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.

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.

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.

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.

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.

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.

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.

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.

Myocardial Late Gadolinium Enhancement: Accuracy of T1 Mapping-based Synthetic Inversion-Recovery Imaging.

PURPOSE: To compare the accuracy of detection and quantification of myocardial late gadolinium enhancement (LGE) with a synthetic inversion-recovery (IR) approach with that of conventional IR techniques. MATERIALS AND METHODS: This prospective study was approved by the institutional review board and compliant with HIPAA. All patients gave written informed consent. Between June and November 2014, 43 patients (25 men; mean age, 54 years ± 16) suspected of having previous myocardial infarction underwent magnetic resonance (MR) imaging, including contrast material-enhanced LGE imaging and T1 mapping. Synthetic magnitude and phase-sensitive IR images were generated on the basis of T1 maps. Images were assessed by two readers. Differences in the per-patient and per-segment LGE detection rates between the synthetic and conventional techniques were analyzed with the McNemar test, and the accuracy of LGE quantification was calculated with the paired t test and Bland-Altman statistics. Interreader agreement for the detection and quantification of LGE was analyzed with κ and Bland-Altman statistics, respectively. RESULTS: Seventeen of the 43 patients (39%) had LGE patterns consistent with myocardial infarction. The sensitivity and specificity of synthetic magnitude and phase-sensitive IR techniques in the detection of LGE were 90% and 95%, respectively, with patient-based analysis and 94% and 99%, respectively, with segment-based analysis. The area of LGE measured with synthetic IR techniques showed excellent agreement with that of conventional techniques (4.35 cm(2) ± 1.88 and 4.14 cm(2)± 1.62 for synthetic magnitude and phase-sensitive IR, respectively, compared with 4.25 cm(2) ± 1.92 and 4.22 cm(2) ± 1.86 for conventional magnitude and phase-sensitive IR, respectively; P > .05). Interreader agreement was excellent for the detection (κ > 0.81) and quantification (bias range, -0.34 to 0.40; P > .05) of LGE. CONCLUSION: The accuracy of the T1 map-based synthetic IR approach in the detection and quantification of myocardial LGE in patients with previous myocardial infarction was similar to that of conventional IR techniques. The use of T1 mapping to derive synthetic LGE images may reduce imaging times and operator dependence in future T1 mapping protocols with full left ventricular coverage.

Validation of T2* in-line analysis for tissue iron quantification at 1.5 T.

BACKGROUND: There is a need for improved worldwide access to tissue iron quantification using T2* cardiovascular magnetic resonance (CMR). One route to facilitate this would be simple in-line T2* analysis widely available on MR scanners. We therefore compared our clinically validated and established T2* method at Royal Brompton Hospital (RBH T2*) against a novel work-in-progress (WIP) sequence with in-line T2* measurement from Siemens (WIP T2*). METHODS: Healthy volunteers (n = 22) and patients with iron overload (n = 78) were recruited (53 males, median age 34 years). A 1.5 T study (Magnetom Avanto, Siemens) was performed on all subjects. The same mid-ventricular short axis cardiac slice and transaxial slice through the liver were used to acquire both RBH T2* images and WIP T2* maps for each participant. Cardiac white blood (WB) and black blood (BB) sequences were acquired. Intraobserver, interobserver and interstudy reproducibility were measured on the same data from a subset of 20 participants. RESULTS: Liver T2* values ranged from 0.8 to 35.7 ms (median 5.1 ms) and cardiac T2* values from 6.0 to 52.3 ms (median 31 ms). The coefficient of variance (CoV) values for direct comparison of T2* values by RBH and WIP were 6.1-7.8 % across techniques. Accurate delineation of the septum was difficult on some WIP T2* maps due to artefacts. The inability to manually correct for noise by truncation of erroneous later echo times led to some overestimation of T2* using WIP T2* compared with the RBH T2*. Reproducibility CoV results for RBH T2* ranged from 1.5 to 5.7 % which were better than the reproducibility of WIP T2* values of 4.1-16.6 %. CONCLUSIONS: Iron estimation using the T2* CMR sequence in combination with Siemens' in-line data processing is generally satisfactory and may help facilitate global access to tissue iron assessment. The current automated T2* map technique is less good for tissue iron assessment with noisy data at low T2* values.

White matter integrity of the cerebellar peduncles as a mediator of effects of prenatal alcohol exposure on eyeblink conditioning.

