The resonant frequencies of a microstrip patch antenna are dependent on the dielectric constant of its substrate and the physical dimensions of its radiation patch. Both of these parameters are ...temperature-dependent. In this paper, we investigated the effects of temperature on the antenna resonant frequencies for the purpose of studying the microstrip patch antenna as a temperature sensor. First, the relationship between the antenna resonant frequency shift and the temperature change is derived based on the transmission line model. To validate the theoretical prediction, antenna sensors bonded on different metal bases were tested in a temperature chamber. By comparing the measured temperature-frequency relationship with the theoretical predictions, we discovered that the dielectric constant of the substrate is not only dependent on temperature but also influenced by the base material. After calibrating the thermal coefficient of the substrate dielectric constant using the measurement data, the differences between the measurements and the theoretical predictions were within the expected systematic error of the reference thermocouple, validating that a microstrip patch antenna can serve as a temperature sensor.
•A very low rate of 3 ± 3 FPs per patient was maintained for all brain metastasis sizes.•Automated segmentation performance for brain metastases depends on metastasis size.•For metastases ≥6 mm, DSC ...was 87%, sensitivity was 99%, and PPV was 67%.•For metastases ≥3 mm and <6 mm, DSC was 64%, sensitivity was 87%, and PPV was 49%.•For metastases <3 mm, DSC was 17%, sensitivity was 25%, and PPV was 28%.
Brain metastases are manually contoured during stereotactic radiosurgery (SRS) treatment planning, which is time-consuming, potentially challenging, and laborious. The purpose of this study was to develop and investigate a 2-stage deep learning (DL) approach (MetNet) for brain metastasis segmentation in pre-treatment magnetic resonance imaging (MRI).
We retrospectively analyzed postcontrast 3D T1-weighted spoiled gradient echo MRIs from 934 patients who underwent SRS between August 2009 and August 2018. Neuroradiologists manually identified brain metastases in the MRIs. The treating radiation oncologist or physicist contoured the brain metastases. We constructed a 2-stage DL ensemble consisting of detection and segmentation models to segment the brain metastases on the MRIs. We evaluated the performance of MetNet by computing sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC) with respect to metastasis size, as well as free-response receiver operating characteristics.
The 934 patients (mean ±standard deviation age 59 ± 13 years, 474 women) were randomly split into 80% training and 20% testing groups (748:186). For patients with metastases 1–52 mm (n = 766), 648 (85%) were detected and segmented with a mean segmentation DSC of 81% ± 15%. Patient-averaged sensitivity was 88% ± 19%, PPV was 58% ± 25%, and DSC was 85% ± 13% with 3 ± 3 false positives (FPs) per patient. When considering only metastases ≥6 mm, patient-averaged sensitivity was 99% ± 5%, PPV was 67% ± 28%, and DSC was 87% ± 13% with 1 ± 2 FPs per patient.
MetNet can segment brain metastases across a broad range of metastasis sizes with high sensitivity, low FPs, and high segmentation accuracy in postcontrast T1-weighted MRI, potentially aiding treatment planning for SRS.
Purpose
To develop and evaluate a sliding‐window convolutional neural network (CNN) for radioactive seed identification in MRI of the prostate after permanent implant brachytherapy.
Methods
...Sixty‐eight patients underwent prostate cancer low‐dose‐rate (LDR) brachytherapy using radioactive seeds stranded with positive contrast MR‐signal seed markers and were scanned using a balanced steady‐state free precession pulse sequence with and without an endorectal coil (ERC). A sliding‐window CNN algorithm (SeedNet) was developed to scan the prostate images using 3D sub‐windows and to identify the implanted radioactive seeds. The algorithm was trained on sub‐windows extracted from 18 patient images. Seed detection performance was evaluated by computing precision, recall, F1‐score, false discovery rate, and false–negative rate. Seed localization performance was evaluated by computing the RMS error (RMSE) between the manually identified and algorithm‐inferred seed locations. SeedNet was implemented into a clinical software package and evaluated on sub‐windows extracted from 40 test patients.
Results
SeedNet achieved 97.6 ± 2.2% recall and 97.2 ± 1.9% precision for radioactive seed detection and 0.19 ± 0.04 mm RMSE for seed localization in the images acquired with an ERC. Without the ERC, the recall remained high, but the false–positive rate increased; the RMSE of the seed locations increased marginally. The clinical integration of SeedNet slightly increased the run‐time, but the overall run‐time was still low.
