Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.

OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS. METHODS: Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed. RESULTS: Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence. CONCLUSIONS: Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it's utility in patient care.

MRI radiomics for hamstring strain injury identification and return to sport classification: a pilot study.

OBJECTIVE: To determine if MRI-based radiomics from hamstring muscles are related to injury and if the features could be used to perform a time to return to sport (RTS) classification. We hypothesize that radiomics from hamstring muscles, especially T2-weighted and diffusion tensor imaging-based features, are related to injury and can be used for RTS classification. SUBJECTS AND METHODS: MRI data from 32 athletes at the University of Wisconsin-Madison that sustained a hamstring strain injury were collected. Diffusion tensor imaging and T1- and T2-weighted images were processed, and diffusion maps were calculated. Radiomics features were extracted from the four hamstring muscles in each limb and for each MRI modality, individually. Feature selection was performed and multiple support vector classifiers were cross-validated to differentiate between involved and uninvolved limbs and perform binary (≤ or > 25 days) and multiclass (< 14 vs. 14-42 vs. > 42 days) classification of RTS. RESULT: The combination of radiomics features from all diffusion tensor imaging and T2-weighted images provided the most accurate differentiation between involved and uninvolved limbs (AUC ≈ 0.84 ± 0.16). For the binary RTS classification, the combination of all extracted radiomics offered the most accurate classification (AUC ≈ 0.95 ± 0.15). While for the multiclass RTS classification, the combination of features from all the diffusion tensor imaging maps provided the most accurate classification (weighted one vs. rest AUC ≈ 0.81 ± 0.16). CONCLUSION: This pilot study demonstrated that radiomics features from hamstring muscles are related to injury and have the potential to predict RTS.

[68 Ga]Ga-FAPI-46 PET for non-invasive detection of pulmonary fibrosis disease activity.

PURPOSE: The lack of effective molecular biomarkers to monitor idiopathic pulmonary fibrosis (IPF) activity or treatment response remains an unmet clinical need. Herein, we determined the utility of fibroblast activation protein inhibitor for positron emission tomography (FAPI PET) imaging in a mouse model of pulmonary fibrosis. METHODS: Pulmonary fibrosis was induced by intratracheal administration of bleomycin (1 U/kg) while intratracheal saline was administered to control mice. Subgroups from each cohort (n = 3-5) underwent dynamic 1 h PET/CT after intravenously injecting FAPI-46 radiolabeled with gallium-68 ([68 Ga]Ga-FAPI-46) at 7 days and 14 days following disease induction. Animals were sacrificed following imaging for ex vivo gamma counting and histologic correlation. [68 Ga]Ga-FAPI-46 uptake was quantified and reported as percent injected activity per cc (%IA/cc) or percent injected activity (%IA). Lung CT density in Hounsfield units (HU) was also correlated with histologic examinations of lung fibrosis. RESULTS: CT only detected differences in the fibrotic response at 14 days post-bleomycin administration. [68 Ga]Ga-FAPI-46 lung uptake was significantly higher in the bleomycin group than in control subjects at 7 days and 14 days. Significantly (P = 0.0012) increased [68 Ga]Ga-FAPI-46 lung uptake in the bleomycin groups at 14 days (1.01 ± 0.12%IA/cc) vs. 7 days (0.33 ± 0.09%IA/cc) at 60 min post-injection of the tracer was observed. These findings were consistent with an increase in both fibrinogenesis and FAP expression as seen in histology. CONCLUSION: CT was unable to assess disease activity in a murine model of IPF. Conversely, FAPI PET detected both the presence and activity of lung fibrogenesis, making it a promising tool for assessing early disease activity and evaluating the efficacy of therapeutic interventions in lung fibrosis patients.

PET Image Quality Improvement for Simultaneous PET/MRI with a Lightweight MRI Surface Coil.

