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.

Attenuation correction and truncation completion for breast PET/MR imaging using deep learning

Objective. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust attenuation correction algorithm for breast PET/MR images that relies on deep learning (DL) to recreate the missing portions of the patient's anatomy (truncation completion), as well as to provide bone information for attenuation correction from only the PET data.Approach. Data acquired from 23 female subjects with invasive breast cancer scanned with18F-fluorodeoxyglucose PET/CT and PET/MR localized to the breast region were used for this study. Three DL models, U-Net with mean absolute error loss (DLMAE) model, U-Net with mean squared error loss (DLMSE) model, and U-Net with perceptual loss (DLPerceptual) model, were trained to predict synthetic CT images (sCT) for PET attenuation correction (AC) given non-attenuation corrected (NAC) PETPET/MRimages as inputs. The DL and Dixon-based sCT reconstructed PET images were compared against those reconstructed from CT images by calculating the percent error of the standardized uptake value (SUV) and conducting Wilcoxon signed rank statistical tests.Main results. sCT images from the DLMAEmodel, the DLMSEmodel, and the DLPerceptualmodel were similar in mean absolute error (MAE), peak-signal-to-noise ratio, and normalized cross-correlation. No significant difference in SUV was found between the PET images reconstructed using the DLMSEand DLPerceptualsCTs compared to the reference CT for AC in all tissue regions. All DL methods performed better than the Dixon-based method according to SUV analysis.Significance. A 3D U-Net with MSE or perceptual loss model can be implemented into a reconstruction workflow, and the derived sCT images allow successful truncation completion and attenuation correction for breast PET/MR images.

Vagus nerve stimulation in the non-human primate: implantation methodology, characterization of nerve anatomy, target engagement and experimental applications

BACKGROUND

Vagus nerve stimulation (VNS) is a FDA approved therapy regularly used to treat a variety of neurological disorders that impact the central nervous system (CNS) including epilepsy and stroke. Putatively, the therapeutic efficacy of VNS results from its action on neuromodulatory centers via projections of the vagus nerve to the solitary tract nucleus. Currently, there is not an established large animal model that facilitates detailed mechanistic studies exploring how VNS impacts the function of the CNS, especially during complex behaviors requiring motor action and decision making.

METHODS

We describe the anatomical organization, surgical methodology to implant VNS electrodes on the left gagus nerve and characterization of target engagement/neural interface properties in a non-human primate (NHP) model of VNS that permits chronic stimulation over long periods of time. Furthermore, we describe the results of pilot experiments in a small number of NHPs to demonstrate how this preparation might be used in an animal model capable of performing complex motor and decision making tasks.

RESULTS

VNS electrode impedance remained constant over months suggesting a stable interface. VNS elicited robust activation of the vagus nerve which resulted in decreases of respiration rate and/or partial pressure of carbon dioxide in expired air, but not changes in heart rate in both awake and anesthetized NHPs.

CONCLUSIONS

We anticipate that this preparation will be very useful to study the mechanisms underlying the effects of VNS for the treatment of conditions such as epilepsy and depression, for which VNS is extensively used, as well as for the study of the neurobiological basis underlying higher order functions such as learning and memory.

Next-Generation Cancer Magnetic Resonance Imaging With Tumor-Targeted Alkylphosphocholine Metal Analogs

OBJECTIVES

In an effort to exploit the elevated need for phospholipids displayed by cancer cells relative to normal cells, we have developed tumor-targeted alkylphosphocholines (APCs) as broad-spectrum cancer imaging and therapy agents. Radioactive APC analogs have exhibited selective uptake and prolonged tumor retention in over 50 cancer types in preclinical models, as well as over 15 cancer types in over a dozen clinical trials. To push the structural limits of this platform, we recently added a chelating moiety capable of binding gadolinium and many other metals for cancer-targeted magnetic resonance imaging (MRI), positron emission tomography imaging, and targeted radionuclide therapy. The aim of this work was to synthesize, characterize, and validate the tumor selectivity of a new broad-spectrum, tumor-targeted, macrocyclic MRI chelate, Gd-NM600, in xenograft and orthotopic tumor models. A secondary aim was to identify and track the in vivo chemical speciation and spatial localization of this new chelate Gd-NM600 in order to assess its Gd deposition properties.