Conclusion
SeedNet can be used to perform automated radioactive seed identification in prostate MRI after LDR brachytherapy. Image quality improvement through pulse sequence optimization is expected to improve SeedNet’s performance when imaging without an ERC.
To investigate machine segmentation of pelvic anatomy in magnetic resonance imaging (MRI)-assisted radiosurgery (MARS) for prostate cancer using prostate brachytherapy MRIs acquired with different ...pulse sequences and image contrasts.
Two hundred 3-dimensional (3D) preimplant and postimplant prostate brachytherapy MRI scans were acquired with a T2-weighted sequence, a T2/T1-weighted sequence, or a T1-weighted sequence. One hundred twenty deep machine learning models were trained to segment the prostate, seminal vesicles, external urinary sphincter, rectum, and bladder using the MRI scans acquired with T2-weighted and T2/T1-weighted image contrast. The deep machine learning models consisted of 18 fully convolutional networks (FCNs) with different convolutional encoders. Both 2-dimensional and 3D U-Net FCNs were constructed for comparison. Six objective functions were investigated: cross-entropy, Jaccard distance, focal loss, and 3 variations of Tversky distance. The performance of the models was compared using similarity metrics, including pixel accuracy, Jaccard index, Dice similarity coefficient (DSC), 95% Hausdorff distance, relative volume difference, Matthews correlation coefficient, precision, recall, and average symmetrical surface distance. We selected the highest-performing architecture and investigated how the amount of training data, use of skip connections, and data augmentation affected segmentation performance. In addition, we investigated whether segmentation on T1-weighted MRI was possible with FCNs trained on only T2-weighted and T2/T1-weighted image contrast.
Overall, an FCN with a DenseNet201 encoder trained via cross-entropy minimization yielded the highest combined segmentation performance. For the 53 3D test MRI scans acquired with T2-weighted or T2/T1-weighted image contrast, the DSCs of the prostate, external urinary sphincter, seminal vesicles, rectum, and bladder were 0.90 ± 0.04, 0.70 ± 0.15, 0.80 ± 0.12, 0.91 ± 0.06, and 0.96 ± 0.04, respectively, after model fine-tuning. For the 5 T1-weighted images, the DSCs of these organs were 0.82 ± 0.07, 0.17 ± 0.15, 0.46 ± 0.21, 0.87 ± 0.06, and 0.88 ± 0.05, respectively.
Machine segmentation of the prostate and surrounding anatomy on 3D MRIs acquired with different pulse sequences for MARS low-dose-rate prostate brachytherapy is possible with a single FCN.
Magnetic resonance imaging (MRI) can facilitate accurate organ delineation and optimal dose distributions in high-dose-rate (HDR) MRI-Assisted Radiosurgery (MARS). Its use for this purpose has been ...limited by the lack of positive-contrast MRI markers that can clearly delineate the lumen of the HDR applicator and precisely show the path of the HDR source on T1- and T2-weighted MRI sequences. We investigated a novel MRI positive-contrast HDR brachytherapy or interventional radiotherapy line marker, C4:S, consisting of C4 (visible on T1-weighted images) complexed with saline. Longitudinal relaxation time (T1) and transverse relaxation time (T2) for C4:S were measured on a 1.5 T MRI scanner. High-density polyethylene (HDPE) tubing filled with C4:S as an HDR brachytherapy line marker was tested for visibility on T1- and T2-weighted MRI sequences in a tissue-equivalent female ultrasound training pelvis phantom. Relaxivity measurements indicated that C4:S solution had good T1-weighted contrast (relative to oil fat signal intensity) and good T2-weighted contrast (relative to water signal intensity) at both room temperature (relaxivity ratio > 1; r2/r1 = 1.43) and body temperature (relaxivity ratio > 1; r2/r1 = 1.38). These measurements were verified by the positive visualization of the C4:S (C4/saline 50:50) HDPE tube HDR brachytherapy line marker on both T1- and T2-weighted MRI sequences. Orientation did not affect the relaxivity of the C4:S contrast solution. C4:S encapsulated in HDPE tubing can be visualized as a positive line marker on both T1- and T2-weighted MRI sequences. MRI-guided HDR planning may be possible with these novel line markers for HDR MARS for several types of cancer.
•This is the largest series of patients with localized prostate cancer treated with proton therapy (PT).•Long-term outcomes after PT ± ADT (androgen deprivation therapy) were excellent across all ...risk groups, particularly for patients with high-risk or very high-risk prostate cancer.•While the role of pelvic lymph node irradiation remains controversial, the clinical outcomes in this study were achieved with infrequent pelvic lymph node irradiation (1%).
