Skin dose investigations on a 0.5 T parallel rotating biplanar linac-MR using Monte Carlo simulations and measurements.

BACKGROUND: The Alberta rotating biplanar linac-MR has a 0.5 T magnetic field parallel to the beamline. When developing a new linac-MR system, interactions of charged particles with the magnetic field necessitate careful consideration of skin dose and tissue interface effects. PURPOSE: To investigate the effect of the magnetic field on skin dose using measurements and Monte Carlo (MC) simulations. METHODS: We develop an MC model of our linac-MR, which we validate by comparison with ion chamber measurements in a water tank. Additionally, MC simulation results are compared with radiochromic film surface dose measurements on solid water. Variations in surface dose as a function of field size are measured using a parallel plate ion chamber in solid water. Using an anthropomorphic computational phantom with a 2 mm-thick skin layer, we investigate dose distributions resulting from three beam arrangements. Magnetic field on and off scenarios are considered for all measurements and simulations. RESULTS: For a 20 × 20 cm2 field size, D 0.2 c c ${D_{0.2cc}}$ (the minimum dose to the hottest contiguous 0.2 cc volume) for the top 2 mm of a simple water phantom is 72% when the magnetic field is on, compared to 34% with magnetic field off (values are normalized to the central axis dose maximum). Parallel plate ion chamber measurements demonstrate that the relative increase in surface dose due to the magnetic field decreases with increasing field size. For the anthropomorphic phantom, D ∼ 0.2 c c ${D_{ \sim 0.2cc}}$ (minimum skin dose in the hottest 1 × 1 × 1 cm3 cube) shows relative increases of 20%-28% when the magnetic field is on compared to when it is off. With magnetic field off, skin D ∼ 0.2 c c ${D_{ \sim 0.2cc}}$ is 71%, 56%, and 21% for medial-lateral tangents, anterior-posterior beams, and a five-field arrangement, respectively. For magnetic field on, the corresponding skin D ∼ 0.2 c c ${D_{ \sim 0.2cc}}$ values are 91%, 67%, and 25%. CONCLUSIONS: Using a validated MC model of our linac-MR, surface doses are calculated in various scenarios. MC-calculated skin dose varies depending on field sizes, obliquity, and the number of beams. In general, the parallel linac-MR arrangement results in skin dose enhancement due to charged particles spiraling along magnetic field lines, which impedes lateral motion away from the central axis. Nonetheless, considering the results presented herein, treatment plans can be designed to minimize skin dose by, for example, avoiding oblique beams and using a larger number of fields.

Prostate size, source configuration, and dosimetry dynamics of stranded 125I seed implants.

PURPOSE: To quantify changes in prostate size and seed movement over time after transperineal implantation of stranded 125I seeds, and to determine their impact on prostate dosimetry. METHODS: CT and MR (T2, balanced steady-state free precession) image triplets were acquired on days 0, 3, 10, and 30 for a cohort of 20 patients and registered automatically. Prostate contours were drawn on MR-T2 images; seeds were found and matched in successive CT images. Prostate volume and dimensions, seed movements, and prostate dose metrics V200, V150, V100 and D90 were calculated, and their dynamic behaviors quantified in an operationally defined prostate coordinate system. RESULTS: Cohort-averaged reductions in prostate A-P dimension (∼8%) and L-R dimension (∼5%) inferred from seed movements agreed with those obtained from contour measurements, whereas prostate volume and S-I dimension (implant direction) reductions inferred from seed movements were overestimated by about 30%. Average overall seed movement was 4.8 ± 3.0 mm, of which the only identifiable systematic component was resolution of prostate edema. Cohort-averaged ratios of prostate V200, V150, V100, and D90 on day 30 relative to day 0 were 1.67, 1.33, 1.02, and 1.08, respectively. CONCLUSIONS: Postimplant prostate size reduction in the SI (implant) direction cannot reliably be inferred from stranded seed movements. Apart from large-scale migration, residual seed movements relative to the prostate after accounting for edema resolution appear to be random. Prostate V100 and D90 changes 30 days post implant are modest, whereas those for V150 and V200 are substantial.

Early Metabolic Changes in 1H-MRSI Predictive for Survival in Patients With Newly Diagnosed High-grade Glioma.