MATERIALS AND METHODS

T1 relaxivities of Gd-NM600 were characterized in water and plasma at 1.5 T and 3.0 T. Tumor uptake and subcellular localization studies were performed using transmission electron microscopy. We imaged 8 different preclinical models of human cancer over time and compared the T1-weighted imaging results to that of a commercial macrocyclic Gd chelate, Gd-DOTA. Finally, matrix-assisted laser desorption and ionization-mass spectrometry imaging was used to characterize and map the tissue distribution of the chemical species of Gd-NM600.

RESULTS

Gd-NM600 exhibits high T1 relaxivity (approximately 16.4 s-1/mM at 1.5 T), excellent tumor uptake (3.95 %ID/g at 48 hours), prolonged tumor retention (7 days), and MRI conspicuity. Moreover, minimal tumor uptake saturability of Gd-NM600 was observed. Broad-spectrum tumor-specific uptake was demonstrated in 8 different human cancer models. Cancer cell uptake of Gd-NM600 via endosomal internalization and processing was revealed with transmission electron microscopy. Importantly, tissue mass spectrometry imaging successfully interrogated the spatial localization and chemical speciation of Gd compounds and also identified breakdown products of Gd species.

CONCLUSIONS

We have introduced a new macrocyclic cancer-targeted Gd chelate that achieves broad-spectrum tumor uptake and prolonged retention. Furthermore, we have demonstrated in vivo stability of Gd-NM600 by ultrahigh resolution MS tissue imaging. A tumor-targeted contrast agent coupled with the enhanced imaging resolution of MRI relative to positron emission tomography may transform oncologic imaging.

Gadolinium-Based Contrast Agent Attenuation Does Not Impact PET Quantification in Simultaneous Dynamic Contrast Enhanced Breast PET/MR

PURPOSE

Simultaneous PET/MR imaging involves injection of a radiopharmaceutical and often also includes administration of a gadolinium-based contrast agent (GBCA). Phantom model studies indicate that attenuation of annihilation photons by GBCAs does not bias quantification metrics of PET radiopharmaceutical uptake. However, a direct comparison of attenuation corrected PET values before and after administration of GBCA has not been performed in patients imaged with simultaneous dynamic PET/MR. The purpose of this study was to investigate the attenuating effect of GBCAs on standardized uptake value (SUV) quantification of ¹⁸ F-fluorodeoxyglucose (FDG) uptake in invasive breast cancer and normal tissues using simultaneous PET/MR.

METHODS

The study included 13 women with newly diagnosed invasive breast cancer imaged using simultaneous dedicated prone breast PET/MR with FDG. PET data collection and two-point Dixon based MR attenuation correction sequences began simultaneously before the administration of GBCA to avoid a potential impact of GBCA on the attenuation correction map. A standard clinical dose of GBCA was intravenously administered for the dynamic contrast enhanced MR sequences obtained during the simultaneous PET data acquisition. PET data were dynamically reconstructed into 60 frames of 30 seconds each. Three timing windows were chosen consisting of a single frame (30 seconds), two frames (60 seconds), or four frames (120 seconds) immediately before and after contrast administration. SUV_max and SUV_mean of the biopsy-proven breast malignancy, fibroglandular tissue of the contralateral normal breast, descending aorta, and liver were calculated prior to and following GBCA administration. Percent change in the SUV metrics were calculated to test for a statistically significant, non-zero percent change using Wilcoxon signed-rank tests.

RESULTS

No statistical change in SUV_max or SUV_mean was found for the breast malignancies or normal anatomical regions during the timing windows before and after GBCA administration.

CONCLUSIONS

GBCAs do not significantly impact the results of PET quantification by means of additional attenuation. However, GBCAs may still affect quantification by affecting MR acquisitions used for MR-based attenuation correction which this study did not address. Corrections to account for attenuation due to clinical concentrations of GBCAs are not necessary in simultaneous PET/MR examinations when MR-based attenuation correction sequences are performed prior to GBCA administration. This article is protected by copyright. All rights reserved.

