illustrations of abdominal–chest region with anatomical structures in key organs and texture patterns in large soft tissue volumes. In a standard series, the tumor volume was measured as 13.90 ± 0.11 cm³ in a respiratory cycle and the tumor’s maximum center-of-mass shift was 2.95 cm/1.84 cm on superior–inferior/anterior–posterior directions. The organ motion during the respiratory cycle was well rendered. The developed motion phantom has the flexibility of motion pattern variation, organ geometry variation, and tumor modeling variation.

Conclusions:

A 4-D computerized phantom was developed and could be used to produce image series with synthetic magnetic resonance textures for magnetic resonance imaging research of liver motion.

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI

PURPOSE

To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI.

METHODS

A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUCFSD) and the maximum enhancement (MEFSD) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K(trans) map and area under MR intensity enhancement curve AUCMR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d1 and d2 of K(trans) and AUCMR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day.

RESULTS

The tumor mean K(trans) and its heterogeneity measurements d1 and d2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUCFSD and MEFSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUCFSD and MEFSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d1 and d2 of K(trans) map were 87.5% and 100% at first and second post-treatment scan days, respectively.

CONCLUSIONS

As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response.

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study

This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into treatment/control groups (n  =  8/group) and received bevacizumab/saline twice weekly (Day1/Day4/Day8). All mice were scanned at one pre- (Day0) and two post-treatment (Day2/Day9) time points using a high spatiotemporal resolution DCE-MRI pulse sequence. The CA extravasation rate constant [Formula: see text] from the Tofts/SS model and the mean intracellular water residence time [Formula: see text] from the SS model were analyzed. A biological subvolume (BV) within the tumor was identified based on the [Formula: see text] intensity distribution, and the SS model parameters within the BV ([Formula: see text] and [Formula: see text]) were analyzed. It is found that [Formula: see text] and [Formula: see text] have a similar spatial distribution in the tumor volume. The Bayesian information criterion results show that the SS model was a better fit for all scans. At Day9, the treatment group had significantly higher tumor mean [Formula: see text] (p  =  0.021), [Formula: see text] (p  =  0.021) and [Formula: see text] (p  = 0.045). When BV from transcytolemmal water exchange analysis was adopted, the treatment group had higher mean [Formula: see text] at both Day2 (p  =  0.038) and Day9 (p  =  0.007). Additionally, at Day9, the treatment group had higher mean [Formula: see text] (p  =  0.045) and higher [Formula: see text] spatial heterogeneity indices (Rényi dimensions) d 1 (p  = 0.010) and d 2 (p  = 0.021). When mean [Formula: see text] and its coefficient of variation (CV) were used to separate treatment/control group samples using supporting vector machine, the accuracy of treatment/control classification was 68.8% at Day2 and 87.5% at Day9; in contrast, the Day2/Day9 accuracy were 62.5%/87.5% using tumor mean [Formula: see text] and its CV and were 50.0%/87.5% using tumor mean [Formula: see text] and its CV, respectively. These results suggest that the SS model parameters outperformed the Tofts model parameters in terms of capturing bevacizumab therapeutic effect in this longitudinal experiment.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

PURPOSE

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K^trans in the extended Tofts model and blood flow F_B and blood volume V_B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated.

RESULTS

The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data.

CONCLUSION

With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.