OBJECTIVE: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI.
METHODS: Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted.
RESULTS: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 ± $$ \\pm $$ 2.6 mm for the fetal eyes and 6.5 ± $$ \\pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning.
CONCLUSIONS: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.

OBJECTIVE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver.
METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots.
RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets.
CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.
OBJECTIVE: To develop a deep learning-based method for automatic septum segmentation from black-blood MR images for the myocardial T2* measurement of thalassemia patients.
METHODS: Retrospective.
METHODS: One hundred forty-six transfusion-dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T).
UNASSIGNED: 1.5 T and 3.0 T, multiecho gradient-echo sequence.
RESULTS: A modified attention U-Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods.
METHODS: Agreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland-Altman plot and the coefficient of variation (CoV).
RESULTS: The median Dice coefficient of deep network-based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: -0.74 to 0.79) msec, 0.43 (95% LoA: -2.1 to 3.0) msec, and 0.36 (95% LoA: -0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively.
CONCLUSIONS: The proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator-dependent variability in practice.
METHODS: 4 TECHNICAL EFFICACY: Stage 1.

To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI.
A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets.
Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age.
Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied \"offline\" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.

Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T2 * relaxometry. However, T2 * mapping requires manual image segmentation, which is a time-consuming process and prone to inter- and intra- segmenter variability. Automating segmentation would address these challenges. A model previously trained using Constructive Interference in Steady State (CISS) scans was applied to T2 * segmentation via transfer learning. It was hypothesized that there would be no significant differences in the model\'s segmentation performance between T2 * and CISS, structural measures versus ground truth manual segmentation, and reliability versus independent and retest manual segmentation. Transfer learning was conducted using 54 T2 * scans of the ACL. Segmentation performance was assessed with Dice coefficient, precision, and sensitivity, and structurally with T2 * value, volume, subvolume proportions, and cross-sectional area. Model performance relative to independent manual segmentation and repeated segmentation by the ground truth segmenter (retest) were evaluated on a random subset. Segmentation performance was analyzed with Mann-Whitney U tests, structural measures with Wilcoxon signed-rank tests, and performance relative to manual segmentation with repeated-measures analysis of variance/Tukey tests (α = 0.05). T2 * segmentation performance was not significantly different from CISS on all measures (p > 0.35). No significant differences were detected in structural measures (p > 0.50). Automatic segmentation performed as well as the retest on all segmentation measures, whereas independent segmentations were lower than retest and/or automatic segmentation (p < 0.023). Structural measures were not significantly different between segmenters. The automatic segmentation model performed as well on the T2 * sequence as on CISS and outperformed independent manual segmentation while performing as well as retest segmentation.

Magnetic resonance imaging (MRI)-based liver iron quantification is the standard of care to guide chelation therapy in children at risk of hemochromatosis. T2* relaxometry is the most widely used technique but requires third-party software for post-processing. Vendor-provided three-dimensional (3-D) multi-echo Dixon techniques are now available that allow inline/automated post-processing.
The purpose of our study was to evaluate the diagnostic accuracy of a volumetric multi-echo Dixon technique using conventional T2* relaxometry as the reference standard in a pediatric and young adult population.
In this retrospective study, we queried the radiology information system to identify all MRIs performed for liver iron quantification from July 2015 to January 2020. All patients had undergone T2* relaxometry on a 1.5-tesla (T) scanner for liver iron concentration (LIC) estimation. In addition, a 3-D multi-echo Dixon was performed using Siemens Healthineers LiverLab (Erlangen, Germany). Two readers independently estimated liver R2* and T2* on the multi-echo Dixon by drawing free-hand regions of interest on the scanner-generated R2* and T2* maps. Conventional T2*-relaxometry-based LIC was the reference standard. We estimated interobserver agreement by concordance correlation coefficient (CCC). We used Bland-Altman analysis and Pearson correlation coefficient (r) to compare LIC by the two methods.
Fifty-four MRIs on 38 patients (22 females) were available for analysis. Mean patient age was 11.8 years (standard deviation [SD] 5.3 years). Reference standard LIC ranged 1.1-21.1 (median 6.8) mg/g dry weight of liver. The concordance between readers for T2* estimation using 3-D multi-echo Dixon was substantial (CCC 0.99, confidence interval 0.99-1.00). Bland-Altman plot showed that all observations were clustered around the zero bias line if the LIC average was ≤8 mg/g, and r was very strong (reader 1 r=0.93, reader 2 r=0.92, both P-values <0.001). With increasing LIC, there was a pattern of poor agreement on the Bland-Altman plot, with observations crossing the lower limits of agreement, and r was very weak (reader 1 r=0.05, P-value 0.84; reader 2 r=0.17, P-value 0.44).
Vendor-based 3-D multi-echo Dixon allows for excellent interobserver correlation in liver T2* estimation. LIC estimated by this method has a very strong correlation with conventional T2* relaxometry if liver iron overload is mild-moderate (LIC ≤8 mg/g).