18F-SynVesT-1 PET/MR Imaging of the Effect of Gut Microbiota on Synaptic Density and Neurite Microstructure: A Preclinical Pilot Study

The gut microbiome profoundly influences brain structure and function. The gut microbiome is hypothesized to play a key role in the etiopathogenesis of neuropsychiatric and neurodegenerative illness; however, the contribution of an intact gut microbiome to quantitative neuroimaging parameters of brain microstructure and function remains unknown. Herein, we report the broad and significant influence of a functional gut microbiome on commonly employed neuroimaging measures of diffusion tensor imaging (DTI), neurite orientation dispersion and density (NODDI) imaging, and SV2A ¹⁸F-SynVesT-1 synaptic density PET imaging when compared to germ-free animals. In this pilot study, we demonstrate that mice, in the presence of a functional gut microbiome, possess higher neurite density and orientation dispersion and decreased synaptic density when compared to age- and sex-matched germ-free mice. Our results reveal the region-specific structural influences and synaptic changes in the brain arising from the presence of intestinal microbiota. Further, our study highlights important considerations for the development of quantitative neuroimaging biomarkers for precision imaging in neurologic and psychiatric illness.

[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.

Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging

Head motion during brain PET imaging can cause significant degradation of the quality of the reconstructed image, leading to reduced diagnostic value and inaccurate quantitation. A fully data-driven motion correction approach was recently demonstrated to produce highly accurate motion estimates (< 1 mm) with high temporal resolution (≥ 1 Hz), which can then be used for a motion corrected reconstruction. This can be applied retrospectively with no impact on the clinical image acquisition protocol. We present a reader-based evaluation and an atlas-based quantitative analysis of this motion correction approach within a clinical cohort. Methods: Clinical patient data were collected over 2019-2020 and processed retrospectively. Motion estimation was performed using image-based registration on reconstructions of ultra-short frames (0.6-1.8 s), after which fully motion corrected list-mode reconstructions were performed. Two readers graded the motion corrected and uncorrected reconstructions. An atlas-based quantitative analysis was performed. Paired Wilcoxon tests were used to test for significant differences in the reader scores and standard uptake values between the reconstructions. Levene's test was used to test whether motion correction had a greater impact on the quantitation in the presence of motion than when low motion was observed. Results: 50 standard clinical ¹⁸F-fluorodeoxyglucose brain PET data sets (age range 13-83 years, mean age ± standard deviation 59 ± 20 years, 27 women) from 3 scanners were collected. The reader study showed a significantly different, diagnostically relevant improvement by motion correction for cases where motion was present (P = 0.02) and no impact in low motion cases. 8% of all data sets improved from diagnostically "unacceptable" to "acceptable". The atlas-based analysis demonstrated a significant difference between the motion corrected and uncorrected reconstructions in cases of high motion for 7 of 8 ROIs (P < 0.05). Conclusion: The proposed data-driven motion estimation and correction approach demonstrated a clinically significant impact on brain PET image reconstruction.

Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance-Only Radiation Therapy Planning Using Perceptual Loss Models

PURPOSE

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region.

METHODS AND MATERIALS

Two models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT.

RESULTS

The sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image.

CONCLUSIONS

This study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.

Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography

Recent developments in artificial intelligence (AI) technology have enabled new developments that can improve attenuation and scatter correction in PET and single-photon emission computed tomography (SPECT). These technologies will enable the use of accurate and quantitative imaging without the need to acquire a computed tomography image, greatly expanding the capability of PET/MR imaging, PET-only, and SPECT-only scanners. The use of AI to aid in scatter correction will lead to improvements in image reconstruction speed, and improve patient throughput. This article outlines the use of these new tools, surveys contemporary implementation, and discusses their limitations.

Anatomy and Physiology of Artificial Intelligence in PET Imaging

Artificial intelligence (AI) has seen an explosion in interest within nuclear medicine. This interest is driven by the rapid progress and eye-catching achievements of machine learning algorithms. The growing foothold of AI in molecular imaging is exposing nuclear medicine personnel to new technology and terminology. Clinicians and researchers can be easily overwhelmed by numerous architectures and algorithms that have been published. This article dissects the backbone of most AI algorithms: the convolutional neural network. The algorithm training workflow and the key ingredients and operations of a convolutional neural network are described in detail. Finally, the ubiquitous U-Net is explained step-by-step.

Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging

PURPOSE

Data-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultrashort frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time.

