Purpose/Objective(s) Brain metastases (BM) detection and segmentation can be difficult and time-consuming during stereotactic radiosurgery (SRS) planning. This work aims to develop deep learning models to automatically detect and segment BM on MRI. Materials/Methods Two volumetric deep convolutional neural network (V-net) models, general (GM) and small lesion models (SM), were developed using T1-weighted gadolinium contrast enhanced and T2-weighted FLAIR MRIs along with ground truth contours of all BMs collected for 53 patients from our own institution. The GM was developed by transfer learning based on a model previously trained with multi-institutional datasets. The SM was a new model presently trained using images slices with small lesions (volumes < 1 cc). The training sets for both models were the data from 43 patients, a total of 5,204 image sets extracted with three different patch sizes (16 × 16 × 16, 32 × 32 × 32, and 64 × 64 × 64). The obtained models were evaluated using a test set of 10 patients in terms of the Dice similarity coefficient (DSC), sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results For the GM, the voxel-level segmentation AUCs were 0.90 +/- 0.10, 0.88 +/- 0.10, and 0.87 +/- 0.11 for patch sizes of 16 × 16 × 16, 32 × 32 × 32, and 64 × 64 × 64, respectively, and the detection sensitivity and specificity were 0.86 and 0.94, respectively, for all test datasets (all lesions). For the test data excluding small lesions, the sensitivity and specificity became 0.93 and 0.95, respectively. For SM, a sensitivity of 0.76 and a specificity of 0.91 were achieved for all small lesions in the test datasets. The DSC was 0.92 for GM for all lesions and was 0.82 for SM for all the small BMs. Conclusion The two deep learning models, SM and GM, can automatically detect and segment brain metastases with reasonable accuracy, sensitivity, and specificity on the MRIs commonly used for SRS planning. For BMs with volumes > 1 cc, the GM achieved clinically relevant results. With further development using larger datasets, the models can be implemented to improve the clinical practice of SRS planning.

Purpose/Objective(s)We have previously reported that the low energy mono-energetic images (MEI) derived from dual-energy CT (DECT) can amplify the detection of radiation-induced changes in tissue. This work aims to investigate the use of low energy MEI derived from daily DECT to assess tumor response during radiation therapy (RT) of early-stage breast cancer.Materials/MethodsDaily DECT data acquired during routine CT-guided RT delivery for 35 early-stage breast cancer patients enrolled in an institutional prospective pre-operative accelerated partial breast irradiation trial, along with tumor pathological data collected from the biopsy before RT and the excised tissue from the surgery post RT, were analyzed. All patients were treated with 30 Gy in 5 fractions in two weeks. The DECTs were acquired immediately before the fractional dose delivery using an in-room CT scanner equipped with a sequential DE protocol. MEI images across a range of x-ray energies were reconstructed using an image-based material decomposition. Fractional changes in mean CT number (mCTN) in gross tumor volume (GTV) on MEIs were extracted and the changes from the first to the last fraction were correlated to the tumor cellularity changes from the pathological data collected before and after RT using the Student's T-Test. As a control, the mCTN changes on MEIs in a region far from irradiated volumes were calculated. The results from the MEI data were compared to those obtained from the standard 120 kVp CTs of the same subjects.ResultsThe data from 20 out of 35 patients were found to be suitable for this analysis. The greatest enhancement in GTV mCTN change was observed for MEI at 40 keV, the lowest available energy. The average change in GTV mCTN between the first and last fractions of treatments was 2.9 times larger for 40 keV MEIs than that for the standard 120 kVp CTs. For 11 out of the 20 patients, the GTV mCTN changes were greater than 15 HU on the 40 keV MEI and their average change in cellularity was 36.5 ± 10.3%. For the remaining 9 patients, the GTV mCTN changes were less than 15 HU on the 40 keV MEI and the average change in cellularity was 14.4 ± 9.8%. The correlation between change in GTV mCTN and change in cellularity was found to be significant (p=0.02). The average mCTN change in the control region was of 0.2 ± 2.0 HU on the 40 keV for all 20 patients.ConclusionTumor pathological response for early-stage breast cancer treated with pre-operative accelerated partial breast irradiation can be predicted by the change of quantitative features, such as mean CT number, on low energy MEI derived from DECT acquired during the radiation treatment. Compared to the standard CT, the low energy MEIs substantially amplify the changes of mCTN, enhancing the viability of using quantitative CT features as a biomarker for early RT response assessment.