Proton therapy (PT) has emerged as a standard-of-care treatment option for localized prostate cancer at our comprehensive cancer center. However, there are few large-scale analyses examining the long-term clinical outcomes. Therefore, this article aims to evaluate the long-term effectiveness and toxicity of PT in patients with localized prostate cancer.
Review of 2772 patients treated from May 2006 through January 2020. Disease risk was stratified according to National Comprehensive Cancer Network guidelines as low LR, n = 640; favorable-intermediate F-IR, n = 850; unfavorable-intermediate U-IR, n = 851; high HR, n = 315; or very high VHR, n = 116. Biochemical failure and toxicity were analyzed using Kaplan-Meier estimates and multivariate models.
The median patient age was 66 years; the median follow-up time was 7.0 years. Pelvic lymph node irradiation was prescribed to 28 patients (1%) (2 0.2% U-IR, 11 3.5% HR, and 15 12.9% VHR). The median dose was 78 Gy in 1.8–2.0 Gy(RBE) fractions. Freedom from biochemical relapse (FFBR) rates at 5 years and 10 years were 98.2% and 96.8% for the LR group; 98.3% and 93.6%, F-IR; 94.2% and 90.2%, U-IR; 94.3% and 85.2%, HR; and 86.1% and 68.5%, VHR. Two patients died of prostate cancer. Overall rates of late grade ≥ 3 GU and GI toxicity were 0.87% and 1.01%.
Proton therapy for localized prostate cancer demonstrated excellent clinical outcomes in this large cohort, even among higher-risk groups with historically poor outcomes despite aggressive therapy.
•High interobserver variability (IoV) was observed at junctions between the prostate and one or more organs at risk.•Radiation oncologists were the most consistent group of observers, but high IoV ...was still observed in this group.•IoV demonstrated a dependence on organ size, and was highest for the external urinary sphincter.•High IoV was observed on both treatment planning MRIs and postimplant quality assessment MRIs.•Dose-volume-histogram parameters for MRI-based prostate radiotherapy are heavily influenced by IoV.
Quantifying the interobserver variability (IoV) of prostate and periprostatic anatomy delineation on prostate MRI is necessary to inform its use for treatment planning, treatment delivery, and treatment quality assessment.
Twenty five prostate cancer patients underwent MRI-based low-dose-rate prostate brachytherapy (LDRPBT). The patients were scanned with a 3D T2-weighted sequence for treatment planning and a 3D T2/T1-weighted sequence for quality assessment. Seven observers involved with the LDRPBT workflow delineated the prostate, external urinary sphincter (EUS), seminal vesicles, rectum, and bladder on all 50 MRIs. IoV was assessed by measuring contour similarity metrics, differences in organ volumes, and differences in dosimetry parameters between unique observer pairs. Measurements from a group of 3 radiation oncologists (G1) were compared against those from a group consisting of the other 4 clinical observers (G2).
IoV of the prostate was lower for G1 than G2 (Matthew’s correlation coefficient MCC, G1 vs. G2: planning–0.906 vs. 0.870, p < 0.001; postimplant–0.899 vs. 0.861, p < 0.001). IoV of the EUS was highest of all the organs for both groups, but was lower for G1 (MCC, G1 vs. G2: planning–0.659 vs. 0.402, p < 0.001; postimplant–0.684 vs. 0.398, p < 0.001). Large differences in prostate dosimetry parameters were observed (G1 maximum absolute prostate ΔD90: planning–76.223 Gy, postimplant–36.545 Gy; G1 maximum absolute prostate ΔV100: planning–13.927%, postimplant–8.860%).
While MRI is optimal in the management of prostate cancer with radiation therapy, significant interobserver variability of the prostate and external urinary sphincter still exist.
Purpose
Perfusion MRI with gadolinium‐based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast ...enhanced (DCE) MRI are two gadolinium‐based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium‐based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors.
Methods
One hundred nine brain‐tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor–to–white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared.
Results
Pearson correlation analysis showed that both the tumor rCBV and tumor–to–white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland‐Altman analysis showed a mean difference between the synthetic and real rCBV tumor–to–white matter ratios of 0.20 with a 95% confidence interval of ±0.47.
Conclusion
Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain‐tumor perfusion imaging of DSC and DCE parameters with a single gadolinium‐based contrast agent administration.
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