Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance.

The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.

Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.

BACKGROUND: For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would reduce resulting artifacts in the reconstructed images. PURPOSE: This work presents a deep learning technique for inter-modality, elastic registration of PET/CT images for improving PET attenuation correction (AC). The feasibility of the technique is demonstrated for two applications: general whole-body (WB) imaging and cardiac myocardial perfusion imaging (MPI), with a specific focus on respiratory and gross voluntary motion. MATERIALS AND METHODS: A convolutional neural network (CNN) was developed and trained for the registration task, comprising two distinct modules: a feature extractor and a displacement vector field (DVF) regressor. It took as input a non-attenuation-corrected PET/CT image pair and returned the relative DVF between them-it was trained in a supervised fashion using simulated inter-image motion. The 3D motion fields produced by the network were used to resample the CT image volumes, elastically warping them to spatially match the corresponding PET distributions. Performance of the algorithm was evaluated in different independent sets of WB clinical subject data: for recovering deliberate misregistrations imposed in motion-free PET/CT pairs and for improving reconstruction artifacts in cases with actual subject motion. The efficacy of this technique is also demonstrated for improving PET AC in cardiac MPI applications. RESULTS: A single registration network was found to be capable of handling a variety of PET tracers. It demonstrated state-of-the-art performance in the PET/CT registration task and was able to significantly reduce the effects of simulated motion imposed in motion-free, clinical data. Registering the CT to the PET distribution was also found to reduce various types of AC artifacts in the reconstructed PET images of subjects with actual motion. In particular, liver uniformity was improved in the subjects with significant observable respiratory motion. For MPI, the proposed approach yielded advantages for correcting artifacts in myocardial activity quantification and potentially for reducing the rate of the associated diagnostic errors. CONCLUSION: This study demonstrated the feasibility of using deep learning for registering the anatomical image to improve AC in clinical PET/CT reconstruction. Most notably, this improved common respiratory artifacts occurring near the lung/liver border, misalignment artifacts due to gross voluntary motion, and quantification errors in cardiac PET imaging.

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.

Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.

Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).

An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development.

The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.

Deep-Learning 18F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma.

Total metabolic tumor volume (TMTV), calculated from 18F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) whose measurement requires the segmentation of all malignant foci throughout the body. No consensus currently exists regarding the most accurate approach for such segmentation. Further, all methods still require extensive manual input from an experienced reader. We examined whether an artificial intelligence-based method could estimate TMTV with a comparable prognostic value to TMTV measured by experts. Methods: Baseline 18F-FDG PET/CT scans of 301 DLBCL patients from the REMARC trial (NCT01122472) were retrospectively analyzed using a prototype software (PET Assisted Reporting System [PARS]). An automated whole-body high-uptake segmentation algorithm identified all 3-dimensional regions of interest (ROIs) with increased tracer uptake. The resulting ROIs were processed using a convolutional neural network trained on an independent cohort and classified as nonsuspicious or suspicious uptake. The PARS-based TMTV (TMTVPARS) was estimated as the sum of the volumes of ROIs classified as suspicious uptake. The reference TMTV (TMTVREF) was measured by 2 experienced readers using independent semiautomatic software. The TMTVPARS was compared with the TMTVREF in terms of prognostic value for progression-free survival (PFS) and overall survival (OS). Results: TMTVPARS was significantly correlated with the TMTVREF (ρ = 0.76; P < 0.001). Using PARS, an average of 24 regions per subject with increased tracer uptake was identified, and an average of 20 regions per subject was correctly identified as nonsuspicious or suspicious, yielding 85% classification accuracy, 80% sensitivity, and 88% specificity, compared with the TMTVREF region. Both TMTV results were predictive of PFS (hazard ratio, 2.3 and 2.6 for TMTVPARS and TMTVREF, respectively; P < 0.001) and OS (hazard ratio, 2.8 and 3.7 for TMTVPARS and TMTVREF, respectively; P < 0.001). Conclusion: TMTVPARS was consistent with that obtained by experts and displayed a significant prognostic value for PFS and OS in DLBCL patients. Classification of high-uptake regions using deep learning for rapidly discarding physiologic uptake may considerably simplify TMTV estimation, reduce observer variability, and facilitate the use of TMTV as a predictive factor in DLBCL patients.