Quantitative magnetic resonance imaging has been used to evaluate the structural integrity of knee joint structures. However, variations in acquisition parameters between scanners pose significant challenges. Understanding the effect of small differences in acquisition parameters for quantitative sequences is vital to the validity of cross-institutional studies, and for the harmonization of large, heterogeneous datasets to train machine learning models. The study objective was to assess the reproducibility of T2 * relaxometry and the constructive interference in steady-state sequence (CISS) across scanners, with minimal hardware-necessitated changes to acquisition parameters. It was hypothesized that there would be no significant differences between scanners in anterior cruciate ligament T2 * relaxation times and CISS signal intensities (SI). Secondarily, it was hypothesized that differences could be corrected by rescaling the SI distribution to harmonize between scanners. Seven volunteers were scanned on 3T Prisma and Tim Trio scanners (Siemens). Three correction methods were evaluated for T2 *: inverse echo time scaling, z-scoring, and Nyúl histogram matching. For CISS, scans were normalized to cortical bone, scaled by the background noise ratio, and log-transformed. Before correction, significant mean differences of 6.0 ± 3.2 ms (71.8%; p = 0.02) and 0.49 ± 0.15 units (40.7%; p = 0.02) for T2 * and CISS across scanners were observed, respectively. After rescaling, T2 * differences decreased to 2.6 ± 2.7 ms (23.9%; p = 0.03), 1.3 ± 2.5 ms (10.9%; p = 0.13), and 1.27 ± 3.0 ms (19.6%; p = 0.40) for inverse echo time, z-scoring, and Nyúl, respectively, while CISS decreased to 0.01 ± 0.11 units (4.0%; p = 0.87). These findings suggest that small acquisition parameter differences may lead to large changes in T2 * and SI values that must be reconciled to compare data across magnets.

Exercise therapy is considered preferential treatment for patellar tendinopathy (PT). However, there is conflicting evidence for structural patellar tendon adaptation in response to exercise therapy and its association with symptoms is weak.
To assess the association between 1) T2 * relaxation times and symptom severity; 2) baseline T2 * and clinical outcome; and 3) longitudinal T2 * changes and clinical outcome in athletes with PT performing exercise therapy.
Randomized controlled clinical trial.
Seventy-six athletes (18-35 years) with clinically diagnosed and ultrasound-confirmed PT.
3D gradient echo sequence (3.0 T).
Patients were enrolled in a randomized trial of progressive tendon-loading exercises (PTLE) versus eccentric exercise therapy (EET). Symptoms were assessed using the Victorian Institute of Sports Assessment (VISA-P) questionnaire. 3D-Ultrashort echo time (UTE)-MRI was acquired at baseline, 12 and 24 weeks. Voxel-wise T2 * relaxation times were quantified using mono-exponential and bi-exponential models. T2 * analysis was performed in three patellar tendon tissue compartments representing: aligned collagen, degenerative tissue, and interface.
Adjusted general linear, mixed-linear models, and generalized estimating equations.
We included 76 patients with PT (58 men, mean age 24 ± 4 years); 38 in the PTLE-group and 38 in the EET-group, of which 57 subjects remained eligible for analysis. T2 * relaxation times were significantly associated with VISA-P in degenerative and interface tissues of the patellar tendon. No association was found between baseline T2 * and VISA-P after 24 weeks (P > 0.29). The estimated mean T2 * in degenerative tissue decreased from 14 msec (95%CI: 12-16) at baseline to 13 msec (95%CI: 11-15) at 12 weeks and to 13 msec (95%CI: 10-15) at 24 weeks. The significant decrease in T2 * from baseline to 24 weeks was associated with improved clinical outcome.
Tissue-specific T2 * relaxation times, identified with 3D-UTE-MRI, decreased significantly in athletes with patellar tendinopathy performing exercise therapy and this decrease was associated with improved clinical outcome.
1 TECHNICAL EFFICACY: Stage 4.