Background During simultaneous PET/MRI, flexible MRI surface coils that lay on the patient are often omitted from PET attenuation correction processing, leading to quantification bias in PET images. Purpose To identify potential PET image quality improvement by using a recently developed lightweight MRI coil technology for the anterior array (AA) surface coil in both a phantom and in vivo study. Materials and Methods A phantom study and a prospective in vivo study were performed with a PET/CT scanner under three conditions: (a) no MRI surface coil (standard of reference), (b) traditional AA coil, and (c) lightweight AA coil. AA coils were not used in attenuation correction processing to emulate clinical PET/MRI. For the phantom study, PET images were reconstructed with and without time of flight (TOF) to assess quantification accuracy and uniformity. The in vivo study consisted of 10 participants (mean age, 66 years ± 10 [standard deviation]; six men) referred for a PET/CT oncologic examination who had undergone imaging between October 2019 and February 2020. Assessment of image quantification bias (defined as the standard error of the mean values) was conducted by comparing mean liver region of interest standardized uptake values with the no-coil standard of reference. A Wilcoxon signed-rank test was used to establish significance. Results For TOF and non-TOF, respectively, the phantom study revealed a mean PET quantification bias of -9.0% and -8.6% with the traditional AA coil and a mean PET quantification bias of -4.3% and -4.0% with the lightweight AA coil. The coefficients of variation reduced from 4.3% and 6.2% with the traditional AA coil to 2.1% and 2.7% with the lightweight AA coil, which demonstrated a homogeneity benefit from the lightweight coil that was greater with, versus without, TOF reconstruction. For the in vivo study, the mean liver standardized uptake value error was -5.9% with the traditional AA coil (P = .002 vs no coil) and -2.4% with the lightweight AA coil (P = .004 vs no coil). Conclusion The lightweight anterior array coil reduced PET image quantification bias by more than 50% compared with the traditional coil. Using the lightweight coil and performing time of flight-based reconstruction each reduced the variation of error. © RSNA, 2020 Online supplemental material is available for this article.

Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement.

OBJECTIVE: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging. METHODS: Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers. RESULTS: Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively. CONCLUSIONS: Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images.

Robustifying Deep Networks for Medical Image Segmentation.

The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. Two representative UNets were implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR) into four pixelwise labels (Gd-enhancing tumor, peritumoral edema, necrotic and non-enhancing tumor, and background). We developed attack strategies based on the fast gradient sign method (FGSM), iterative FGSM (i-FGSM), and targeted iterative FGSM (ti-FGSM) to produce effective but imperceptible attacks. Additionally, we explored the effectiveness of distillation and adversarial training via data augmentation to counteract these adversarial attacks. Robustness was measured by comparing the Dice coefficients for the attacks using Wilcoxon signed-rank tests. The experimental results show that attacks based on FGSM, i-FGSM, and ti-FGSM were effective in reducing the quality of image segmentation by up to 65% in the Dice coefficient. For attack defenses, distillation performed significantly better than adversarial training approaches. However, all defense approaches performed worse compared to unperturbed test images. Therefore, segmentation networks can be adversely affected by targeted attacks that introduce visually minor (and potentially undetectable) modifications to existing images. With an increasing interest in applying deep learning techniques to medical imaging data, it is important to quantify the ramifications of adversarial inputs (either intentional or unintentional).

Changes in Endogenous Dopamine Induced by Methylphenidate Predict Functional Connectivity in Nonhuman Primates.

Dopamine (DA) levels in the striatum are increased by many therapeutic drugs, such as methylphenidate (MPH), which also alters behavioral and cognitive functions thought to be controlled by the PFC dose-dependently. We linked DA changes and functional connectivity (FC) using simultaneous [18F]fallypride PET and resting-state fMRI in awake male rhesus monkeys after oral administration of various doses of MPH. We found a negative correlation between [18F]fallypride nondisplaceable binding potential (BPND) and MPH dose in the head of the caudate (hCd), demonstrating increased extracellular DA resulting from MPH administration. The decreased BPND was negatively correlated with FC between the hCd and the PFC. Subsequent voxelwise analyses revealed negative correlations with FC between the hCd and the dorsolateral PFC, hippocampus, and precuneus. These results, showing that MPH-induced changes in DA levels in the hCd predict resting-state FC, shed light on a mechanism by which changes in striatal DA could influence function in the PFC.SIGNIFICANCE STATEMENT Dopamine transmission is thought to play an essential role in shaping large scale-neural networks that underlie cognitive functions. It is the target of therapeutic drugs, such as methylphenidate (Ritalin), which blocks the dopamine transporter, thereby increasing extracellular dopamine levels. Methylphenidate is used extensively to treat attention deficit hyperactivity disorder, even though its effects on cognitive functions and their underlying neural mechanisms are not well understood. To date, little is known about the link between changes in dopamine levels and changes in functional brain organization. Using simultaneous PET/MR imaging, we show that methylphenidate-induced changes in endogenous dopamine levels in the head of the caudate predict changes in resting-state functional connectivity between this structure and the prefrontal cortex, precuneus, and hippocampus.