Single-site 123I-FP-CIT reference values from individuals with non-degenerative parkinsonism-comparison with values from healthy volunteers.

PURPOSE: Iodine 123-radiolabeled 2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (123I-FP-CIT) SPECT can be performed to distinguish degenerative forms of movement disorders/parkinsonism/tremor from other entities such as idiopathic tremor or drug-induced parkinsonism. For equivocal cases, semi-quantification and comparison to reference values are a necessary addition to visual interpretation of 123I-FP-CIT scans. To overcome the challenges of multi-center recruitment and scanning of healthy volunteers, we generated 123I-FP-CIT reference values from individuals with various neurological conditions but without dopaminergic degeneration, scanned at a single center on the same SPECT-CT system following the same protocol, and compared them to references from a multi-center database built using healthy volunteers' data. METHODS: From a cohort of 1884 patients, we identified 237 subjects (120 men, 117 women, age range 16-88 years) through a two-stage selection process. Every patient had a final clinical diagnosis after a mean follow-up of 4.8 ± 1.3 years. Images were reconstructed using (1) Flash3D with scatter and CT-based attenuation corrections (AC) and (2) filtered back projection with Chang AC. Volume-of-interest analysis was performed using a commercial software to calculate specific binding ratios (SBRs), caudate-to-putamen ratios, and asymmetry values on different striatal regions. Generated reference values were assessed according to age and gender and compared with those from the ENC-DAT study, and their robustness was tested against a cohort of patients with different diagnoses. RESULTS: Age had a significant negative linear effect on all SBRs. Overall, the reduction rate per decade in SBR was between 3.80 and 5.70%. Women had greater SBRs than men, but this gender difference was only statistically significant for the Flash3D database. Linear regression was used to correct for age-dependency of SBRs and to allow comparisons to age-matched reference values and "normality" limits. Generated regression parameters and their 95% confidence intervals (CIs) were comparable to corresponding European Normal Control Database of DaTscan (ENC-DAT) results. For example, 95% CI mean slope for the striatum in women is - 0.015 ([- 0.019, - 0.011]) for the Flash3D database versus - 0.015 ([- 0.021, - 0.009]) for ENC-DAT. Caudate-to-putamen ratios and asymmetries were not influenced by age or gender. CONCLUSION: The generated 123I-FP-CIT references values have similar age-related distribution, with no increase in variance due to comorbidities when compared to values from a multi-center study with healthy volunteers. This makes it possible for sites to build their 123I-FP-CIT references from scans acquired during routine clinical practice.

18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks.