Multiple blood cell transfusions may cause iron overload or even liver fibrosis, requiring early diagnosis and intervention. SF is the standard for estimating iron levels in the body, but it also increases with inflammation. We hypothesized that T2 * magnetic resonance (MR) relaxometry is a more accurate alternative for follow-up in pediatric patients before and after allogenic SCT. Twenty-three children (mean age 10.2 years, 10 female, 13 male) were evaluated prospectively before SCT as well as at least 1 year after SCT with T2 * relaxometry on a 1.5 T MR-scanner to estimate liver iron concentrations from the T2 * values (\"MR-Fe\"). The results were compared with SF, while also considering CRP, and correlated with the number of transfusions. Overall, 24.3 transfusions were administered in average, mainly within 100 days of SCT (mean 10.5 units). Both MR-Fe and SF increased after SCT and decreased in the absence of new transfusions 1 year later without chelate therapy. This suggests regeneration of LP and iron loss, although the original states were not reached. Additionally, simultaneous peaks of CRP and SF were observed directly after SCT. MR-Fe did neither reveal these peaks nor was it associated with CRP (P = .39). We postulate that these early CRP and SF peaks after SCT are probably related to inflammatory reactions and not to iron overload. Thus, SF is not reliable for iron overload diagnosis after SCT in every condition. Beside this interaction, SF and MR-Fe revealed similar accuracy. MRI, however, has practical and economical disadvantages in routine estimation of iron.

Neuroinflammation with microglia activation is thought to be closely related to cortical multiple sclerosis (MS) lesion pathogenesis.
Using 11C-PBR28 and 7 Tesla (7T) imaging, we assessed in 9 relapsing-remitting multiple sclerosis (RRMS) and 10 secondary progressive multiple sclerosis (SPMS) patients the following: (1) microglia activation in lesioned and normal-appearing cortex, (2) cortical lesion inflammatory profiles, and (3) the relationship between neuroinflammation and cortical integrity.
Mean 11C-PBR28 uptake was measured in focal cortical lesions, cortical areas with 7T quantitative T2* (q-T2*) abnormalities, and normal-appearing cortex. The relative difference in cortical 11C-PBR28 uptake between patients and 14 controls was used to classify cortical lesions as either active or inactive. Disease burden was investigated according to cortical lesion inflammatory profiles. The relation between q-T2* and 11C-PBR28 uptake along the cortex was assessed.
11C-PBR28 uptake was abnormally high in cortical lesions in RRMS and SPMS; in SPMS, tracer uptake was significantly increased also in normal-appearing cortex. 11C-PBR28 uptake and q-T2* correlated positively in many cortical areas, negatively in some regions. Patients with high cortical lesion inflammation had worse clinical outcome and higher intracortical lesion burden than patients with low inflammation.
11C-PBR28 and 7T imaging reveal distinct profiles of cortical inflammation in MS, which are related to disease burden.