Rapid single scan ramped hybrid-encoding for bicomponent T2* mapping in a human knee joint: A feasibility study.

The purpose of this study is to determine the feasibility of using a single scan ramped hybrid-encoding (RHE) method for rapid bicomponent T2* analysis of the human knee joint. The proposed method utilizes RHE to acquire ultrashort echo time (UTE) and subsequent gradient echo images at 16 different echo times ranging between 40 µs and 30 ms in a single scan. In the proposed RHE technique, UTE imaging was followed by acquisition of 14 gradient recalled echo images, where an additional UTE image was obtained within the first readout by oversampling single point imaging (SPI) encoding. The single scan RHE method with a 9-minute scan time was performed on human cadaveric knee joints from six donors and in vivo knee joints from four healthy volunteers at 3 T. A bicomponent signal model was used to characterize the short T2* and long T2* water components. Mean bicomponent T2* parameters for patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL) and meniscus were calculated. In the experimental results, the RHE technique provided bicomponent T2* parameter estimations of tendon, ACL, PCL and meniscus, which were similar to previously reported values in the literature. In conclusion, the proposed single scan RHE technique provides rapid bicomponent T2* analysis of the human knee joint with a total scan time of less than 9 minutes.

Prospective comparison of longitudinal change in hepatic proton density fat fraction (PDFF) estimated by magnitude-based MRI (MRI-M) and complex-based MRI (MRI-C).

PURPOSE: To compare longitudinal hepatic proton density fat fraction (PDFF) changes estimated by magnitude- vs. complex-based chemical-shift-encoded MRI during a weight loss surgery (WLS) program in severely obese adults with biopsy-proven nonalcoholic fatty liver disease (NAFLD). METHODS: This was a secondary analysis of a prospective dual-center longitudinal study of 54 adults (44 women; mean age 52 years; range 27-70 years) with obesity, biopsy-proven NAFLD, and baseline PDFF ≥ 5%, enrolled in a WLS program. PDFF was estimated by confounder-corrected chemical-shift-encoded MRI using magnitude (MRI-M)- and complex (MRI-C)-based techniques at baseline (visit 1), after a 2- to 4-week very low-calorie diet (visit 2), and at 1, 3, and 6 months (visits 3 to 5) after surgery. At each visit, PDFF values estimated by MRI-M and MRI-C were compared by a paired t test. Rates of PDFF change estimated by MRI-M and MRI-C for visits 1 to 3, and for visits 3 to 5 were assessed by Bland-Altman analysis and intraclass correlation coefficients (ICCs). RESULTS: MRI-M PDFF estimates were lower by 0.5-0.7% compared with those of MRI-C at all visits (p < 0.001). There was high agreement and no difference between PDFF change rates estimated by MRI-M vs. MRI-C for visits 1 to 3 (ICC 0.983, 95% CI 0.971, 0.99; bias = - 0.13%, p = 0.22), or visits 3 to 5 (ICC 0.956, 95% CI 0.919-0.977%; bias = 0.03%, p = 0.36). CONCLUSION: Although MRI-M underestimates PDFF compared with MRI-C cross-sectionally, this bias is consistent and MRI-M and MRI-C agree in estimating the rate of hepatic PDFF change longitudinally. KEY POINTS: • MRI-M demonstrates a significant but small and consistent bias (0.5-0.7%; p < 0.001) towards underestimation of PDFF compared with MRI-C at 3 T. • Rates of PDFF change estimated by MRI-M and MRI-C agree closely (ICC 0.96-0.98) in adults with severe obesity and biopsy- proven NAFLD enrolled in a weight loss surgery program. • Our findings support the use of either MRI technique (MRI-M or MRI-C) for clinical care or by individual sites or for multi-center trials that include PDFF change as an endpoint. However, since there is a bias in their measurements, the same technique should be used in any given patient for longitudinal follow-up.

Monitoring Fatty Liver Disease with MRI Following Bariatric Surgery: A Prospective, Dual-Center Study.