Fetal alcohol spectrum disorders (FASD) are characterized by a range of neurodevelopmental deficits that result from prenatal exposure to alcohol. These can include cognitive, behavioural, and neurological impairment, as well as structural and functional brain damage. Eyeblink conditioning (EBC) is among the most sensitive endpoints affected in FASD. The cerebellar peduncles, large bundles of myelinated nerve fibers that connect the cerebellum to the brainstem, constitute the principal white matter element of the EBC circuit. Diffusion tensor imaging (DTI) is used to assess white matter integrity in fibre pathways linking brain regions. DTI scans of 54 children with FASD and 23 healthy controls, mean age 10.1 ± 1.0 years, from the Cape Town Longitudinal Cohort were processed using voxelwise group comparisons. Prenatal alcohol exposure was related to lower fractional anisotropy (FA) bilaterally in the superior cerebellar peduncles and higher mean diffusivity (MD) in the left middle peduncle, effects that remained significant after controlling for potential confounding variables. Lower FA and higher MD in these regions were associated with poorer EBC performance. Moreover, effects of alcohol exposure on EBC decreased significantly after inclusion of these DTI measures in regression models, suggesting that these white matter deficits partially mediate the relation of prenatal alcohol exposure to EBC. The associations of greater alcohol consumption with these DTI measures are largely attributable to greater radial diffusivity, possibly indicating poorer myelination. Thus, these data suggest that fetal alcohol-related deficits in EBC are attributable, in part, to poorer myelination in key regions of the cerebellar peduncles.

Instantaneous signal loss simulation (InSiL): an improved algorithm for myocardial T1 mapping using the MOLLI sequence.

PURPOSE: To propose a T1 mapping algorithm for the modified Look-Locker inversion-recovery (MOLLI) sequence that can improve T1 estimation accuracy. MATERIALS AND METHODS: The modified T1 mapping algorithm (InSiL) is based on the simulation of MOLLI signal evolution and simulates the longitudinal magnetization signal perturbation by each single-shot image acquisition in MOLLI as an instantaneous signal loss. InSiL was evaluated against original MOLLI using Bloch simulations, phantom studies, and in 15 healthy volunteers at 1.5T. RESULTS: In phantom studies, the maximum absolute error by InSiL is less than 2%, while that by MOLLI is more than 20% for T1 values from 221 msec to 1539 msec. The benefit of InSiL is greatest at heart rate (HR) >80 bpm and T1 >1000 msec, and InSiL reduced MOLLI T1 error from 14.9 ± 4.5% to 0.4 ± 0.3%. Average InSiL-derived native myocardial T1 values at 1.5T in healthy volunteers were significantly higher than MOLLI-derived values by 236.9 ± 11.7 msec (1160.3 ± 25.1 msec vs. 923.4 ± 22.3 msec, P < 0.001) at an average HR of 65.1 ± 14.7 bpm. CONCLUSION: The proposed InSiL approach yields better T1 mapping accuracy than MOLLI, and is less sensitive to HR variation in tissues with longer T1 values.

Saturation pulse design for quantitative myocardial T1 mapping.

BACKGROUND: Quantitative saturation-recovery based T1 mapping sequences are less sensitive to systematic errors than the Modified Look-Locker Inversion recovery (MOLLI) technique but require high performance saturation pulses. We propose to optimize adiabatic and pulse train saturation pulses for quantitative T1 mapping to have <1 % absolute residual longitudinal magnetization (|MZ/M0|) over ranges of B0 and [Formula: see text] (B1 scale factor) inhomogeneity found at 1.5 T and 3 T. METHODS: Design parameters for an adiabatic BIR4-90 pulse were optimized for improved performance within 1.5 T B0 (±120 Hz) and [Formula: see text] (0.7-1.0) ranges. Flip angles in hard pulse trains of 3-6 pulses were optimized for 1.5 T and 3 T, with consideration of T1 values, field inhomogeneities (B0 = ±240 Hz and [Formula: see text]=0.4-1.2 at 3 T), and maximum achievable B1 field strength. Residual MZ/M0 was simulated and measured experimentally for current standard and optimized saturation pulses in phantoms and in-vivo human studies. T1 maps were acquired at 3 T in human subjects and a swine using a SAturation recovery single-SHot Acquisition (SASHA) technique with a standard 90°-90°-90° and an optimized 6-pulse train. RESULTS: Measured residual MZ/M0 in phantoms had excellent agreement with simulations over a wide range of B0 and [Formula: see text]. The optimized BIR4-90 reduced the maximum residual |MZ/M0| to <1 %, a 5.8× reduction compared to a reference BIR4-90. An optimized 3-pulse train achieved a maximum residual |MZ/M0| <1 % for the 1.5 T optimization range compared to 11.3 % for a standard 90°-90°-90° pulse train, while a 6-pulse train met this target for the wider 3 T ranges of B0 and [Formula: see text]. The 6-pulse train demonstrated more uniform saturation across both the myocardium and entire field of view than other saturation pulses in human studies. T1 maps were more spatially homogeneous with 6-pulse train SASHA than the reference 90°-90°-90° SASHA in both human and animal studies. CONCLUSIONS: Adiabatic and pulse train saturation pulses optimized for different constraints found at 1.5 T and 3 T achieved <1 % residual |MZ/M0| in phantom experiments, enabling greater accuracy in quantitative saturation recovery T1 imaging.