Background Fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT is a routine tool for staging patients with lymphoma and lung cancer. Purpose To evaluate configurations of deep convolutional neural networks (CNNs) to localize and classify uptake patterns of whole-body 18F-FDG PET/CT images in patients with lung cancer and lymphoma. Materials and Methods This was a retrospective analysis of consecutive patients with lung cancer or lymphoma referred to a single center from August 2011 to August 2013. Two nuclear medicine experts manually delineated foci with increased 18F-FDG uptake, specified the anatomic location, and classified these findings as suspicious for tumor or metastasis or nonsuspicious. By using these expert readings as the reference standard, a CNN was developed to detect foci positive for 18F-FDG uptake, predict the anatomic location, and determine the expert classification. Examinations were divided into independent training (60%), validation (20%), and test (20%) subsets. Results This study included 629 patients (mean age, 52.2 years ± 20.4 [standard deviation]; 394 men). There were 302 patients with lung cancer and 327 patients with lymphoma. For the test set (123 patients; 10 782 foci), the CNN areas under the receiver operating characteristic curve (AUCs) for determining hypermetabolic 18F-FDG PET/CT foci that were suspicious for cancer versus nonsuspicious by using the five input features were as follows: CT alone, 0.78 (95% confidence interval [CI]: 0.72, 0.83); 18F-FDG PET alone, 0.97 (95% CI: 0.97, 0.98); 18F-FDG PET/CT, 0.98 (95% CI: 0.97, 0.99); 18F-FDG PET/CT maximum intensity projection (MIP), 0.98 (95% CI: 0.98, 0.99); and 18F-FDG PET/CT MIP atlas, 0.99 (95% CI: 0.98, 1.00). The combination of 18F-FDG PET and CT information improved overall classification accuracy (AUC, 0.975 vs 0.981, respectively; P < .001). Anatomic localization accuracy of the CNN was 2543 of 2639 (96.4%; 95% CI: 95.5%, 97.1%) for body part, 2292 of 2639 (86.9%; 95% CI: 85.3%, 88.5%) for region (ie, organ), and 2149 of 2639 (81.4%; 95% CI: 79.3%-83.5%) for subregion. Conclusion The fully automated anatomic localization and classification of fluorine 18-fluorodeoxyglucose PET uptake patterns in foci suspicious and nonsuspicious for cancer in patients with lung cancer and lymphoma by using a convolutional neural network is feasible and achieves high diagnostic performance when both CT and PET images are used. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Froelich and Salavati in this issue.

Assessing Reliability of Myocardial Blood Flow After Motion Correction With Dynamic PET Using a Bayesian Framework.

The estimation of myocardial blood flow (MBF) in dynamic PET can be biased by many different processes. A major source of error, particularly in clinical applications, is patient motion. Patient motion, or gross motion, creates displacements between different PET frames as well as between the PET frames and the CT-derived attenuation map, leading to errors in MBF calculation from voxel time series. Motion correction techniques are challenging to evaluate quantitatively and the impact on MBF reliability is not fully understood. Most metrics, such as signal-to-noise ratio (SNR), are characteristic of static images, and are not specific to motion correction in dynamic data. This study presents a new approach of estimating motion correction quality in dynamic cardiac PET imaging. It relies on calculating a MBF surrogate, K1 , along with the uncertainty on the parameter. This technique exploits a Bayesian framework, representing the kinetic parameters as a probability distribution, from which the uncertainty measures can be extracted. If the uncertainty extracted is high, the parameter studied is considered to have high variability - or low confidence - and vice versa. The robustness of the framework is evaluated on simulated time activity curves to ensure that the uncertainties are consistently estimated at the multiple levels of noise. Our framework is applied on 40 patient datasets, divided in 4 motion magnitude categories. Experienced observers manually realigned clinical datasets with 3D translations to correct for motion. K1 uncertainties were compared before and after correction. A reduction of uncertainty after motion correction of up to 60% demonstrates the benefit of motion correction in dynamic PET and as well as provides evidence of the usefulness of the new method presented.

Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an IS-SENSE reconstruction.