Purpose To longitudinally monitor liver fat before and after bariatric surgery by using quantitative chemical shift-encoded (CSE) MRI and to compare with changes in body mass index (BMI), weight, and waist circumference (WC). Materials and Methods For this prospective study, which was approved by the internal review board, a total of 126 participants with obesity who were undergoing evaluation for bariatric surgery with preoperative very low calorie diet (VLCD) were recruited from June 27, 2010, through May 5, 2015. Written informed consent was obtained from all participants. Participants underwent CSE MRI measuring liver proton density fat fraction (PDFF) before VLCD (2-3 weeks before surgery), after VLCD (1-3 days before surgery), and 1, 3, and 6-10 months following surgery. Linear regression was used to estimate rates of change of PDFF (ΔPDFF) and body anthropometrics. Initial PDFF (PDFF0), initial anthropometrics, and anthropometric rates of change were evaluated as predictors of ΔPDFF. Mixed-effects regression was used to estimate time to normalization of PDFF. Results Fifty participants (mean age, 51.0 years; age range, 27-70 years), including 43 women (mean age, 50.8 years; age range, 27-70 years) and seven men (mean age, 51.7 years; age range, 36-62 years), with mean PDFF0 ± standard deviation of 18.1% ± 8.6 and mean BMI0 of 44.9 kg/m2 ± 6.5 completed the study. By 6-10 months following surgery, mean PDFF decreased to 4.9% ± 3.4 and mean BMI decreased to 34.5 kg/m2 ± 5.4. Mean estimated time to PDFF normalization was 22.5 weeks ± 11.5. PDFF0 was the only strong predictor for both ΔPDFF and time to PDFF normalization. No body anthropometric correlated with either outcome. Conclusion Average liver proton density fat fraction (PDFF) decreased to normal (< 5%) by 6-10 months following surgery, with mean time to normalization of approximately 5 months. Initial PDFF was a strong predictor of both rate of change of PDFF and time to normalization. Body anthropometrics did not predict either outcome. Online supplemental material is available for this article. © RSNA, 2018.

Dynamic whole-body PET imaging: principles, potentials and applications.

PURPOSE: In this article, we discuss dynamic whole-body (DWB) positron emission tomography (PET) as an imaging tool with significant clinical potential, in relation to conventional standard uptake value (SUV) imaging. BACKGROUND: DWB PET involves dynamic data acquisition over an extended axial range, capturing tracer kinetic information that is not available with conventional static acquisition protocols. The method can be performed within reasonable clinical imaging times, and enables generation of multiple types of PET images with complementary information in a single imaging session. Importantly, DWB PET can be used to produce multi-parametric images of (i) Patlak slope (influx rate) and (ii) intercept (referred to sometimes as "distribution volume"), while also providing (iii) a conventional 'SUV-equivalent' image for certain protocols. RESULTS: We provide an overview of ongoing efforts (primarily focused on FDG PET) and discuss potential clinically relevant applications. CONCLUSION: Overall, the framework of DWB imaging [applicable to both PET/CT(computed tomography) and PET/MRI (magnetic resonance imaging)] generates quantitative measures that may add significant value to conventional SUV image-derived measures, with limited pitfalls as we also discuss in this work.

Simultaneous determination of dynamic cardiac metabolism and function using PET/MRI.

BACKGROUND: Cardiac metabolic changes in heart disease precede overt contractile dysfunction. However, metabolism and function are not typically assessed together in clinical practice. The purpose of this study was to develop a cardiac positron emission tomography/magnetic resonance (PET/MR) stress test to assess the dynamic relationship between contractile function and metabolism in a preclinical model. METHODS: Following an overnight fast, healthy pigs (45-50 kg) were anesthetized and mechanically ventilated. 18F-fluorodeoxyglucose (18F-FDG) solution was administered intravenously at a constant rate of 0.01 mL/s for 60 minutes. A cardiac PET/MR stress test was performed using normoxic gas (FIO2 = .209) and hypoxic gas (FIO2 = .12). Simultaneous cardiac imaging was performed on an integrated 3T PET/MR scanner. RESULTS: Hypoxic stress induced a significant increase in heart rate, cardiac output, left ventricular (LV) ejection fraction (EF), and peak torsion. There was a significant decline in arterial SpO2, LV end-diastolic and end-systolic volumes in hypoxia. Increased LV systolic function was coupled with an increase in myocardial FDG uptake (Ki) during hypoxic stress. CONCLUSION: PET/MR with continuous FDG infusion captures dynamic changes in both cardiac metabolism and contractile function. This technique warrants evaluation in human cardiac disease for assessment of subtle functional and metabolic abnormalities.