BACKGROUND: The purpose of this study was to evaluate the association of specific threshold values for changes in metabolic metrics measured from 1H magnetic resonance spectroscopic imaging (MRSI) to survival of patients with high-grade glioma treated with multimodality therapy. PATIENTS AND METHODS: Forty-four patients with newly diagnosed high-grade glioma were prospectively enrolled. Serial MRI and MRSI scans provided measures of tumor choline, creatine, and N-acetylaspartate (NAA). Cox regression analyses adjusted for patient age, KPS, and delivery of concurrent chemotherapy were used to assess the association of changes in metabolic metrics with survival. RESULTS: Median follow-up time for patients at risk was 13.4 years. Overall survival (OS) was longer in patients with ≤20% increase (vs. >20%) in normalized choline (p=0.024) or choline/NAA (p=0.024) from baseline to week 4 of RT. During this period, progression-free survival (PFS) was longer in patients with ≤40% increase (vs. >40%) in normalized choline (p=0.013). Changes in normalized creatine, choline/creatine, and NAA/creatine from baseline to mid-RT were not associated with OS. From baseline to post-RT, changes in metabolic metrics were not associated with OS or PFS. CONCLUSION: Threshold values for serial changes in choline metrics on mid-RT MRSI associated with OS and PFS were identified. Metabolic metrics at post-RT did not predict for these survival endpoints. These findings suggest a potential clinical role for MRSI to provide an early assessment of treatment response and could enable risk-adapted therapy in clinical trial development and clinical practice.

Use of machine learning to select texture features in investigating the effects of axial loading on T2-maps from magnetic resonance imaging of the lumbar discs.

BACKGROUND: Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading. METHODS: Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T2-weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen's d compared loading conditions. FINDINGS: All statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P2-Offset, P4-Offset) and information measure of correlation 1 (P3-Offset, P4-Offset) detected significant changes in the nucleus. Statistically significant changes were also observed for homogeneity (P2-Offset, P3-Offset), contrast (P2-Offset), and difference variance (P4-Offset) of the posterior annulus. INTERPRETATION: MRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.

Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data.

PURPOSE: A rapid real-time 2D accelerated method was developed for magnetic resonance imaging (MRI) using principal component analysis (PCA) in the temporal domain. This method employs a moving window of previous dynamic frames to reconstruct the current, real-time frame within this window. This technique could be particularly useful in real-time tracking applications such as in MR-guided radiotherapy, where low latency real-time reconstructions are essential. METHODS: The method was tested retrospectively on 15 fully-sampled data sets of lung patient data acquired on a 3T Philips Achieva system. High frequency data are incoherently undersampled, while the central low-frequency data are always acquired to characterize the temporal fluctuations through PCA. The undersampling pattern is derived in such a way that all of k-space is acquired within a pre-determined number of frames. The missing data in the current frame are then filled in by fitting the temporal characterizations to the acquired undersampled data, using a pre-determined number of PCs. A subset of six patients was used to test the contour ability of the images. Various accelerations between 3x and 8x were tested along with the optimal number of PCs for fitting. A comparison was also performed with previous work from our group proposed by Dietz et al. as well as with a standard low resolution acquisition. In order to determine how the method would perform at lower signal to noise ratio (SNR), noise levels of 2×, 4×, and 6× were added to the 3T data. Metrics such as normalised mean square error and Dice coefficient were used to measure the reconstruction image quality and contour ability. RESULTS: The proposed method demonstrated good temporal robustness as consistent metrics were detected for the duration of the imaging session. It was found that the optimal number of PCs for temporal fitting was dependent on the acceleration rate. For the data tested, five PCs were found to be optimal at the acceleration rates of 3× and 4×. This number decreases to three at accelerations of 5× and 6× and further decreases to two at an acceleration rate of 8×, likely due to greater instability with fewer acquired data points. The use of too many PCs for fitting increased the chances of noisy reconstruction which affected contourability. CONCLUSIONS: The proposed 2D real-time MR acceleration method demonstrated greater robustness in the metrics over time when compared with previous real-time PCA methods using metrics such as normalised mean squared error, peak SNR and structural similarity up to an acceleration of 8x. Improved temporal robustness of image structure contourability and accurate definition was also demonstrated using several metrics including the Dice coefficient. Reconstruction of raw acquired data can be performed at approximately 50 ms per frame using an Intel core i5 CPU. The method has the advantage of being very flexible in terms of hardware requirements as it can operate successfully on a single coil channel and does not require specialized computing power to implement in real-time.