Free-breathing T2* mapping using respiratory motion corrected averaging.

BACKGROUND: Pixel-wise T2* maps based on breath-held segmented image acquisition are prone to ghost artifacts in instances of poor breath-holding or cardiac arrhythmia. Single shot imaging is inherently immune to ghost type artifacts. We propose a free-breathing method based on respiratory motion corrected single shot imaging with averaging to improve the signal to noise ratio. METHODS: Images were acquired using a multi-echo gradient recalled echo sequence and T2* maps were calculated at each pixel by exponential fitting. For 40 subjects (2 cohorts), two acquisition protocols were compared: (1) a breath-held, segmented acquisition, and (2) a free-breathing, single-shot multiple repetition respiratory motion corrected average. T2* measurements in the interventricular septum and liver were compared for the 2-methods in all studies with diagnostic image quality. RESULTS: In cohort 1 (N = 28) with age 51.4 ± 17.6 (m ± SD) including 1 subject with severe myocardial iron overload, there were 8 non-diagnostic breath-held studies due to poor image quality resulting from ghost artifacts caused by respiratory motion or arrhythmias. In cohort 2 (N = 12) with age 30.9 ± 7.5 (m ± SD), including 7 subjects with severe myocardial iron overload and 4 subjects with mild iron overload, a single subject was unable to breath-hold. Free-breathing motion corrected T2* maps were of diagnostic quality in all 40 subjects. T2* measurements were in excellent agreement (In cohort #1, T2*FB = 0.95 x T2*BH + 0.41, r2 = 0.93, N = 39 measurements, and in cohort #2, T2*FB = 0.98 x T2*BH + 0.05, r2 > 0.99, N = 22 measurements). CONCLUSIONS: A free-breathing approach to T2* mapping is demonstrated to produce consistently good quality maps in the presence of respiratory motion and arrhythmias.

Corpus callosum thickness on mid-sagittal MRI as a marker of brain volume: a pilot study in children with HIV-related brain disease and controls.

BACKGROUND: Corpus callosum thickness measurement on mid-sagittal MRI may be a surrogate marker of brain volume. This is important for evaluation of diseases causing brain volume gain or loss, such as HIV-related brain disease and HIV encephalopathy. OBJECTIVE: To determine if thickness of the corpus callosum on mid-sagittal MRI is a surrogate marker of brain volume in children with HIV-related brain disease and in controls without HIV. MATERIALS AND METHODS: A retrospective MRI analysis in children (<5 years old) with HIV-related brain disease and controls used a custom-developed semi-automated tool, which divided the midline corpus callosum and measured its thickness in multiple locations. Brain volume was determined using volumetric analysis. Overall corpus callosum thickness and thickness of segments of the corpus callosum were correlated with overall and segmented (grey and white matter) brain volume. RESULTS: Forty-four children (33 HIV-infected patients and 11 controls) were included. Significant correlations included overall corpus callosum (mean) and total brain volume (P = 0.05); prefrontal corpus callosum maximum with white matter volume (P = 0.02); premotor corpus callosum mean with total brain volume (P = 0.04) and white matter volume (P = 0.02), premotor corpus callosum maximum with white matter volume (P = 0.02) and sensory corpus callosum mean with total brain volume (P = 0.02). CONCLUSION: Corpus callosum thickness correlates with brain volume both in HIV-infected patients and controls.

Optimized saturation recovery protocols for T1-mapping in the heart: influence of sampling strategies on precision.

