TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames. The code is available at https://github.com/gxq1998/TAI-GAN.

Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA.

BACKGROUND: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. PURPOSE: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. METHODS: A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. RESULTS: Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6-1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0-1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00-1.00] and 0.99 [0.93-1.00] for calcium and uptake values, respectively). CONCLUSIONS: We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring.

Profiling and predicting distinct tau progression patterns: An unsupervised data-driven approach to flortaucipir positron emission tomography.

INTRODUCTION: How to detect patterns of greater tau burden and accumulation is still an open question. METHODS: An unsupervised data-driven whole-brain pattern analysis of longitudinal tau positron emission tomography (PET) was used first to identify distinct tau accumulation profiles and then to build baseline models predictive of tau-accumulation type. RESULTS: The data-driven analysis of longitudinal flortaucipir PET from studies done by the Alzheimer's Disease Neuroimaging Initiative, Avid Pharmaceuticals, and Harvard Aging Brain Study (N = 348 cognitively unimpaired, N = 188 mild cognitive impairment, N = 77 dementia), yielded three distinct flortaucipir-progression profiles: stable, moderate accumulator, and fast accumulator. Baseline flortaucipir levels, amyloid beta (Aß) positivity, and clinical variables, identified moderate and fast accumulators with 81% and 95% positive predictive values, respectively. Screening for fast tau accumulation and Aß positivity in early Alzheimer's disease, compared to Aß positivity with variable tau progression profiles, required 46% to 77% lower sample size to achieve 80% power for 30% slowing of clinical decline. DISCUSSION: Predicting tau progression with baseline imaging and clinical markers could allow screening of high-risk individuals most likely to benefit from a specific treatment regimen.

TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.

The rapid tracer kinetics of rubidium-82 (82Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable. Alternatively, a promising approach utilizes generative methods to handle the tracer distribution changes to assist existing registration methods. To improve frame-wise registration and parametric quantification, we propose a Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) to transform the early frames into the late reference frame using an all-to-one mapping. Specifically, a feature-wise linear modulation layer encodes channel-wise parameters generated from temporal tracer kinetics information, and rough cardiac segmentations with local shifts serve as the anatomical information. We validated our proposed method on a clinical 82Rb PET dataset and found that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, motion estimation accuracy and clinical myocardial blood flow (MBF) quantification were improved compared to using the original frames. Our code is published at https://github.com/gxq1998/TAI-GAN.

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network.

Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric Ki and Vb images, and significantly reduced parametric fitting error. We also showed the potential of the proposed motion correction method for impacting downstream analysis of the estimated parametric images, improving the ability to distinguish malignant from benign hypermetabolic regions of interest. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline, showing its potential to be easily applied in clinical settings.

Change in positron emission tomography perfusion imaging quality with a data-driven motion correction algorithm.

INTRODUCTION: Cardiac motion frequently reduces the interpretability of PET images. This study utilized a prototype data-driven motion correction (DDMC) algorithm to generate corrected images and compare DDMC images with non-corrected images (NMC) to evaluate image quality and change of perfusion defect size and severity. METHODS: Rest and stress images with NMC and DDMC from 40 consecutive patients with motion were rated by 2 blinded investigators on a 4-point visual ordinal scale (0: minimal motion; 1: mild motion; 2: moderate motion; 3: severe motion/uninterpretable). Motion was also quantified using Dwell Fraction, which is the fraction of time the motion vector shows the heart to be within 6 mm of the corrected position and was derived from listmode data of NMC images. RESULTS: Minimal motion was seen in 15% of patients, while 40%, 30%, and 15% of patients had mild moderate and severe motion, respectively. All corrected images showed an improvement in quality and were interpretable after processing. This was confirmed by a significant correlation (Spearman's correlation coefficient 0.626, P < .001) between machine measurement of motion quantification and physician interpretation. CONCLUSION: The novel DDMC algorithm improved quality of cardiac PET images with motion. Correlation between machine measurement of motion quantification and physician interpretation was significant.

Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning.

PURPOSE: In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers. METHODS: In 173 subjects imaged with 68Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework. RESULTS: In the development set, including 18F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1-87.8] for identification of suspicious uptake sites, 77% (CI: 70.0-83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage. CONCLUSION: The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.

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.

Positron emission tomography PET/CT harmonisation study of different clinical PET/CT scanners using commercially available software.

OBJECTIVES: Harmonisation is the process whereby standardised uptake values from different scanners can be made comparable. This PET/CT pilot study aimed to evaluate the effectiveness of harmonisation of a modern scanner with image reconstruction incorporating resolution recovery (RR) with another vendor older scanner operated in two-dimensional (2D) mode, and for both against a European standard (EARL). The vendor-proprietary software EQ•PET was used, which achieves harmonisation with a Gaussian smoothing. A substudy investigated effect of RR on harmonisation. METHODS: Phantom studies on each scanner were performed to optimise the smoothing parameters required to achieve successful harmonisation. 80 patients were retrospectively selected; half were imaged on each scanner. As proof of principle, a cohort of 10 patients was selected from the modern scanner subjects to study the effects of RR on harmonisation. RESULTS: Before harmonisation, the modern scanner without RR adhered to EARL specification. Using the phantom data, filters were derived for optimal harmonisation between scanners and with and without RR as applicable, to the EARL standard. The 80-patient cohort did not reveal any statistically significant differences. In the 10-patient cohort SUVmax for RR > no RR irrespective of harmonisation but differences lacked statistical significance (one-way ANOVA F(3.36) = 0.37, p = 0.78). Bland-Altman analysis showed that harmonisation reduced the SUVmax ratio between RR and no RR to 1.07 (95% CI 0.96-1.18) with no outliers. CONCLUSIONS: EQ•PET successfully enabled harmonisation between modern and older scanners and against the EARL standard. Harmonisation reduces SUVmax and dependence on the use of RR in the modern scanner. ADVANCES IN KNOWLEDGE: EQ•PET is feasible to harmonise different PET/CT scanners and reduces the effect of RR on SUVmax.

Semiautomatically Quantified Tumor Volume Using 68Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer.

Prostate-specific membrane antigen (PSMA)-targeting PET imaging is becoming the reference standard for prostate cancer staging, especially in advanced disease. Yet, the implications of PSMA PET-derived whole-body tumor volume for overall survival are poorly elucidated to date. This might be because semiautomated quantification of whole-body tumor volume as a PSMA PET biomarker is an unmet clinical challenge. Therefore, in the present study we propose and evaluate a software that enables the semiautomated quantification of PSMA PET biomarkers such as whole-body tumor volume. Methods: The proposed quantification is implemented as a research prototype. PSMA-accumulating foci were automatically segmented by a percental threshold (50% of local SUVmax). Neural networks were trained to segment organs in PET/CT acquisitions (training CTs: 8,632, validation CTs: 53). Thereby, PSMA foci within organs of physiologic PSMA uptake were semiautomatically excluded from the analysis. Pretherapeutic PSMA PET/CTs of 40 consecutive patients treated with 177Lu-PSMA-617 were evaluated in this analysis. The whole-body tumor volume (PSMATV50), SUVmax, SUVmean, and other whole-body imaging biomarkers were calculated for each patient. Semiautomatically derived results were compared with manual readings in a subcohort (by 1 nuclear medicine physician). Additionally, an interobserver evaluation of the semiautomated approach was performed in a subcohort (by 2 nuclear medicine physicians). Results: Manually and semiautomatically derived PSMA metrics were highly correlated (PSMATV50: R2 = 1.000, P < 0.001; SUVmax: R2 = 0.988, P < 0.001). The interobserver agreement of the semiautomated workflow was also high (PSMATV50: R2 = 1.000, P < 0.001, interclass correlation coefficient = 1.000; SUVmax: R2 = 0.988, P < 0.001, interclass correlation coefficient = 0.997). PSMATV50 (ml) was a significant predictor of overall survival (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006 P = 0.004). Conclusion: PSMATV50 is a promising PSMA PET biomarker that is reproducible and easily quantified by the proposed semiautomated software. Moreover, PSMATV50 is a significant predictor of overall survival in patients with advanced prostate cancer who receive 177Lu-PSMA-617 therapy.