The effects of axial loading on the morphometric and T2 characteristics of lumbar discs in relation to disc degeneration.

BACKGROUND: Intervertebral disc degeneration affects the morphology, biomechanics and biochemistry of the disc. The study aimed to compare the effects of compression and traction on lumbar discs measurements in relation to degeneration. METHODS: Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain rested supine 15 min before an unloaded T2-mapping MRI, were then loaded 20 min with 50% body weight with imaging during the last 5 min, and then repeated this process under traction. For lumbar discs, height, angle, width, mean-T2, and T2-weighted centroid locations were calculated. A repeated measure ANCOVA and Cohen's d compared loading conditions. Relations between measurement changes between conditions and degeneration assessed by Pfirrmann ratings were examined graphically. FINDINGS: From compression to traction, we observed significant: decrease in L1-2 mean-T2 (Effect size = -0.35); inferior and posterior shift in L4-5 (0.4, 0.14) and L5-S1 (0.25, 0.33) T2-weighted centroid. From unloaded to compression, we observed a significant: increase in L5-S1 width (Effect Size = 0.22); anterior shift in L1-2 T2-weighted centroid (0.39); and L3-4 (mean 2.1°) and L4-5 (1.8°) extension angle. More degeneration was graphically related with larger changes from Compression to Traction (more superior and, anterior position of the T2-weighted centroid, increased height, reduced extension of segmental angle) and from Unloaded to Compression larger changes in inferior displacement of the T2-weighted centroid, decrease in height) but less anterior displacement of the centroid and less change in segmental angles. INTERPRETATION: The largest loading responses were at lower levels, generally with more degeneration. T2-weighted centroid locations, angle and disc height detected the largest loading response.

Texture analysis in the classification of T2 -weighted magnetic resonance images in persons with and without low back pain.

Magnetic resonance imaging findings often do not distinguish between people with and without low back pain (LBP). However, there are still a large number of people who undergo magnetic resonance imaging to help determine the etiology of their back pain. Texture analysis shows promise for the classification of tissues that look similar, and machine learning can minimize the number of comparisons. This study aimed to determine if texture features from lumbar spine magnetic resonance imaging differ between people with and without LBP. In total, 14 participants with chronic LBP were matched for age, weight, and gender with 14 healthy volunteers. A custom texture analysis software was used to construct a gray-level co-occurrence matrix with one to four pixels offset in 0° direction for the disc and superior and inferior endplate regions. The Random Forests Algorithm was used to select the most promising classifiers. The linear mixed-effect model analysis was used to compare groups (pain vs. pain-free) at each level controlling for age. The Random Forest Algorithm recommended focusing on intervertebral discs and endplate zones at L4-5 and L5-S1. Differences were observed between groups for L5-S1 superior endplate contrast, homogeneity, and energy (p = .02). Differences were observed for L5-S1 disc contrast and homogeneity (p < .01), as well as for the inferior endplates contrast, homogeneity, and energy (p < .03). Magnetic resonance imaging textural features may have potential in identifying structures that may be the target of further investigations about the reasons for LBP.

How thin can you go? Performance of thin copper and aluminum RF coil conductors.

PURPOSE: To evaluate the impact of emerging conductor technology on RF coils. Performance and resulting image quality of thin or alternate conductors (eg, aluminum instead of copper) and thicknesses (9-600 µm) are compared in terms of SNR. METHODS: Eight prototype RF coils (15 cm × 15 cm square loops) were constructed and bench-tested to measure quality factor. The coils used 6-mm-wide conducting strips of either copper or aluminum of a few different thicknesses (copper: 17, 32, 35, 127, 600 µm; aluminum: 9, 13, 20, 127 µm) on acetate projector sheets for backing. Corresponding image SNR was measured at 0.48 tesla (20.56 MHz). RESULTS: The coils spanned a range of unloaded quality factors from 89 to 390 and a fivefold range of losses. The image SNRs were consistent with the coils' bench-measured efficiencies (0.33-0.73). Thin aluminum conductors (9 µm) led to the highest reduction in SNR (65% that of 127 µm copper). Thin copper (<32 µm) conductors lead to a much smaller decrease in SNR (approximately 10%) compared to 127 µm copper. No performance difference was observed between 127 µm thick copper and aluminum. The much thicker 600 µm copper bars only yield a 5% improvement in SNR. CONCLUSION: Even at 0.48 tesla, copper RF coil conductors much thinner than those in conventional construction can be used while maintaining SNR greater than 50% that of thick copper. These emerging coil conductor technologies enable RF coil functionality that cannot be achieved otherwise.