Cardiac MR is considered the gold standard in assessing RV function. The purpose of this study is to evaluate the clinical utility of an investigational iterative reconstruction algorithm in the quantitative assessment of RV function. This technique has the potential to improve the clinical utility of CMR in the evaluation of RV pathologies, particularly in patients with dyspnea, by shortening acquisition times without adversely influencing imaging performance. Segmented cine images were acquired on 9 healthy volunteers and 29 patients without documented RV pathologies using conventional GRAPPA acquisition with factor 2 acceleration (GRAPPA 2), a spatio-temporal TSENSE acquisition with factor 4 acceleration (TSENSE 4), and iteratively reconstructed Sparse SENSE acquisition with factor 4 acceleration (IS-SENSE 4). 14 subjects were re-analyzed and intraclass correlation coefficients (ICC) were calculated and Bland-Altman plots generated to assess agreement. Two independent reviewers qualitatively scored images. Comparison of acquisition techniques was performed using univariate analysis of variance (ANOVA). Differences in RV EF, BSA-indexed ESV (ESVi), BSA-indexed EDV (EDVi), and BSA-indexed SV (SVi) were shown to be statistically insignificant via ANOVA testing. R(2) values for linear regression of TSENSE 4 and IS-SENSE 4 versus GRAPPA 2 were 0.34 and 0.72 for RV-EF, and 0.61 and 0.76 for RV-EDVi. ICC values for intraobserver and interobserver quantification yielded excellent agreement, and Bland-Altman plots assessing agreement were generated as well. Qualitative review yielded small, but statistically significant differences in image quality and noise between TSENSE 4 and IS-SENSE 4. All three techniques were rated nearly artifact free. Segmented imaging acquisitions with IS-SENSE reconstruction and an acceleration factor of 4 accurately and reliably quantitates RV systolic function parameters, while maintaining image quality. TSENSE-4 accelerated acquisitions showed poorer correlation to standard imaging, and inferior interobserver and intraobserver agreement. IS-SENSE has the potential to shorten cine acquisition times by 50 %, improving imaging options in patients with intermittent arrhythmias or difficulties with breath holding.

Steady-state first-pass perfusion (SSFPP): a new approach to 3D first-pass myocardial perfusion imaging.

PURPOSE: To describe and characterize a new approach to first-pass myocardial perfusion utilizing balanced steady-state free precession acquisition without the use of saturation recovery or other magnetization preparation. THEORY: The balanced steady-state free precession sequence is inherently sensitive to contrast agent enhancement of the myocardium. This sensitivity can be used to advantage in first-pass myocardial perfusion imaging by eliminating the need for magnetization preparation. METHODS: Bloch equation simulations, phantom experiments, and in vivo 2D imaging studies were run comparing the proposed technique with three other methods: saturation recovery spoiled gradient echo, saturation recovery steady-state free precession, and steady-state spoiled gradient echo without magnetization preparation. Additionally, an acquisition-reconstruction strategy for 3D perfusion imaging is proposed and initial experience with this approach is demonstrated in healthy subjects and one patient. RESULTS: Phantom experiments verified simulation results showing the sensitivity of the balanced steady-state free precession sequence to contrast agent enhancement in solid tissue is similar to that of magnetization-prepared acquisitions. Images acquired in normal volunteers showed the proposed technique provided superior signal and signal-to-noise ratio compared with all other sequences at baseline as well as postcontrast. CONCLUSIONS: A new approach to first-pass myocardial perfusion is presented that obviates the need for magnetization preparation and provides high signal-to-noise ratio.

Phase-sensitive inversion recovery for myocardial T1 mapping with motion correction and parametric fitting.

The assessment of myocardial fibrosis and extracellular volume requires accurate estimation of myocardial T1 s. While image acquisition using the modified Look-Locker inversion recovery technique is clinically feasible for myocardial T1 mapping, respiratory motion can limit its applicability. Moreover, the conventional T1 fitting approach using the magnitude inversion recovery images can lead to less stable T1 estimates and increased computational cost. In this article, we propose a novel T1 mapping scheme that is based on phase-sensitive image reconstruction and the restoration of polarity of the MR signal after inversion. The motion correction is achieved by registering the reconstructed images after background phase removal. The restored signal polarity of the inversion recovery signal helps the T1 fitting resulting in improved quality of the T1 map and reducing the computational cost. Quantitative validation on a data cohort of 45 patients proves the robustness of the proposed method against varying image contrast. Compared to the magnitude T1 fitting, the proposed phase-sensitive method leads to less fluctuation in T1 estimates.

Assessment of left ventricular myocardial scar in coronary artery disease by a three-dimensional MR imaging technique.