The aCORN experiment measures the neutron-decay electron-antineutrino correlation (a coefficient) using a novel method based on an asymmetry in proton time-of-flight for events where the beta electron and recoil proton are detected in delayed coincidence. We report the data analysis and result from the second run at the NIST Center for Neutron Research, using the high-flux cold neutron beam on the new NG-C neutron guide end position: a = -0.10758 +/- 0.00136(stat) +/- 0.00148(sys). This is consistent within uncertainties with the result from the first aCORN run on the NG-6 cold neutron beam. Combining the two aCORN runs, we obtain a = -0.10782 +/- 0.00124(stat) +/- 0.00133(sys), which has an overall relative standard uncertainty of 1.7%. The corresponding result for the ratio of weak coupling constants lambda = G(A)/G(V) is lambda = -1.2796 +/- 0.0062.

Lung PBV maps derived from equilibrium contrast enhanced DECT acquired for radiation therapy planning may provide a valuable new tool to assess the spatial distribution of normal lung function, alternative to the lung ventilation map. With additional study the PBV maps may be used to guide radiation treatment planning for lung cancer patients.

Pancreatic cancer is expected to become the 2nd leading cause of cancer-related death in the United States by 2020. Discovering reliable and accessible biomarkers for the prediction of treatment response would aid clinicians in choosing the best course of treatment. Dual-source dual energy CT (DECT) is an emerging technology which enables the derivation of Iodine density maps when a patient is simulated with contrast. From these Iodine maps and the hematocrit measured on the day of simulation, it is possible to calculate the extracellular volume (ECV) fraction, which has been correlated with patient outcome. We seek to find a correlation between the ECV fraction and change in Carbohydrate Antigen (CA) 19-9 as a surrogate for treatment response. DECT study sets acquired in the standard CT simulation on a dual source simulator for 12 pancreatic cancer patients at the late arterial contrast phase were used. Each patient was to receive a dose of 50.4 Gy in 28 fractions. The study patients were chosen based on the presence of a solid tumor in the pancreas which could be clearly delineated on the imaging sets. A region of interest was placed in the tumor and in the aorta. From the ratio of the I density calculated from the DECT in the ROI and the hematocrit taken at the time of simulation, the ECV fraction was calculated. The ECV fraction was then compared to the change in CA19-9 as calculated from a few days before and a few days after chemoradiation therapy. Distant metastases as the cause of CA 19-9 elevation was ruled out on subsequent restaging prior to surgery. The average hematocrit, ECV fraction, and change in Ca19-9 during treatment for the 12 patients was 35.9 ± 5.4%, 37.29 ± 17.3% and -5.625 ± 32.0 respectively. We found a linear correlation between the ECV fraction and the change in CA19-9 with an R2 of 0.94: ΔCA19-9 = 1.7932 × ECVfraction – 72.487 Thus, with increased ECV fraction in the tumor, the CA19-9 concentration also increased during treatment. Similarly, a reduction in CA19-9 during treatment correlated with a smaller ECV fraction. It has been shown that with a neoadjuvant treatment approach, failure to normalize CA19-9 prior to surgery is associated with reduced survival. Hence for the 12 patients, a reduced ECV fraction is correlated with treatment resistance. Therefore, ECV fraction, as calculated from Iodine maps derived from dual source DECT, may be useful as a predictor of treatment response for pancreatic cancer patients as measured by a change in the CA19-9.

PURPOSE:We present the advantages of using dual-energy CT (DECT) for radiation therapy (RT) planning based on our clinical experience.METHODS:DECT data acquired for 20 representative patients of different tumor sites and/or clinical situations with dual-source simultaneous scanning (Drive, Siemens) and single-source sequential scanning (Definition, Siemens) using 80 and 140-kVp X-ray beams were analyzed. The data were used to derive iodine maps, fat maps, and mono-energetic images (MEIs) from 40 to 190 keV to exploit the energy dependence of X-ray attenuation. The advantages of using these DECT-derived images for RT planning were investigated.RESULTS:When comparing 40 keV MEIs to conventional 120-kVp CT, soft tissue contrast between the duodenum and pancreatic head was enhanced by a factor of 2.8. For a cholangiocarcinoma patient, contrast between tumor and surrounding tissue was increased by 96 HU and contrast-to-noise ratio was increased by up to 60% for 40 keV MEIs compared to conventional CT. Simultaneous dual-source DECT also preserved spatial resolution in comparison to sequential DECT as evidenced by the identification of vasculature in a pancreas patient. Volume of artifacts for five patients with titanium implants was reduced by over 95% for 190 keV MEIs compared to 120-kVp CT images. A 367-cm3 region of photon starvation was identified by low CT numbers in the soft tissue of a mantle patient in a conventional CT scan but was eliminated in a 190 keV MEI. Fat maps enhanced image contrast as demonstrated by a meningioma patient.CONCLUSION:The use of DECT for RT simulation offers clinically meaningful advantages through improved simulation workflow and enhanced structure delineation for RT planning.