METHODS

Data from 18 F-fluorodeoxyglucose (FDG) and 18 F-florbetaben (FBB) tracer studies with varying count rates are analyzed, for PET/MR and PET/CT scanners. For framed reconstructions using various parameter combinations interframe motion is simulated and image-based registrations are performed to estimate that motion.

RESULTS

For FDG and FBB tracers using 4 × 105 true and scattered coincidence events per frame ensures that 95% of the registrations will be accurate to within 1 mm of the ground truth. This corresponds to a frame duration of 0.5-1 sec for typical clinical PET activity levels. Using four MLEM iterations with no subsets, a transaxial pixel size of 4 mm, a post-smoothing filter with 4-6 mm full width at half maximum, and averaging two or more frames to create the reference image provides an optimal set of parameters to produce accurate registrations while keeping the reconstruction and processing time low.

CONCLUSIONS

It is shown that very short frames (≤1 sec) can be used to provide accurate and quick data-driven rigid motion estimates for use in an event-by-event motion corrected reconstruction.

Dynamic FDG PET Imaging to Probe for Cardiac Metabolic Remodeling in Adults Born Premature

Individuals born very premature have an increased cardiometabolic and heart failure risk. While the structural differences of the preterm heart are now well-described, metabolic insights into the physiologic mechanisms underpinning this risk are needed. Here, we used dynamic fluorodeoxyglucose (FDG) positron emission tomography/magnetic resonance imaging (PET-MRI) in young adults born term and preterm during normoxic (N = 28 preterm; 18 term) and hypoxic exposure (12% O2; N = 26 preterm; 17 term) to measure the myocardial metabolic rate of glucose (MMRglc) in young adults born term (N = 18) and preterm (N = 32), hypothesizing that young adults born preterm would have higher rates of MMRglc under normoxic conditions and a reduced ability to augment glucose metabolism under hypoxic conditions. MMRglc was calculated from the myocardial and blood pool time-activity curves by fitting the measured activities to the 3-compartment model of FDG kinetics. MMRglc was similar at rest between term and preterm subjects, and decreased during hypoxia exposure in both groups (p = 0.02 for MMRglc hypoxia effect). There were no differences observed between groups in the metabolic response to hypoxia, either globally (serum glucose and lactate measures) or within the myocardium. Thus, we did not find evidence of altered myocardial metabolism in the otherwise healthy preterm-born adult. However, whether subtle changes in myocardial metabolism may preceed or predict heart failure in this population remains to be determined.

Application and Construction of Deep Learning Networks in Medical Imaging

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems, such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this article reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.

Measuring Glucose Uptake in Primary Invasive Breast Cancer Using Simultaneous Time-of-Flight Breast PET/MRI: A Method Comparison Study with Prone PET/CT

Purpose

To compare the measurement of glucose uptake in primary invasive breast cancer using simultaneous, time-of-flight breast PET/MRI with prone time-of-flight PET/CT.

Materials and Methods

In this prospective study, women with biopsy-proven invasive breast cancer undergoing preoperative breast MRI from 2016 to 2018 were eligible. Participants who had fasted underwent prone PET/CT of the breasts approximately 60 minutes after injection of 370 MBq (10 mCi) fluorine 18 fluorodeoxyglucose (18F-FDG) followed by prone PET/MRI using standard clinical breast MRI sequences performed simultaneously with PET acquisition. Volumes of interest were drawn for tumors and contralateral normal breast fibroglandular tissue to calculate standardized uptake values (SUVs). Spearman correlation, Wilcoxon signed ranked test, Mann-Whitney test, and Bland-Altman analyses were performed.

Results

Twenty-three women (mean age, 50 years; range, 33-70 years) were included. Correlation between tumor uptake values measured with PET/MRI and PET/CT was strong (r s = 0.95-0.98). No difference existed between modalities for tumor maximum SUV (SUVmax) normalized to normal breast tissue SUVmean (normSUVmax) (P = .58). The least amount of measurement bias was observed with normSUVmax, +3.86% (95% limits of agreement: -28.92, +36.64).