Could compression and traction loading improve the ability of magnetic resonance imaging to identify findings related to low back pain?

BACKGROUND: Diagnostic imaging is routinely used to depict structural abnormalities in people with low back pain (LBP), but most findings are prevalent in people with and without LBP. It has been suggested that LBP is related to changes induced in the spine due to loading. Therefore, new imaging measurements are needed to improve our ability to identify structures relating to LBP. OBJECTIVES: To investigate the response of the lumbar spine to compression and traction in participants with and without chronic LBP using MRI T2-mapping. METHOD: Fifteen participants with chronic LBP were matched for age, weight, and gender with 15 healthy volunteers. All participants underwent MRI under three loading conditions maintained for 20 min each: resting supine, followed by compression and traction, both using 50% body weight. Participants were imaged in the last 5 min of each loading condition. Disc morphometric and fluid-based measurements from T2-maps were obtained. RESULTS: Traditional MRI measurements (i.e. disc height, width and mean signal intensity) were not able to capture any differences in the changes measured in response to loading between individuals with and without pain. The location of the T2 weighted centroid (WC) was able to capture the difference between groups in response to compression in the horizontal (p < 0.01) and vertical direction (p < 0.01), and in response to traction in the vertical direction (p < 0.01). While the location of T2WC moved anteriorly (Effect Size (ES): 0.44) and inferiorly with compression in those with pain (ES: 0.34), it moved posteriorly (ES: -0.14) and superiorly (ES: -0.05) in the group without pain. In response to traction, the vertical location of T2WC moved superiorly in both groups but the change was larger in those with pain (ES Pain = -0.52; ES No Pain: -0.13). CONCLUSION: The novel measurements of the location of the T2WC in the intervertebral discs were the only measurements capturing differences in response to loading between those with and without low back pain.

Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.

Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the data. While the strategy of incoherent k-space acquisition is proven for techniques such as compressed sensing, it may not be optimal for all techniques. This study compares the use of coherent low-resolution (coherent-LR) and incoherent undersampling phase-encoding for real-time 3D CNN image reconstruction. Data were acquired with our 3 T Philips Achieva system. A retrospective analysis was performed on six non-small cell lung cancer patients who received dynamic acquisitions consisting of 650 free breathing images using a bSSFP sequence. We retrospectively undersampled the data by 5x and 10x acceleration using the two phase-encoding schemes. A quantitative analysis was conducted evaluating the tumor segmentations from the CNN reconstructed data using the Dice coefficient (DC) and centroid displacement. The reconstruction noise was evaluated using the structural similarity index (SSIM). Furthermore, we qualitatively investigated the CNN reconstruction using prospectively undersampled data, where the fully sampled training data set is acquired separately from the accelerated undersampled data. The patient averaged DC, centroid displacement, and SSIM for the tumor segmentation at 5x and 10x was superior using coherent low-resolution undersampling. Furthermore, the patient-specific CNN can be trained in under 6 h and the reconstruction time was 54 ms per image. Both the incoherent and coherent-LR prospective CNN reconstructions yielded qualitatively acceptable images; however, the coherent-LR reconstruction appeared superior to the incoherent reconstruction. We have demonstrated that coherent-LR undersampling for real-time CNN image reconstruction performs quantitatively better for the retrospective case of lung tumor segmentation, and qualitatively better for the prospective case. The tumor segmentation mean DC increased for all six patients at 5x acceleration and the temporal (dynamic) variance of the segmentation was reduced. The reconstruction speed achieved for our current implementation was 54 ms, providing an acceptable frame rate for real-time on-the-fly MR imaging.