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.

Using EQ·PET to reduce reconstruction-dependent variations in [18F]FDG-PET brain imaging.

This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [18F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ([Formula: see text]) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ([Formula: see text]) OSEM 3D with TOF, 6 iterations and 21 subsets; and ([Formula: see text]) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets. EQ·PET filters were computed as the Gaussian smoothing that best independently aligned the recovery coefficients (RCs) of reconstructions [Formula: see text] and [Formula: see text] with the RCs of the reference reconstruction, [Formula: see text]. The performance of the EQ·PET filter to reduce variations in quantification due to differences in reconstruction was investigated using clinical PET brain images of 35 early-onset Alzheimer's disease (EOAD) patients. Qualitative assessments and multiple quantitative metrics on the cortical surface at different scale levels with or without partial volume effect correction were evaluated on the [18F]FDG brain data before and after application of the EQ·PET filter. The EQ·PET methodology succeeded in finding the optimal smoothing that minimised root-mean-square error (RMSE) calculated using human brain [18F]FDG-PET datasets of EOAD patients, providing harmonized comparisons in the neurological context. Performance was superior for TOF than for TOF + PSF reconstructions. Results showed the capability of the EQ·PET methodology to minimize reconstruction-induced variabilities between brain [18F]FDG-PET images. However, moderate variabilities remained after harmonizing PSF reconstructions with standard non-PSF OSEM reconstructions, suggesting that precautions should be taken when using PSF modelling.

How Often Do We Fail to Classify the Treatment Response with [18F]FDG PET/CT Acquired on Different Scanners? Data from Clinical Oncological Practice Using an Automatic Tool for SUV Harmonization.

PURPOSE: Tumor response evaluated by 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) with standardized uptake value (SUV) is questionable when pre- and post-treatment PET/CT are acquired on different scanners. The aims of our study, performed in oncological patients who underwent pre- and post-treatment [18F]FDG PET/CT on different scanners, were (1) to evaluate whether EQ·PET, a proprietary SUV inter-exams harmonization tool, modifies the EORTC tumor response classification and (2) to assess which classification (harmonized and non-harmonized) better predicts clinical outcome. PROCEDURES: We retrospectively identified 95 PET pairs (pre- and post-treatment) performed on different scanners (Biograph mCT, Siemens; GEMINI GXL, Philips) in 73 oncological patients (52F; 57.8 ± 16.3 years). An 8-mm Gaussian filter was applied for the Biograph protocol to meet the EANM/EARL harmonization standard; no filter was needed for GXL. SUVmax and SUVmaxEQ of the same target lesion in the pre- and post-treatment PET/CT were noted. For each PET pair, the metabolic response classification (responder/non-responder), derived from combining the EORTC response categories, was evaluated twice (with and without harmonization). In discordant cases, the association of each metabolic response classification with final clinical response assessment and survival data (2-year disease-free survival, DFS) was assessed. RESULTS: On Biograph, SUVmaxEQ of all target lesions was significantly lower (p = 0.001) than SUVmax (8.5 ± 6.8 vs 12.5 ± 9.6; - 38.6 %). A discordance between the two metabolic response classifications (harmonized and non-harmonized) was found in 19/95 (20 %) PET pairs. In this subgroup (n = 19; mean follow-up, 33.9 ± 9 months), responders according to harmonized classification (n = 9) had longer DFS (47.5 months, 88.9 %) than responders (n = 10) according to non-harmonized classification (26.3 months, 50.0 %; p = 0.01). Moreover, harmonized classification showed a better association with final clinical response assessment (17/19 PET pairs). CONCLUSIONS: The harmonized metabolic response classification is more associated with the final clinical response assessment, and it is able to better predict the DFS than the non-harmonized classification. EQ·PET is a useful harmonization tool for evaluating metabolic tumor response using different PET/CT scanners, also in different departments or for multicenter studies.