Conclusion

These results demonstrate measurement agreement between PET/CT, the current reference standard for tumor glucose uptake quantification, and simultaneous time-of-flight breast 18F-FDG PET/MRI.Keywords: Breast, Comparative Studies, PET/CT, PET/MR Supplemental material is available for this article. © RSNA, 2021See also the commentary by Mankoff and Surti in this issue.

Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs

There has been substantial interest in developing techniques for synthesizing CT-like images from MRI inputs, with important applications in simultaneous PET/MR and radiotherapy planning. Deep learning has recently shown great potential for solving this problem. The goal of this research was to investigate the capability of four common clinical MRI sequences (T1-weighted gradient-echo [T1], T2-weighted fat-suppressed fast spin-echo [T2-FatSat], post-contrast T1-weighted gradient-echo [T1-Post], and fast spin-echo T2-weighted fluid-attenuated inversion recovery [CUBE-FLAIR]) as inputs into a deep CT synthesis pipeline. Data were obtained retrospectively in 92 subjects who had undergone an MRI and CT scan on the same day. The patient's MR and CT scans were registered to one another using affine registration. The deep learning model was a convolutional neural network encoder-decoder with skip connections similar to the U-net architecture and Inception V3 inspired blocks instead of sequential convolution blocks. After training with 150 epochs and a batch size of 6, the model was evaluated using structural similarity index (SSIM), peak SNR (PSNR), mean absolute error (MAE), and dice coefficient. We found that feasible results were attainable for each image type, and no single image type was superior for all analyses. The MAE (in HU) of the resulting synthesized CT in the whole brain was 51.236 ± 4.504 for CUBE-FLAIR, 45.432 ± 8.517 for T1, 44.558 ± 7.478 for T1-Post, and 45.721 ± 8.7767 for T2, showing not only feasible, but also very compelling results on clinical images. Deep learning-based synthesis of CT images from MRI is possible with a wide range of inputs, suggesting that viable images can be created from a wide range of clinical input types.

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.

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.

Rapid development of application-specific flexible MRI receive coils

Over the last 30 years, there have been dramatic changes in phased array coil technology leading to increasing channel density and parallel imaging functionality. Current receiver array coils are rigid and often mismatched to patient's size. Recently there has been a move towards flexible coil technology, which is more conformal to the human anatomy. Despite the advances of so-called flexible surface coil arrays, these coils are still relatively rigid and limited in terms of design conformability, compromising signal-to-noise ratio (SNR) for flexibility, and are not designed for optimum parallel imaging performance. The purpose of this study is to report on the development and characterization of a 15-channel flexible foot and ankle coil, rapidly designed and constructed using highly decoupled radio-frequency (RF) coil elements. Coil performance was evaluated by performing SNR and g-factor measurements. In vivo testing was performed in a healthy volunteer using both the 15-channel coil and a commercially available 8-channel foot coil. The highly decoupled elements used in this design allow for extremely rapid development and prototyping of application-specific coils for different patient sizes (adult vs child) with minimal additional design consideration in terms of coil overlap and geometry. Image quality was comparable to a commercially available RF coil.

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.

Summary of the First ISMRM-SNMMI Workshop on PET/MRI: Applications and Limitations

Since the introduction of simultaneous PET/MRI in 2011, there have been significant advancements. In this review, we highlight several technical advancements that have been made primarily in attenuation and motion correction and discuss the status of multiple clinical applications using PET/MRI. This review is based on the experience at the first PET/MRI conference cosponsored by the International Society for Magnetic Resonance in Medicine and the Society of Nuclear Medicine and Molecular Imaging.

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.

A deep learning approach for 18F-FDG PET attenuation correction

Background

To develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously valued pseudo-computed tomography (CT) images from uncorrected ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET images. A deep convolutional encoder-decoder network was trained to identify tissue contrast in volumetric uncorrected PET images co-registered to CT data. A set of 100 retrospective 3D FDG PET head images was used to train the model. The model was evaluated in another 28 patients by comparing the generated pseudo-CT to the acquired CT using Dice coefficient and mean absolute error (MAE) and finally by comparing reconstructed PET images using the pseudo-CT and acquired CT for attenuation correction. Paired-sample t tests were used for statistical analysis to compare PET reconstruction error using deepAC with CT-based attenuation correction.