The delineation of targets for CT based radiation treatment (RT) planning frequently utilizes contrast enhancement via Iodine bolus. However, dose calculations should not be performed on a CT with a contrast agent because the patient won’t receive bolus during treatment. Hence it is typical to acquire CT twice during RT simulation: once before the injection of bolus and once after. The registration between these two CT sets can introduce error, particularly for abdominal and thoracic tumor sites, due to the motion between the two acquisitions. In this work, we investigate the feasibility of using virtual non-contrast (VNC) images derived from dual-energy CT (DECT) to eliminate the pre-contrast CT and the registration error. CT datasets, including DECT and conventional 120 kVp pre- and post-contrast CTs, acquired for 10 pancreatic cancer patients were used. The DECT sets were acquired simultaneously using a dual source CT simulator. For each case, a VNC was derived from the DECT and was registered to the pre-contrast CT. The gross tumor volume (GTV) and organs at risk (OAR) were delineated on the contrast CT and then populated to the pre-contrast CT. An IMRT plan of 50.4 Gy to the GTV in 28 fractions was calculated on the pre-contrast CT. The dose distribution was reconstructed on the VNC image. Dose volume parameters (DVP) of the original plan on the pre-contrast CT were compared to reconstructed plan on the VNC. The motion related differences between the pre- and post-contrast CTs were assessed in the target region. The CT number in the bone of the VNC sets dropped an average of 65 HU in comparison to the pre-contrast CTs. On average, the distance between the centroids of the duodenum differs by an average of 6.7 ± 4.0 mm and as much as 13.3 mm as measured on the pre- and post-contrast CTs. The dose distributions on the pre-contrast CT and VNC are almost identical. The GTV mean dose and GTV maximum dose differ by 0.1% and 0.2% respectively between the two plans for the cases analyzed. For organs at risk the maximum point dose difference is 0.3% and 0.5% for the duodenum and cord respectively; the liver mean dose differs by 0.1%; the kidney V15Gy differs by 1.0%. The VNC images derived from DECT can be used to replace the conventional pre-contrast CT for RT planning, eliminating the need for a pre-contrast CT scan and subsequently eliminating the error in the registration of pre- and post-contrast CT. The use of VNC improves clinical workflow and reduces imaging dose to the patient.

Radiation can induce changes in CT textures during radiation therapy (RT) and the changes can potentially be used to assess RT response for breast cancer. These changes will be amplified if the X-Ray energy is reduced due to the rapid growth of the photoelectric effect below 100 keV. The purpose of this study is to investigate the use of low energy mono-energetic images (MEI) obtained from dual energy (DE) CT to enhance the detection of radiation induced changes during RT delivery. DECT data was analyzed for 10 breast cancer patients treated with 30 Gy in 5 fractions delivered on non-consecutive days on a phase 2 study of pre-operative accelerated partial breast irradiation (APBI) during routine CT-guided RT delivery using an in-room CT scanner equipped with a sequential DE protocol. Conventional CT (120 kVp) and DECT (with a sequential protocol) were acquired in alternate days. MEI images across a range of energies were reconstructed using an image-based material decomposition. Changes in quantitative features including mean CT number (mCTN) and first order textures (skewness, kurtosis, entropy, variance) were extracted. The results from the MEI data were compared to those obtained from the standard 120 kVp CTs of the same subjects. Correlation between the changes of selected CT features and the cellularity data obtained from the pathologic analysis of the surgical specimen after the APBI was assessed. Radiation can induce substantial changes in the textures of the daily CTs. As measured from MEI, mCTN and entropy can be changed by up to 35 HU and 17%, respectively, during RT delivery. Compared to those based on the conventional CT, these changes were amplified by the low energy MEI. The average change in mCTN between the first and last week of treatment was 2.0 times larger for 40 keV MEI than that for average change in mCTN for the 120 kVp CTs. For a representative patient the kurtosis decreased by a factor of 4.6 during RT delivery as measured on 40 keV MEI, 3.8 times larger than that from 120 kVp CTs. The excised tissue of patients that experienced a change in mCTN from 40 keV MEI of greater than 10 HU had an average cellularity of 2.8 ± 2.4% whereas for those for whom the change in mCTN was less than 10 had an average cellularity of 42.5 ± 22%. A significant correlation was observed between change in mCTN at 40 keV and low (≤10%) cellularity of excised tissue using the Student’s t-Test (p=0.007). Using the same test the correlation between change in mCTN at 120 kVp and low cellularity of excised tissue was not found to be significant (p=0.055). Radiation-induced changes in CT textures during RT delivery for breast cancer are amplified if they are measured from MEI of DECT, enhancing the viability of using CT texture changes as a biomarker for early RT response assessment while patients are receiving treatment.