Accelerated real-time cardiac MRI using iterative sparse SENSE reconstruction: comparing performance in patients with sinus rhythm and atrial fibrillation.

OBJECTIVES: To compare accelerated real-time cardiac MRI (CMR) using sparse spatial and temporal undersampling and non-linear iterative SENSE reconstruction (RT IS SENSE) with real-time CMR (RT) and segmented CMR (SEG) in a cohort that included atrial fibrillation (AF) patients. METHODS: We evaluated 27 subjects, including 11 AF patients, by acquiring steady-state free precession cine images covering the left ventricle (LV) at 1.5 T with SEG (acceleration factor 2, TR 42 ms, 1.8 × 1.8 × 6 mm3), RT (acceleration factor 3, TR 62 ms, 3.0 × 3.0 × 7 mm3), and RT IS SENSE (acceleration factor 9.9-12, TR 42 ms, 2.0 × 2.0 × 7 mm3). We performed quantitative LV functional analysis in sinus rhythm (SR) patients and qualitatively scored image quality, noise and artefact using a 5-point Likert scale in the complete cohort and AF and SR subgroups. RESULTS: There was no difference between LV functional parameters between acquisitions in SR patients. RT IS SENSE short-axis image quality was superior to SEG (4.5 ± 0.6 vs. 3.9 ± 1.1, p = 0.007) and RT (3.8 ± 0.4, p = 0.003). There was reduced artefact in RT IS SENSE compared to SEG (4.4 ± 0.6 vs. 3.8 ± 1.2, p = 0.04), driven by arrhythmia performance. RT IS SENSE short-axis image quality was superior to SEG (4.6 ± 0.5 vs. 3.1 ± 1.0, p < 0.001) in the AF subgroup. CONCLUSION: Accelerated real-time CMR with iterative sparse SENSE provides excellent clinical performance, especially in patients with AF. KEY POINTS: • Iterative sparse SENSE significantly accelerates real-time cardiovascular MRI acquisitions. • It provides excellent qualitative and quantitative performance in sinus rhythm patients. • It outperforms standard segmented acquisitions in patients with atrial fibrillation. • It improves the trade-off between temporal and spatial resolution in real-time imaging.

Motion-Corrected Real-Time Cine Magnetic Resonance Imaging of the Heart: Initial Clinical Experience.

OBJECTIVES: Free-breathing real-time (RT) imaging can be used in patients with difficulty in breath-holding; however, RT cine imaging typically experiences poor image quality compared with segmented cine imaging because of low resolution. Here, we validate a novel unsupervised motion-corrected (MOCO) reconstruction technique for free-breathing RT cardiac images, called MOCO-RT. Motion-corrected RT uses elastic image registration to generate a single heartbeat of high-quality data from a free-breathing RT acquisition. MATERIALS AND METHODS: Segmented balanced steady-state free precession (bSSFP) cine images and free-breathing RT images (Cartesian, TGRAPPA factor 4) were acquired with the same spatial/temporal resolution in 40 patients using clinical 1.5 T magnetic resonance scanners. The respiratory cycle was estimated using the reconstructed RT images, and nonrigid unsupervised motion correction was applied to eliminate breathing motion. Conventional segmented RT and MOCO-RT single-heartbeat cine images were analyzed to evaluate left ventricular (LV) function and volume measurements. Two radiologists scored images for overall image quality, artifact, noise, and wall motion abnormalities. Intraclass correlation coefficient was used to assess the reliability of MOCO-RT measurement. RESULTS: Intraclass correlation coefficient showed excellent reliability (intraclass correlation coefficient ≥ 0.95) of MOCO-RT with segmented cine in measuring LV function, mass, and volume. Comparison of the qualitative ratings indicated comparable image quality for MOCO-RT (4.80 ± 0.35) with segmented cine (4.45 ± 0.88, P = 0.215) and significantly higher than conventional RT techniques (3.51 ± 0.41, P < 0.001). Artifact and noise ratings for MOCO-RT (1.11 ± 0.26 and 1.08 ± 0.19) and segmented cine (1.51 ± 0.90, P = 0.088 and 1.23 ± 0.45, P = 0.182) were not different. Wall motion abnormality ratings were comparable among different techniques (P = 0.96). CONCLUSIONS: The MOCO-RT technique can be used to process conventional free-breathing RT cine images and provides comparable quantitative assessment of LV function and volume measurements to conventional segmented cine imaging while providing improved image quality and less artifact and noise. The free-breathing MOCO-RT reconstruction method may have considerable clinical utility in cardiac magnetic resonance imaging for patients with difficulty breath-holding.