Results

deepAC produced pseudo-CTs with Dice coefficients of 0.80 ± 0.02 for air, 0.94 ± 0.01 for soft tissue, and 0.75 ± 0.03 for bone and MAE of 111 ± 16 HU relative to the PET/CT dataset. deepAC provides quantitatively accurate ¹⁸F-FDG PET results with average errors of less than 1% in most brain regions.

Conclusions

We have developed an automated approach (deepAC) that allows generation of a continuously valued pseudo-CT from a single ¹⁸F-FDG non-attenuation-corrected (NAC) PET image and evaluated it in PET/CT brain imaging.

Feasibility of Deep Learning-Based PET/MR Attenuation Correction in the Pelvis Using Only Diagnostic MR Images

This study evaluated the feasibility of using only diagnostically relevant magnetic resonance (MR) images together with deep learning for positron emission tomography (PET)/MR attenuation correction (deepMRAC) in the pelvis. Such an approach could eliminate dedicated MRAC sequences that have limited diagnostic utility but can substantially lengthen acquisition times for multibed position scans. We used axial T2 and T1 LAVA Flex magnetic resonance imaging images that were acquired for diagnostic purposes as inputs to a 3D deep convolutional neural network. The network was trained to produce a discretized (air, water, fat, and bone) substitute computed tomography (CT) (CTsub). Discretized (CTref-discrete) and continuously valued (CTref) reference CT images were created to serve as ground truth for network training and attenuation correction, respectively. Training was performed with data from 12 subjects. CTsub, CTref, and the system MRAC were used for PET/MR attenuation correction, and quantitative PET values of the resulting images were compared in 6 test subjects. Overall, the network produced CTsub with Dice coefficients of 0.79 ± 0.03 for cortical bone, 0.98 ± 0.01 for soft tissue (fat: 0.94 ± 0.0; water: 0.88 ± 0.02), and 0.49 ± 0.17 for bowel gas when compared with CTref-discrete. The root mean square error of the whole PET image was 4.9% by using deepMRAC and 11.6% by using the system MRAC. In evaluating 16 soft tissue lesions, the distribution of errors for maximum standardized uptake value was significantly narrower using deepMRAC (-1.0% ± 1.3%) than using system MRAC method (0.0% ± 6.4%) according to the Brown-Forsy the test (P < .05). These results indicate that improved PET/MR attenuation correction can be achieved in the pelvis using only diagnostically relevant MR images.

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.

Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging

PURPOSE

In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition.

METHODS

MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 s). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on eight human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image.

RESULT

Dice coefficients for air (within the head), soft tissue, and bone labels were 0.76 ± 0.03, 0.96 ± 0.006, and 0.88 ± 0.01. In PET quantitation, the proposed MRAC method produced relative PET errors less than 1% within most brain regions.

CONCLUSION

The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantitation with accurate and rapid pseudo CT generation.

Dielectric properties of 3D-printed materials for anatomy specific 3D-printed MRI coils

Additive manufacturing provides a low-cost and rapid means to translate 3D designs into the construction of a prototype. For MRI, this type of manufacturing can be used to construct various components including the structure of RF coils. In this paper, we characterize the material properties (dielectric constant and loss tangent) of several common 3D-printed polymers in the MRI frequency range of 63-300 MHz (for MRI magnetic field strengths of 1.5-7 T), and utilize these material properties in full-wave electromagnetic simulations to design and construct a very low-cost subject/anatomy-specific 3D-printed receive-only RF coil that fits close to the body. We show that the anatomy-specific coil exhibits higher signal-to-noise ratio compared to a conventional flat surface coil.

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.

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.

Ramped hybrid encoding for improved ultrashort echo time imaging

PURPOSE

We propose a new acquisition to minimize the per-excitation encoding duration and improve the imaging capability for short T2 * species.

METHODS

In the proposed ramped hybrid encoding (RHE) technique, gradients are applied before the radiofrequency (RF) pulse as in pointwise encoding time reduction with radial acquisition (PETRA) and zero echo time (ZTE) imaging. However, in RHE, gradients are rapidly ramped after RF excitation to the maximum amplitude to minimize encoding duration. To acquire central k-space data not measured during RF deadtime, RHE uses a hybrid encoding scheme similar to PETRA. A new gradient calibration method based on single-point imaging was developed to estimate the k-space trajectory and enable robust and high quality reconstruction.