MR-based treatment planning in radiation therapy using a deep learning approach.

PURPOSE: To develop and evaluate the feasibility of deep learning approaches for MR-based treatment planning (deepMTP) in brain tumor radiation therapy. METHODS AND MATERIALS: A treatment planning pipeline was constructed using a deep learning approach to generate continuously valued pseudo CT images from MR images. A deep convolutional neural network was designed to identify tissue features in volumetric head MR images training with co-registered kVCT images. A set of 40 retrospective 3D T1-weighted head images was utilized to train the model, and evaluated in 10 clinical cases with brain metastases by comparing treatment plans using deep learning generated pseudo CT and using an acquired planning kVCT. Paired-sample Wilcoxon signed rank sum tests were used for statistical analysis to compare dosimetric parameters of plans made with pseudo CT images generated from deepMTP to those made with kVCT-based clinical treatment plan (CTTP). RESULTS: deepMTP provides an accurate pseudo CT with Dice coefficients for air: 0.95 ± 0.01, soft tissue: 0.94 ± 0.02, and bone: 0.85 ± 0.02 and a mean absolute error of 75 ± 23 HU compared with acquired kVCTs. The absolute percentage differences of dosimetric parameters between deepMTP and CTTP was 0.24% ± 0.46% for planning target volume (PTV) volume, 1.39% ± 1.31% for maximum dose and 0.27% ± 0.79% for the PTV receiving 95% of the prescribed dose (V95). Furthermore, no significant difference was found for PTV volume (P = 0.50), the maximum dose (P = 0.83) and V95 (P = 0.19) between deepMTP and CTTP. CONCLUSIONS: We have developed an automated approach (deepMTP) that allows generation of a continuously valued pseudo CT from a single high-resolution 3D MR image and evaluated it in partial brain tumor treatment planning. The deepMTP provided dose distribution with no significant difference relative to a kVCT-based standard volumetric modulated arc therapy plans.

Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared with current MR imaging-based AC approaches. © RSNA, 2017 Online supplemental material is available for this article.

Rapid dual-echo ramped hybrid encoding MR-based attenuation correction (dRHE-MRAC) for PET/MR.

PURPOSE: In this study, we propose a rapid acquisition for MR-based attenuation correction (MRAC) in positron emission tomography (PET)/MR imaging, in which an ultrashort echo time (UTE) image and an out-of-phase echo image are obtained within a single rapid scan (35 s) at high spatial resolution (1 mm3 ), which allows accurate estimation of a pseudo CT image using 4-class tissue classification (discrete bone, discrete air, continuous fat, and continuous water). METHODS: In dual-echo ramped hybrid encoding (dRHE), a UTE echo is directly followed by a second out-of-phase echo, in which hybrid spatial encoding combining single-point imaging and 3-dimensional radial frequency encoding is used to improve the quality of both images. Two-point Dixon reconstruction is used to estimate fat- and water-separated images, and UTE images are used to estimate bone. Air and bone segmentation is improved by using multiple UTE images with an advanced hybrid-encoding scheme that allows reconstruction of multiple UTE images. To evaluate the proposed method, dRHE-MRAC PET/MR brain imaging was performed in 10 subjects. Dice coefficients and PET reconstruction errors relative to CT-based attenuation correction were compared with existing system MRAC approaches. RESULTS: In dRHE-MRAC, the Dice coefficients for soft tissue, air, and bone were respectively 0.95 ± 0.01, 0.62 ± 0.06, and 0.78 ± 0.05, which was a significantly improved result compared with existing approaches. In most brain regions, dRHE-MRAC showed significantly reduced PET error (less than 1%) with P values less than 0.05. CONCLUSIONS: Dual-echo ramped hybrid encoding enables rapid and robust imaging for MRAC with a very rapid acquisition. Magn Reson Med 79:2912-2922, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Fully phase-encoded MRI near metallic implants using ultrashort echo times and broadband excitation.