Evaluation of the impact of strain correction on the orientation of cardiac diffusion tensors with in vivo and ex vivo porcine hearts.

PURPOSE: To evaluate the importance of strain-correcting stimulated echo acquisition mode echo-planar imaging cardiac diffusion tensor imaging. METHODS: Healthy pigs (n = 11) were successfully scanned with a 3D cine displacement-encoded imaging with stimulated echoes and a monopolar-stimulated echo-planar imaging diffusion tensor imaging sequence at 3 T during diastasis, peak systole, and strain sweet spots in a midventricular short-axis slice. The same diffusion tensor imaging sequence was repeated ex vivo after arresting the hearts in either a relaxed (KCl-induced) or contracted (BaCl2 -induced) state. The displacement-encoded imaging with stimulated echoes data were used to strain-correct the in vivo cardiac diffusion tensor imaging in diastole and systole. The orientation of the primary (helix angles) and secondary (E2A) diffusion eigenvectors was compared with and without strain correction and to the strain-free ex vivo data. RESULTS: Strain correction reduces systolic E2A significantly when compared without strain correction and ex vivo (median absolute E2A = 34.3° versus E2A = 57.1° (P = 0.01), E2A = 60.5° (P = 0.006), respectively). The systolic distribution of E2A without strain correction is closer to the contracted ex vivo distribution than with strain correction, root mean square deviation of 0.027 versus 0.038. CONCLUSIONS: The current strain-correction model amplifies the contribution of microscopic strain to diffusion resulting in an overcorrection of E2A. Results show that a new model that considers cellular rearrangement is required. Magn Reson Med 79:2205-2215, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A novel framework for evaluating the image accuracy of dynamic MRI and the application on accelerated breast DCE MRI.

OBJECTIVE: To develop a novel framework for evaluating the accuracy of quantitative analysis on dynamic contrast-enhanced (DCE) MRI with a specific combination of imaging technique, scanning parameters, and scanner and software performance and to test this framework with breast DCE MRI with Time-resolved angiography WIth Stochastic Trajectories (TWIST). MATERIALS AND METHODS: Realistic breast tumor phantoms were 3D printed as cavities and filled with solutions of MR contrast agent. Full k-space raw data of individual tumor phantoms and a uniform background phantom were acquired. DCE raw data were simulated by sorting the raw data according to TWIST view order and scaling the raw data according to the enhancement based on pharmaco-kinetic (PK) models. The measured spatial and temporal characteristics from the images reconstructed using the scanner software were compared with the original PK model (ground truth). RESULTS: Images could be reconstructed using the manufacturer's platform with the modified 'raw data.' Compared with the 'ground truth,' the RMS error in all images was <10% in most cases. With increasing view-sharing acceleration, the error of the initial uptake slope decreased while the error of peak enhancement increased. Deviations of PK parameters varied with the type of enhancement. CONCLUSION: A new framework has been developed and tested to more realistically evaluate the quantitative measurement errors caused by a combination of the imaging technique, parameters and scanner and software performance in DCE-MRI.