We have previously reported on the feasibility of using radiation-induced CT texture changes (delta radiomics) measured from daily CTs during radiotherapy (RT) as an imaging biomarker for RT response in various tumors. This study aims to evaluate time stability in texture features over a timescale similar to the duration of conventional RT delivery. CT data sets of a 3-D printed anatomically-informed texture phantom containing three modules (liver, lung and uniform with low-contrast lesions) and the homogeneous module of a quality assurance phantom were acquired at regular intervals over the course of an 8 week period, to simulate the timescale of conventional RT duration. A CT scanner installed in the RT room and our institution’s standard abdominal protocol (120 kVp, 126 mAs, 0.6 pitch, 1.2mm focal-spot size, 17 mGy CTDIvol, 3mm slice thickness, 0.988/0.988mm pixel spacing and B30f reconstruction kernel) were used. In each phantom module, 12 regions of interest (ROI) each with a volume of 8 cm3 were selected and 60 texture features including histogram, GLCM and GLRM features, were extracted using an in-house program. Each ROI was transferred to all images in the module series using rigid registration of the images. The time stability of each feature was evaluated by fitting the longitudinal datasets using linear mixed effects models. A 95% confidence interval (CI) was established to quantify the expected variation over a treatment timescale for each of the 60 features evaluated in each phantom. These results were compared with delta radiomics of first order textural features in pancreatic cancer RT patient data previously reported. It will also be used to evaluate the utility and practicality of higher-order textural feature analysis using current pancreatic cancer RT patient data. In the homogeneous module, the 95% CI for the variation in mean CT number (MCTN) is ±1.0 HU, i.e. variation in MCTN greater than 1 HU is significant at the p≤0.05 level. The expected variation in MCTN increased with increasing complexity of the phantom (1.5 HU, 2.1 HU and 8.3 HU for the low contrast, liver and lung modules respectively) which is primarily a result of the partial volume effect, due to the uncertainty in the position of the z -slices. In the homogeneous phantom, skewness varied by ±0.12 and kurtosis varied by ±0.25. These values confirm that the previously reported values of change in MCTN, skewness and kurtosis in patient daily CTs for pancreatic cancer of 4.7±4.5 HU, +0.12 and -0.39, respectively, are significant at the p≤0.05 level, with respect to the expected variation. Machine-related uncertainty in CT texture is predictable. CT texture measurements of anatomically-informed texture phantoms are highly repeatable and stable. This indicates that previously reported radiation-induced changes in patients’ CT texture features are outside the range of expected variation, suggesting their potential for early assessment of treatment response.

We describe an apparatus used to measure the electron-antineutrino angular correlation coefficient in free neutron decay. The apparatus employs a novel measurement technique in which the angular correlation is converted into a proton time-of-flight asymmetry that is counted directly, avoiding the need for proton spectroscopy. Details of the method, apparatus, detectors, data acquisition, and data reduction scheme are presented, along with a discussion of the important systematic effects.

Abstract Background and purpose CT scan protocols are often created with imaging parameters set to minimize imaging dose with acceptable image quality for diagnostic purpose. This study aimed to optimize CT imaging parameters to help accurately delineate structures for radiation therapy planning and delivery guidance. Materials and methods Imaging parameters were optimized with CT data acquired for a phantom to create image quality enhancement (IQE) protocols, which were subsequently used to scan a prostate and a pancreatic cancer patient who underwent image-guided radiotherapy (IGRT). The patient images were compared with those scanned with standard clinical protocols, the quality of these images was assessed with various methods (survey, inter- and intra-observer variations, and dice coefficient analysis) for the two patient cases. Results An effective tube current–time product of   ∼1000 mAs was found to be a reasonable choice to balance CT quality and CT dose. With increased dose and penetration taken into account, 100 and 120 kV tube voltages were found appropriate for the IQE protocols. The inter- and intra-observer variations for the IQE data were smaller than those with the standard protocols. Dice coefficient analysis indicated that the IQE protocols lead to improved dice coefficient by as much as 8 percentage points for the two cases studied. Conclusion CT image quality can be improved with the IQE protocols created in this study, to provide better soft tissue contrast, which would be beneficial for use in radiation therapy, e.g., for planning data acquisition or for IGRT for hypo-fractionated treatments.