PURPOSE: To develop a fully phase-encoded MRI method for distortion-free imaging near metallic implants, in clinically feasible acquisition times. THEORY AND METHODS: An accelerated 3D fully phase-encoded acquisition with broadband excitation and ultrashort echo times is presented, which uses a broadband radiofrequency pulse to excite the entire off-resonance induced by the metallic implant. Furthermore, fully phase-encoded imaging is used to prevent distortions caused by frequency encoding, and to obtain ultrashort echo times for rapidly decaying signal. RESULTS: Phantom and in vivo acquisitions were used to describe the relationship among excitation bandwidth, signal loss near metallic implants, and T1 weighting. Shorter radiofrequency pulses captured signal closer to the implant by improving spectral coverage and allowing shorter echo times, whereas longer pulses improved T1 weighting through larger maximum attainable flip angles. Comparisons of fully phase-encoded acquisition with broadband excitation and ultrashort echo times to T1 -weighted multi-acquisition with variable resonance image combination selective were performed in phantoms and subjects with metallic knee and hip prostheses. These acquisitions had similar contrast and acquisition efficiency. CONCLUSIONS: Accelerated fully phase-encoded acquisitions with ultrashort echo times and broadband excitation can generate distortion free images near metallic implants in clinically feasible acquisition times. Magn Reson Med 79:2156-2163, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A rapid and robust gradient measurement technique using dynamic single-point imaging.

PURPOSE: We propose a new gradient measurement technique based on dynamic single-point imaging (SPI), which allows simple, rapid, and robust measurement of k-space trajectory. METHODS: To enable gradient measurement, we utilize the variable field-of-view (FOV) property of dynamic SPI, which is dependent on gradient shape. First, one-dimensional (1D) dynamic SPI data are acquired from a targeted gradient axis, and then relative FOV scaling factors between 1D images or k-spaces at varying encoding times are found. These relative scaling factors are the relative k-space position that can be used for image reconstruction. The gradient measurement technique also can be used to estimate the gradient impulse response function for reproducible gradient estimation as a linear time invariant system. RESULTS: The proposed measurement technique was used to improve reconstructed image quality in 3D ultrashort echo, 2D spiral, and multi-echo bipolar gradient-echo imaging. In multi-echo bipolar gradient-echo imaging, measurement of the k-space trajectory allowed the use of a ramp-sampled trajectory for improved acquisition speed (approximately 30%) and more accurate quantitative fat and water separation in a phantom. CONCLUSION: The proposed dynamic SPI-based method allows fast k-space trajectory measurement with a simple implementation and no additional hardware for improved image quality. Magn Reson Med 78:950-962, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Externally calibrated parallel imaging for 3D multispectral imaging near metallic implants using broadband ultrashort echo time imaging.

PURPOSE: To develop an externally calibrated parallel imaging technique for three-dimensional multispectral imaging (3D-MSI) in the presence of metallic implants. THEORY AND METHODS: A fast, ultrashort echo time (UTE) calibration acquisition is proposed to enable externally calibrated parallel imaging techniques near metallic implants. The proposed calibration acquisition uses a broadband radiofrequency (RF) pulse to excite the off-resonance induced by the metallic implant, fully phase-encoded imaging to prevent in-plane distortions, and UTE to capture rapidly decaying signal. The performance of the externally calibrated parallel imaging reconstructions was assessed using phantoms and in vivo examples. RESULTS: Phantom and in vivo comparisons to self-calibrated parallel imaging acquisitions show that significant reductions in acquisition times can be achieved using externally calibrated parallel imaging with comparable image quality. Acquisition time reductions are particularly large for fully phase-encoded methods such as spectrally resolved fully phase-encoded three-dimensional (3D) fast spin-echo (SR-FPE), in which scan time reductions of up to 8 min were obtained. CONCLUSION: A fully phase-encoded acquisition with broadband excitation and UTE enabled externally calibrated parallel imaging for 3D-MSI, eliminating the need for repeated calibration regions at each frequency offset. Significant reductions in acquisition time can be achieved, particularly for fully phase-encoded methods like SR-FPE. Magn Reson Med 77:2303-2309, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Dosimetric comparison of DEFGEL and PAGAT formulae paired with an MRI acquisition.

Normoxic Polyacrylamide Gels, or nPAGs, are 3D gel dosimeters that measure dose through the process of radiation-induced polymerization. Two nPAG formulae are DEFGEL and PAGAT, which are very similar, but differ mainly due to different weight fractions of monomers. The dosimetric resolutions of the two formulae when paired with a Spin-Echo (SE) MRI sequence and a monoexponential fit were compared over a range of 0-15 Gy. It was found that in the dose range 0-6 Gy the PAGAT formula generally showed a much finer dose resolution, while the DEFGEL formula showed a finer resolution from 8-15 Gy.