The consistency of myocardial strain derived from heart deformation analysis.

The purpose of this study was to assess the consistency of semi-automated myocardial strain analysis by prototype software across field strengths, temporal resolutions, and examinations. 35 volunteers (48 ± 13 years; 20% women) and 25 patients (54 ± 12 years; 44% women) without significant cardiac dysfunction underwent cine cardiac magnetic resonance imaging (CMR) at 1.5 T with a temporal resolution of 39.2 msec. 34 subjects also underwent imaging at 3.0 T; 16 had repeat examinations within 14 days; and 9 underwent CMR with temporal resolutions of 12.5 and 39.2 msec on the same day. Prototype heart deformation analysis (HDA) software was used to retrospectively quantify strain from segmented balanced steady state free precession (bSSFP) cinegraphic images. Myocardial contours were automatically generated on short axis images and drawn at end-diastole by two independent reviewers on long-axis images. Contours were automatically propagated throughout the cardiac cycle. Global and regional peak systolic strain were compared across observers, field strengths, temporal resolutions, and examinations. Inter-observer agreement was excellent (ICC > 0.87, p < 0.01). Inter-examination variability was low, ranging from 1.7 (1.0-2.4)% to 2.5 (1.9-3.1)%, except for radial strain: 9.2 (7.6-10.5)%. Most global and regional strain values were not significantly different across field strengths and temporal resolutions (p > 0.05). Normal global peak systolic strain values with HDA were -25.0 (-24.0 to -26.1)% (LV circumferential), 60.5 (55.3 to 65.6)% (LV radial), -22.3 (-20.5 to - 24.0)% (LV longitudinal), and -26.0 (-23.8 to -28.2)% (RV longitudinal). HDA prototype software enabled efficient and consistent quantification of myocardial strain from conventional bSSFP cine CMR data, demonstrating clinical feasibility.

Effect of inversion time on the precision of myocardial late gadolinium enhancement quantification evaluated with synthetic inversion recovery MR imaging.

OBJECTIVES: To evaluate the influence of inversion time (TI) on the precision of myocardial late gadolinium enhancement (LGE) quantification using synthetic inversion recovery (IR) imaging in patients with myocardial infarction (MI). METHODS: Fifty-three patients with suspected prior MI underwent 1.5-T cardiac MRI with conventional magnitude (MagIR) and phase-sensitive IR (PSIR) LGE imaging and T1 mapping at 15 min post-contrast. T1-based synthetic MagIR and PSIR images were calculated with a TI ranging from -100 to +150 ms at 5-ms intervals relative to the optimal TI (TI0). LGE was quantified using a five standard deviation (5SD) and full width at half-maximum (FWHM) thresholds. Measurements were compared using one-way analysis of variance. RESULTS: The MagIRsy technique provided precise assessment of LGE area at TIs ≥ TI0, while precision was decreased below TI0. The LGE area showed significant differences at ≤ -25 ms compared to TI0 using 5SD (P < 0.001) and at ≤ -65 ms using the FWHM approach (P < 0.001). LGE measurements did not show significant difference over the analysed TI range in the PSIRsy images using either of the quantification methods. CONCLUSIONS: T1 map-based PSIRsy images provide precise quantification of MI independent of TI at the investigated time point post-contrast. MagIRsy-based MI quantification is precise at TI0 and at longer TIs while showing decreased precision at TI values below TI0. KEY POINTS: • Synthetic IR imaging retrospectively generates LGE images at any theoretical TI • Synthetic IR imaging can simulate the effect of TI on LGE quantification • Fifteen minutes post-contrast MagIR sy accurately quantifies infarcts from TI 0 to TI 0 + 150 ms • Fifteen minutes post-contrast PSIR sy provides precise infarct size independent of TI • Synthetic IR imaging has further advantages in reducing operator dependence.