BACKGROUND: T1-mapping has the potential to detect and quantify diffuse processes such as interstitial fibrosis. Detection of disease at an early stage by measurement of subtle changes requires a high degree of reproducibility. Initial implementation of saturation recovery (SR) T1-mapping employed 3-parameter fitting which was highly accurate but was quite sensitive to noise; 2-parameter fitting greatly reduced the sensitivity to noise at the expense of a small degree of systematic bias. A recently introduced implementation that uses a variable readout flip angle greatly reduces systematic errors in T1-measurement thereby making it feasible to use SR methods with 2-parameter fitting with improved accuracy and precision. SR T1 mapping techniques with multi-heartbeat recovery times have been proposed to better sample the T1 recovery curve, but have not been evaluated for 2-parameter fitting. METHODS: An analytic formulation for calculating the standard deviation (SD) for SR T1-mapping with 2-parameter fitting is developed and validated using Monte-Carlo simulation. The coefficient of variation is compared for a brute force optimization of sampling and for several previously described sampling schemes for T1 measurement over several uncertainty ranges. Experimental validation is performed in phantoms over a range of T1, and in-vivo both native and post-contrast. Pixel-wise SD maps are calculated for SR T1-mapping. RESULTS: Sampling schemes that use a non-saturated anchor image and multiple (N) measurements at a single fixed saturation delay are found to be near optimum for the case of known T1 and are close to the brute force optimized solution over wide ranges of native and post-contrast T1 values. The fixed delay sampling scheme is simple to implement and provides an improvement over uniformly distributed schemes. CONCLUSIONS: Sampling strategies for saturation recovery methods for myocardial T1-mapping have been optimized and validated experimentally. Reduced SD, or improved precision, may be achieved by using fixed saturation delays when considering native myocardium and post-contrast T1 ranges. Pixel-wise estimates of T1 mapping errors have been formulated and validated for SR fitting methods.

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance.

BACKGROUND: The most time consuming and limiting step in three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) MR image analysis is the demarcation of the left ventricle (LV) from its surrounding anatomical structures. The aim of this study is to implement a semi-automated segmentation algorithm for 3D cine DENSE CMR using a guide point model approach. METHODS: A 3D mathematical model is fitted to guide points which were interactively placed along the LV borders at a single time frame. An algorithm is presented to robustly propagate LV epicardial and endocardial surfaces of the model using the displacement information encoded in the phase images of DENSE data. The accuracy, precision and efficiency of the algorithm are tested. RESULTS: The model-defined contours show good accuracy when compared to the corresponding manually defined contours as similarity coefficients Dice and Jaccard consist of values above 0.7, while false positive and false negative measures show low percentage values. This is based on a measure of segmentation error on intra- and inter-observer spatial overlap variability. The segmentation algorithm offers a 10-fold reduction in the time required to identify LV epicardial and endocardial borders for a single 3D DENSE data set. CONCLUSION: A semi-automated segmentation method has been developed for 3D cine DENSE CMR. The algorithm allows for contouring of the first cardiac frame where blood-myocardium contrast is almost nonexistent and reduces the time required to segment a 3D DENSE data set significantly.

Correlating brain volume and callosal thickness with clinical and laboratory indicators of disease severity in children with HIV-related brain disease.

BACKGROUND: Objective MRI markers of central nervous system disease severity may precede subjective features of HIV encephalopathy in children. Previous work in HIV-infected adults shows that brain atrophy was associated with low CD4 and with neuropsychological impairment. Significant thinning of the corpus callosum (CC), predominantly anteriorly, was also found in HIV-infected adults and correlated with CD4 levels. These findings have not been tested in children. PURPOSE: The aim of this study was to determine if brain volume and midsagittal CC linear measurements (thickness and length) on MRI in children with HIV-related brain disease correlate with clinical and laboratory parameters of disease severity. METHODS: Retrospective MRI analysis in children with HIV-related brain disease used a volumetric analysis software and a semi-automated tool to measure brain volume and callosal thickness/length, respectively. Each measure was correlated with clinical parameters of disease severity including Griffiths Mental Development scores (GMDS), absolute CD4 counts (cells/mm(3)), nadir CD4 (the lowest CD4 recorded, excluding baseline), duration of HAART, and decreased brain growth. RESULTS: Thirty-three children with HIV-related brain disease were included. Premotor segment of the CC mean thickness correlated with age (p = 0.394). Motor CC maximum thickness correlated significantly with general developmental quotient (p = 0.0277); CC length correlated with a diagnosis of acquired microcephaly (p = 0.0071) and to CD4 level closest to date of the MRI scan (p = 0.04). CONCLUSIONS: Length of the CC and the "motor CC segment" may represent surrogate clinical biomarkers of central nervous system disease severity and with decreased level of immunity in HIV-infected patients that precede established HIV encephalopathy.

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.