Single patient convolutional neural networks for real-time MR reconstruction: a proof of concept application in lung tumor segmentation for adaptive radiotherapy.

Investigate 3D (spatial and temporal) convolutional neural networks (CNNs) for real-time on-the-fly magnetic resonance imaging (MRI) reconstruction. In particular, we investigated the applicability of training CNNs on a patient-by-patient basis for the purpose of lung tumor segmentation. Data were acquired with our 3 T Philips Achieva system. A retrospective analysis was performed on six non-small cell lung cancer patients who received fully sampled dynamic acquisitions consisting of 650 free breathing images using a bSSFP sequence. We retrospectively undersampled the six patient's data by 5× and 10× acceleration. The retrospective data was used to quantitatively compare the CNN reconstruction to gold truth data via the Dice coefficient (DC) and centroid displacement to compare the tumor segmentations. Reconstruction noise was investigated using the normalized mean square error (NMSE). We further validated the technique using prospectively undersampled data from a volunteer and motion phantom. The retrospectively undersampled data at 5× and 10× acceleration was reconstructed using patient specific trained CNNs. The patient average DCs for the tumor segmentation at 5× and 10× acceleration were 0.94 and 0.92, respectively. These DC values are greater than the inter- and intra-observer segmentations acquired by radiation oncologist experts as reported in a previous study of ours. Furthermore, the patient specific CNN can be trained in under 6 h and the reconstruction time was 65 ms per image. The prospectively undersampled CNN reconstruction data yielded qualitatively acceptable images. We have shown that 3D CNNs can be used for real-time on-the-fly dynamic image reconstruction utilizing both spatial and temporal data in this proof of concept study. We evaluated the technique using six retrospectively undersampled lung cancer patient data sets, as well as prospectively undersampled data acquired from a volunteer and motion phantom. The reconstruction speed achieved for our current implementation was 65 ms per image.

Evaluating performance of a user-trained MR lung tumor autocontouring algorithm in the context of intra- and interobserver variations.

PURPOSE: Real-time tracking of lung tumors using magnetic resonance imaging (MRI) has been proposed as a potential strategy to mitigate the ill-effects of breathing motion in radiation therapy. Several autocontouring methods have been evaluated against a "gold standard" of a single human expert user. However, contours drawn by experts have inherent intra- and interobserver variations. In this study, we aim to evaluate our user-trained autocontouring algorithm with manually drawn contours from multiple expert users, and to contextualize the accuracy of these autocontours within intra- and interobserver variations. METHODS: Six nonsmall cell lung cancer patients were recruited, with institutional ethics approval. Patients were imaged with a clinical 3 T Philips MR scanner using a dynamic 2D balanced SSFP sequence under free breathing. Three radiation oncology experts, each in two separate sessions, contoured 130 dynamic images for each patient. For autocontouring, the first 30 images were used for algorithm training, and the remaining 100 images were autocontoured and evaluated. Autocontours were compared against manual contours in terms of Dice's coefficient (DC) and Hausdorff distances (dH ). Intra- and interobserver variations of the manual contours were also evaluated. RESULTS: When compared with the manual contours of the expert user who trained it, the algorithm generates autocontours whose evaluation metrics (same session: DC = 0.90(0.03), dH  = 3.8(1.6) mm; different session DC = 0.88(0.04), dH  = 4.3(1.5) mm) are similar to or better than intraobserver variations (DC = 0.88(0.04), and dH  = 4.3(1.7) mm) between two sessions. The algorithm's autocontours are also compared to the manual contours from different expert users with evaluation metrics (DC = 0.87(0.04), dH  = 4.8(1.7) mm) similar to interobserver variations (DC = 0.87(0.04), dH  = 4.7(1.6) mm). CONCLUSIONS: Our autocontouring algorithm delineates tumor contours (<20 ms per contour), in dynamic MRI of lung, that are comparable to multiple human experts (several seconds per contour), but at a much faster speed. At the same time, the agreement between autocontours and manual contours is comparable to the intra- and interobserver variations. This algorithm may be a key component of the real time tumor tracking workflow for our hybrid Linac-MR device in the future.

Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis (CS-PCA): Demonstration in lung tumor tracking.

PURPOSE: This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis (CS-PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. METHODS: Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS-PCA algorithm. Using a database of fully sampled k-space, principal components (PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS-PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error (NMSE), as well as the dice coefficients (DC) and centroid displacement of the tumor segmentations. RESULTS: Our results demonstrate that CS-PCA performed superior than CS alone. The CS-PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS-PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710HQ CPU @ 2.5 GHz), depending on the chosen parameters. CONCLUSIONS: A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac-MRI system. Our CS-PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The reconstruction speed for the Split Bregman CS ranged from 200 to 260 ms, whereas the CS-PCA reconstruction speed ranged from 5 to 20 ms implemented using nonoptimized MATLAB code.

A non-axial superconducting magnet design for optimized patient access and minimal SAD for use in a Linac-MR hybrid: proof of concept.

A prototype rotating hybrid magnetic resonance imaging system and linac has been developed to allow for simultaneous imaging and radiation delivery parallel to B 0. However, the design of a compact magnet capable of rotation in a small vault with sufficient patient access and a typical clinical source-to-axis distance (SAD) is challenging. This work presents a novel superconducting magnet design as a proof of concept that allows for a reduced SAD and ample patient access by moving the superconducting coils to the side of the yoke. The yoke and pole-plate structures are shaped to direct the magnetic flux appropriately. The outer surface of the pole plate is optimized subject to the minimization of a cost function, which evaluates the uniformity of the magnetic field over an ellipsoid. The magnetic field calculations required in this work are performed with the 3D finite element method software package Opera-3D. Each tentative design strategy is virtually modeled in this software package, which is externally controlled by MATLAB, with its key geometries defined as variables. The optimization variables are the thickness of the pole plate at control points distributed over the pole plate surface. A novel design concept as a superconducting non-axial magnet is introduced, which could create a large uniform B 0 magnetic field with fewer geometric restriction. This non-axial 0.5 T superconducting magnet has a moderately reduced SAD of 123 cm and a vertical patient opening of 68 cm. This work is presented as a proof of principle to investigate the feasibility of a non-axial magnet with the coils located around the yoke, and the results encourage future design optimizations to maximize the benefits of this non-axial design.

Sliding window prior data assisted compressed sensing for MRI tracking of lung tumors.

PURPOSE: Hybrid magnetic resonance imaging and radiation therapy devices are capable of imaging in real-time to track intrafractional lung tumor motion during radiotherapy. Highly accelerated magnetic resonance (MR) imaging methods can potentially reduce system delay time and/or improves imaging spatial resolution, and provide flexibility in imaging parameters. Prior Data Assisted Compressed Sensing (PDACS) has previously been proposed as an acceleration method that combines the advantages of 2D compressed sensing and the KEYHOLE view-sharing technique. However, as PDACS relies on prior data acquired at the beginning of a dynamic imaging sequence, decline in image quality occurs for longer duration scans due to drifts in MR signal. Novel sliding window-based techniques for refreshing prior data are proposed as a solution to this problem. METHODS: MR acceleration is performed by retrospective removal of data from the fully sampled sets. Six patients with lung tumors are scanned with a clinical 3 T MRI using a balanced steady-state free precession (bSSFP) sequence for 3 min at approximately 4 frames per second, for a total of 650 dynamics. A series of distinct pseudo-random patterns of partial k-space acquisition is generated such that, when combined with other dynamics within a sliding window of 100 dynamics, covers the entire k-space. The prior data in the sliding window are continuously refreshed to reduce the impact of MR signal drifts. We intended to demonstrate two different ways to utilize the sliding window data: a simple averaging method and a navigator-based method. These two sliding window methods are quantitatively compared against the original PDACS method using three metrics: artifact power, centroid displacement error, and Dice's coefficient. The study is repeated with pseudo 0.5 T images by adding complex, normally distributed noise with a standard deviation that reduces image SNR, relative to original 3 T images, by a factor of 6. RESULTS: Without sliding window implemented, PDACS-reconstructed dynamic datasets showed progressive increases in image artifact power as the 3 min scan progresses. With sliding windows implemented, this increase in artifact power is eliminated. Near the end of a 3 min scan at 3 T SNR and 5× acceleration, implementation of an averaging (navigator) sliding window method improves our metrics by the following ways: artifact power decreases from 0.065 without sliding window to 0.030 (0.031), centroid error decreases from 2.64 to 1.41 mm (1.28 mm), and Dice coefficient agreement increases from 0.860 to 0.912 (0.915). At pseudo 0.5 T SNR, the improvements in metrics are as follows: artifact power decreases from 0.110 without sliding window to 0.0897 (0.0985), centroid error decreases from 2.92 mm to 1.36 mm (1.32 mm), and Dice coefficient agreements increases from 0.851 to 0.894 (0.896). CONCLUSIONS: In this work we demonstrated the negative impact of slow changes in MR signal for longer duration PDACS dynamic scans, namely increases in image artifact power and reductions of tumor tracking accuracy. We have also demonstrated sliding window implementations (i.e., refreshing of prior data) of PDACS are effective solutions to this problem at both 3 T and simulated 0.5 T bSSFP images.