RESULTS

RHE enables a shorter per-excitation encoding time and provides the highest spatial resolution among ultrashort T2 * imaging methods. In phantom and in vivo experiments, RHE exhibited robust imaging with negligible chemical shift or blurriness caused by T2 * decay and unwanted slice selection.

CONCLUSION

RHE allows the shortest per-excitation encoding time for ultrashort T2 * imaging, which alleviates the impact of fast T2 * decay occurring during encoding, and enables improved spatial resolution. Magn Reson Med 76:814-825, 2016. © 2015 Wiley Periodicals, Inc.

Accelerated 4D quantitative single point EPR imaging using model-based reconstruction

PURPOSE

Electron paramagnetic resonance imaging has surfaced as a promising noninvasive imaging modality that is capable of imaging tissue oxygenation. Due to extremely short spin-spin relaxation times, electron paramagnetic resonance imaging benefits from single-point imaging and inherently suffers from limited spatial and temporal resolution, preventing localization of small hypoxic tissues and differentiation of hypoxia dynamics, making accelerated imaging a crucial issue.

METHODS

In this study, methods for accelerated single-point imaging were developed by combining a bilateral k-space extrapolation technique with model-based reconstruction that benefits from dense sampling in the parameter domain (measurement of the T2 (*) decay of a free induction delay). In bilateral kspace extrapolation, more k-space samples are obtained in a sparsely sampled region by bilaterally extrapolating data from temporally neighboring k-spaces. To improve the accuracy of T2 (*) estimation, a principal component analysis-based method was implemented.

RESULTS

In a computer simulation and a phantom experiment, the proposed methods showed its capability for reliable T2 (*) estimation with high acceleration (8-fold, 15-fold, and 30-fold accelerations for 61×61×61, 95×95×95, and 127×127×127 matrix, respectively).

CONCLUSION

By applying bilateral k-space extrapolation and model-based reconstruction, improved scan times with higher spatial resolution can be achieved in the current single-point electron paramagnetic resonance imaging modality.

Single acquisition quantitative single-point electron paramagnetic resonance imaging

PURPOSE

Electron paramagnetic resonance imaging has emerged as a promising noninvasive technology to dynamically image tissue oxygenation. Owing to its extremely short spin-spin relaxation times, electron paramagnetic resonance imaging benefits from a single-point imaging scheme where the entire free induction decay signal is captured using pure phase encoding. However, direct T2 (*)/pO2 quantification is inhibited owing to constant magnitude gradients which result in time-decreasing field of view. Therefore, conventional acquisition techniques require repeated imaging experiments with differing gradient amplitudes (typically 3), which results in long acquisition time.

METHODS

In this study, gridding was evaluated as a method to reconstruct images with equal field of view to enable direct T2 (*)/pO2 quantification within a single imaging experiment. Additionally, an enhanced reconstruction technique that shares high spatial k-space regions throughout different phase-encoding time delays was investigated (k-space extrapolation).

RESULTS

The combined application of gridding and k-space extrapolation enables pixelwise quantification of T2 (*) from a single acquisition with improved image quality across a wide range of phase-encoding time delays. The calculated T2 (*)/pO2 does not vary across this time range.

CONCLUSIONS

By utilizing gridding and k-space extrapolation, accurate T2 (*)/pO2 quantification can be achieved within a single data set to allow enhanced temporal resolution (by a factor of 3).

Location of myofiber damage in skeletal muscle after lengthening contractions

High-force lengthening contractions are associated with muscle damage and pain, and the muscle-tendon junction is commonly cited as the primary area where myofiber damage occurs. We induced injury in the rat tibialis anterior muscle and acquired magnetic resonance imaging (MRI) images postinjury. We also assayed membrane damage and quantified the number of centrally nucleated myofibers throughout the injured muscles. Results suggest that myofiber injury occurs primarily in the middle portion of the muscle, with interstitial edema in the middle and distal portions.