PURPOSE: To evaluate the feasibility of free-breathing three-dimensional (3D) phase sensitive inversion recovery (PSIR) Turbo FLASH late gadolinium enhancement (LGE) magnetic resonance images (MRI) on left ventricular scar in patients with coronary artery disease (CAD) compared with clinically established breathhold two-dimensional (2D) PSIR Turbo FLASH images. MATERIALS AND METHODS: In 58 consecutive patients with confirmed CAD, LGE MRI using the two sequences have been acquired. Image quality was graded on a four-point scale according to the image appearance. Qualitative evaluation including the distribution area and the transmural extent of the scar based on the American Heart Association's (AHA's) 17-segment model was performed in both of 2D and 3D images. The scar volumes were compared quantitatively between 2D and 3D images. RESULTS: A total of 51 individuals were used for final statistical analysis. No differences were noted in image quality (P = 0.80), scar distribution area (P = 0.17), and scar transmural extent (P = 0.20) between 3D and 2D images. There was strong correlation in scar volume between the 3D and 2D results (r = 0.940; P < 0.001; Y = 0.298 + 1.251X, R(2) = 0.876). But the scar volume derived from 3D images was significantly larger than that derived from 2D images (2D versus 3D, 20.08 ± 9.41 cm(3) versus 25.41 ± 12.57 cm(3) , t = -7.60; P < 0.001). The trend toward a larger scar volume identified by 3D method was indicated through Bland-Altman analysis. CONCLUSION: Free-breathing 3D PSIR Turbo FLASH imaging is another feasible method to identify left ventricular myocardial scar in patients with CAD and detects more scar volume compared with breathhold 2D PSIR Turbo FLASH imaging.

Myocardial T2 mapping with respiratory navigator and automatic nonrigid motion correction.

Quantitative T2 mapping was recently shown to be superior to T2-weighted imaging in detecting T2 changes across myocardium. Pixel-wise T2 mapping is sensitive to misregistration between the images used to generate the parameter map. In this study, utility of two motion-compensation strategies-(i) navigator gating with prospective slice correction and (ii) nonrigid registration-was investigated for myocardial T2 mapping in short axis and horizontal long axis views. Navigator gating provides respiratory motion compensation, whereas registration corrects for residual cardiac and respiratory motion between images; thus, the two strategies provided complementary functions. When these were combined, respiratory-motion-induced T2 variability, as measured by both standard deviation and interquartile range, was comparable to that in breath-hold T2 maps. In normal subjects, this combined motion-compensation strategy increased the percentage of myocardium with T2 measured to be within normal range from 60.1% to 92.2% in short axis and 62.3% to 92.7% in horizontal long axis. The new motion-compensated T2 mapping technique, which combines navigator gating, prospective slice correction, and nonrigid registration to provide through-plane and in-plane motion correction, enables a method for fully automatic and robust free-breathing T2 mapping.

MRI-guided biopsy to correlate tissue specimens with MR elastography stiffness readings in liver transplants.

RATIONALE AND OBJECTIVES: Magnetic resonance elastography (MRE) can noninvasively measure the stiffness of liver tissue and display this information in anatomic maps. Magnetic resonance imaging (MRI) guidance has not previously been used to biopsy segments of heterogeneous stiffness identified on MRE. Dedicated study of MRE in post-liver transplant patients is also limited. In this study, the ability of real-time MRI to guide biopsies of segments of the liver with different MRE stiffness values in the same post-transplant patient was assessed. MATERIALS AND METHODS: MRE was performed in 9 consecutive posttransplant patients with history of hepatitis C. Segments of highest and lower stiffness on MRE served as targets for subsequent real-time MRI-guided biopsy using T2-weighted imaging. The ability of MRI-guided biopsy to successfully obtain tissue specimens was assessed. The Wilcoxon signed-rank test was used to compare mean stiffness differences for highest and lower MRE stiffness segments, with α = 0.05. RESULTS: MRI guidance allowed successful sampling of liver tissue for all (18/18) biopsies. There was a statistically significant difference in mean MRE stiffness values between highest (4.61 ± 1.99 kPa) and lower stiffness (3.03 ± 1.75 kPa) (P = .0039) segments biopsied in the 9 posttransplant patients. CONCLUSION: Real-time MRI can guide biopsy in patients after liver transplantation based on MRE stiffness values. This study supports the use of MRI guidance to sample tissue based on functional information.