SNR efficiency of combined bipolar gradient echoes: Comparison of three-dimensional FLASH, MPRAGE, and multiparameter mapping with VFA-FLASH and MP2RAGE.

PURPOSE: High-bandwidth bipolar multiecho gradient echo sequences are increasingly popular in structural brain imaging because of reduced water-fat shifts, lower susceptibility effects, and improved signal-to-noise ratio (SNR) efficiency. In this study, we investigated the performance of three three-dimensional multiecho sequences (MPRAGE, MP2RAGE, and FLASH) with scan times < 9 min and 1-mm isotropic resolution against their single-echo, low-bandwidth counterparts at 3T. We also compared the performance of multiparameter mapping (PD, T1 , and T2*) with bipolar multiecho MP2RAGE versus the variable flip angle technique with multiecho FLASH (VFA-FLASH). METHODS: Multiecho sequences were optimized to yield equivalent contrast and improved SNR compared with their single-echo counterparts. Theoretical SNR gains were verified with measurements in a multilayered phantom. Robust image processing pipelines extracted PD, T1 , and T2* maps from MP2RAGE or VFA-FLASH, and the corresponding SNR was measured with varying SENSE accelerations (R = 1-5) and number of echoes (N = 1-12). All sequences were tested on four healthy volunteers. RESULTS: Multiecho sequences achieved SNR gains of 1.3-1.6 over single-echo sequences. MP2RAGE yielded comparable T1 -to-noise ratio to VFA-FLASH, but significantly lower SNR (<50%) in PD and T2* maps. Measured SNR gains agreed with the theoretical predictions for SENSE accelerations ≤3. CONCLUSION: Multiecho sequences achieve higher SNR efficiency over conventional single-echo sequences, despite three-fold higher sampling bandwidths. VFA-FLASH surpasses MP2RAGE in its ability to map three parameters with high SNR and 1-mm isotropic resolution in a clinically relevant scan time (∼8:30 min), whereas MP2RAGE yields lower intersubject variability in T1 . Magn Reson Med 77:2186-2202, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Role of serial multiparametric magnetic resonance imaging in prostate cancer active surveillance.

AIM: To examine whether addition of 3T multiparametric magnetic resonance imaging (mpMRI) to an active surveillance protocol could detect aggressive or progressive prostate cancer. METHODS: Twenty-three patients with low risk disease were enrolled on this active surveillance study, all of which had Gleason score 6 or less disease. All patients had clinical assessments, including digital rectal examination and prostate specific antigen (PSA) testing, every 6 mo with annual 3T mpMRI scans with gadolinium contrast and minimum sextant prostate biopsies. The MRI images were anonymized of patient identifiers and clinical information and each scan underwent radiological review without the other results known. Descriptive statistics for demographics and follow-up as well as the sensitivity and specificity of mpMRI to identify prostate cancer and progressive disease were calculated. RESULTS: During follow-up (median 24.8 mo) 11 of 23 patients with low-risk prostate cancer had disease progression and were taken off study to receive definitive treatment. Disease progression was identified through upstaging of Gleason score on subsequent biopsies for all 11 patients with only 2 patients also having a PSA doubling time of less than 2 years. All 23 patients had biopsy confirmed prostate cancer but only 10 had a positive index of suspicion on mpMRI scans at baseline (43.5% sensitivity). Aggressive disease prediction from baseline mpMRI scans had satisfactory specificity (81.8%) but low sensitivity (58.3%). Twenty-two patients had serial mpMRI scans and evidence of disease progression was seen for 3 patients all of whom had upstaging of Gleason score on biopsy (30% specificity and 100% sensitivity). CONCLUSION: Addition of mpMRI imaging in active surveillance decision making may help in identifying aggressive disease amongst men with indolent prostate cancer earlier than traditional methods.