Independent component analysis applied to self-paced functional MR imaging paradigms

Self-paced functional MR imaging (fMRI) paradigms, in which the task timing is determined by the subject's performance, can offer several advantages over commonly applied paradigms with predetermined stimulus timing. Independent component analysis (ICA) does not require specification of a timed response function, and could be an advantageous method of deriving results from fMRI data sets with varying response timings and durations. In this study normal volunteers (N = 10) each performed two self-paced fMRI motor and arithmetic paradigms. Individual data sets were analyzed with the Infomax spatial ICA algorithm. Conventional regression analysis was performed for comparison purposes. Spatial ICA effectively produced task-related components from each of the self-paced data sets, even in a few cases where regression analysis yielded non-specific functional maps. For the motor paradigm, these components consistently mapped to primary motor areas. ICA of the arithmetic paradigm yielded multiple task-related components that variably mapped to regions of parietal and frontal lobes. Regression analysis generally yielded similar spatial maps. The multiple task-related ICA components that were sometimes produced from each self-paced data set can be challenging to identify and evaluate for significance. These preliminary results indicate that ICA is useful as an exploratory and complementary method to conventional regression analysis for fMRI of self-paced paradigms.

Voxel-based morphometry of unilateral temporal lobe epilepsy reveals abnormalities in cerebral white matter

Voxel-based morphometric (VBM) investigations of temporal lobe epilepsy have focused on the presence and distribution of gray matter abnormalities. VBM studies to date have identified the expected abnormalities in hippocampus and extrahippocampal temporal lobe, as well as more diffuse abnormalities in the thalamus, cerebellum, and extratemporal neocortical areas. To date, there has not been a comprehensive VBM investigation of cerebral white matter in nonlesional temporal lobe epilepsy. This study examined 25 lateralized temporal lobe epilepsy patients (13 left, 12 right) and 62 healthy controls in regard to both temporal and extratemporal lobe gray and white matter. Consistent with prior reports, gray matter abnormalities were evident in ipsilateral hippocampus and ipsilateral thalamus. Temporal and extratemporal white matter was affected ipsilateral to the side of seizure onset, in both left and right temporal lobe epilepsy groups. These findings indicate that chronic temporal lobe epilepsy is associated not only with abnormalities in gray matter, but also with concomitant abnormalities in cerebral white matter regions that may affect connectivity both within and between the cerebral hemispheres.

Membrane and bubble oxygenators compared by preservation of myocardial function

Membrane oxygenators reputedly preserve erythrocytes, platelets, kidneys, brain and lungs better than bubble oxygenators; preservation of ventricular function by the two types of oxygenator is compared in isolated hearts, extremely sensitive to imperfections of perfusion, by isovolumic tests which are simple and accurate, especially for evaluating compliance. Canine hearts were perfused for three hours with either disposable bubble (Temptrol) or membrane (Lande-Edwards) oxygenators. Values at a standard point on regression slopes os isovolumic contractile force, velocity and compliance (volume) against end-diastolic pressure were used to express final functions as percentages of initial ones. Terminal proportional ventricular weight was an index of oedema. The final mean percentages with standard error measurements of initial values for the 12 hearts perfused on buble oxygenators and the 10 on membrane oxygenators were, respectively: 97 +/- 11.5% and 87 +/- 10.7% for contractile force, 117 +/- 23.1% and 88 +/- 10.44% for contractile velocity, and 97.2 +/- 8.48% and 117.3 +/- 12.5% for ventricular compliance, which was the function nearest to showing a significant difference with P less than 0.1. There was no significant difference in weights. This membrane oxygenator, as cheap and simple as conventional ones, probably has similar advantages for the myocardium as for other tissues.

Protection of the ischaemic myocardium by glucose-insulin-potassium infusion assessed by ventricular function and electron microscopy

The protective effect of GIK infusion on the ischaemic myocardium was assessed by isovolumic function tests and electron microscopy. There were two control groups, group 1 hearts underwent 2 hours of continous normothermic cross-perfusion and group 2 hearts endured 2 hours of ischaemia. Group 3 hearts were administered GIK solution prior to ischaemic arrest. The group 3 hearts showed less loss of contractile force and contractile velocity from the ischaemic period compared to group 2 hearts, but no benfit was shown in terms of compliance. Electron microscopic examination showed slightly less damage in the group 3 hearts compared to group 2 hearts. Group 1 hearts maintained better function than either of the other two groups. Ultrastructure was not examined in group 1 hearts. A slight protective effect of GIK on the ischaemic myocardium was thus confirmed.