T2-weighted cardiac MR assessment of the myocardial area-at-risk and salvage area in acute reperfused myocardial infarction: comparison of state-of-the-art dark blood and bright blood T2-weighted sequences.

PURPOSE: To compare different state-of-the-art T2-weighted (T2w) imaging sequences combined with late gadolinium enhancement (LGE) for myocardial salvage area (MSA) assessment by cardiac magnetic resonance (CMR). T2w imaging has been used to assess the myocardial area at risk (AAR) in acute myocardial infarction (AMI) patients, but its clinical application is challenging due to technical and physical limitations. MATERIALS AND METHODS: Thirty patients with reperfused AMI underwent complete CMR imaging 2-5 days after hospital admission. Myocardial AAR and MSA were quantified on four different T2w sequences: (a) free-breathing T2-prepared single-shot balanced steady-state free precession (T2p_ssbSSFP); (b) breathhold T2-weighted acquisition for cardiac unified T2 edema (ACUTE); (c) breathhold T2w dark-blood inversion recovery turbo-spin echo (IR-TSE) (short-term inversion recovery: STIR); and (d) free-breathing high-resolution T2 dark-blood navigated BLADE. The diagnostic performance of each technique was also assessed. RESULTS: Quantitative analysis showed significant differences in myocardial AAR extent as quantified by the four T2w sequences (P < 0.05). There were also significant differences in sensitivity, specificity and overall diagnostic performance. CONCLUSION: Detection and quantification of AAR, and thus of MSA, by T2wCMR in reperfused AMI patients varied significantly between different T2w sequences in the same clinical setting.

Coronary wall MR imaging in patients with rapid heart rates: a feasibility study of black-blood steady-state free precession (SSFP).

We assessed the hypothesis that black-blood steady-state free precession (SSFP) would provide coronary wall images comparable to images from TSE and have better performance than TSE under conditions of fast heart rate. With IRB approval, thirty participants without a history of coronary artery disease (19 men, 11 women, 26-83 y/o) were scanned with a 1.5 T MR scanner. Cross-sectional black-blood images of the proximal portions of coronary arteries were acquired with a two-dimensional (2D), double inversion recovery (DIR) prepared TSE sequence and a 2D DIR SSFP sequence on the same planes. Image quality (ranked with a 4-point system, scored from 0 to 3), vessel wall area and thickness, signal-to-noise ratio (SNR) of the wall and contrast-to-noise ratio (CNR, wall to lumen) were compared between SSFP and TSE with SPSS software (v 13.0). Totally 28 scans were completed. For SSFP and TSE, there was no difference in image quality. SSFP had a higher SNR (23.7 ± 10.1 vs. 14.4 ± 5.2, P < 0.001) and wall-lumen CNR (8.8 ± 4.5 vs. 6.7 ± 3.2, P = 0.001). Good agreements between measured wall area (r = 0.701, P < 0.001) and thickness (r = 0.560, P < 0.001) were found. For 10 participants with heart rate more than 80 beats/min, the image quality of SSFP was higher than TSE (P = 0.016). SSFP provided image quality and measurement accuracy that was comparable to TSE. With its higher performance under fast heart rate conditions, SSFP may break through the existing thresholds for heart rate and extend clinical applicability of coronary wall MR imaging to a larger population.

Motion correction for myocardial T1 mapping using image registration with synthetic image estimation.

Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and nonischemic cardiomyopathies. Image acquisition using the modified Look-Locker inversion recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this article, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust nonrigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map.