Analytical corrections of banding artifacts in driven equilibrium single pulse observation of T2 (DESPOT2).

PURPOSE: DESPOT2 is a single-component T2 mapping technique based on bSSFP imaging. It has seen limited application because of banding artifacts and magnetization transfer (MT) effects. In this work, acquisitions are optimized to minimize MT effects, while exact and approximate analytical equations enable automatic correction of banding artifacts within the T2 maps in mere seconds. THEORY AND METHODS: The technique was verified on an agar phantom at 3 tesla. The T2 resulting from four different data combination techniques was compared with the T2 from CPMG. Two comparable DESPOT2 scan protocols (short vs. long TR/TRF ) designed to minimize MT effects, were tested both in the phantom and in vivo. A third protocol was tested in the brain of 8 volunteers and analytical correction schemes were compared with DESPOT2-FM. RESULTS: The T2 measurements in agar agree with CPMG within ∼7% and in vivo results agree with values reported in the literature. The approximate analytical solutions provide increased robustness to hardware imperfections and higher T2 -to-noise ratio than the exact solutions. CONCLUSION: New analytical solutions enable fast and accurate whole-brain T2 mapping from previously measured T1 and B1 maps, and bSSFP images with at least two phase offsets and two flip angles (=4 datasets, 8 min scan). Magn Reson Med 76:1790-1804, 2016. © 2015 International Society for Magnetic Resonance in Medicine.

Neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR.

PURPOSE: To develop a neural-network based autocontouring algorithm for intrafractional lung-tumor tracking using Linac-MR and evaluate its performance with phantom and in-vivo MR images. METHODS: An autocontouring algorithm was developed to determine both the shape and position of a lung tumor from each intrafractional MR image. A pulse-coupled neural network was implemented in the algorithm for contrast improvement of the tumor region. Prior to treatment, to initiate the algorithm, an expert user needs to contour the tumor and its maximum anticipated range of motion in pretreatment MR images. During treatment, however, the algorithm processes each intrafractional MR image and automatically generates a tumor contour without further user input. The algorithm is designed to produce a tumor contour that is the most similar to the expert's manual one. To evaluate the autocontouring algorithm in the author's Linac-MR environment which utilizes a 0.5 T MRI, a motion phantom and four lung cancer patients were imaged with 3 T MRI during normal breathing, and the image noise was degraded to reflect the image noise at 0.5 T. Each of the pseudo-0.5 T images was autocontoured using the author's algorithm. In each test image, the Dice similarity index (DSI) and Hausdorff distance (HD) between the expert's manual contour and the algorithm generated contour were calculated, and their centroid positions were compared (Δd centroid). RESULTS: The algorithm successfully contoured the shape of a moving tumor from dynamic MR images acquired every 275 ms. From the phantom study, mean DSI of 0.95-0.96, mean HD of 2.61-2.82 mm, and mean Δd centroid of 0.68-0.93 mm were achieved. From the in-vivo study, the author's algorithm achieved mean DSI of 0.87-0.92, mean HD of 3.12-4.35 mm, as well as Δd centroid of 1.03-1.35 mm. Autocontouring speed was less than 20 ms for each image. CONCLUSIONS: The authors have developed and evaluated a lung tumor autocontouring algorithm for intrafractional tumor tracking using Linac-MR. The autocontouring performance in the Linac-MR environment was evaluated using phantom and in-vivo MR images. From the in-vivo study, the author's algorithm achieved 87%-92% of contouring agreement and centroid tracking accuracy of 1.03-1.35 mm. These results demonstrate the feasibility of lung tumor autocontouring in the author's laboratory's Linac-MR environment.