Cardiac T2 star mapping: standardized inline analysis of long and short axis at three identical 1.5 T MRI scanners

Heiß, Rafael; Wiesmueller, Marco; Treutlein, Christoph; Seuß, Hannes; Uder, Michael; May, Matthias; Wuest, Wolfgang

doi:10.1007/s10554-018-1503-1

Cited by 4 publications

(7 citation statements)

References 22 publications

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“…It has been recommended to use midventricular septum as a comparatively artifact‐free region to measure iron overload 31 . The postmortem finding showed that iron distribution is heterogeneous in the myocardium, as reported using the global longitudinal strain method, 43 and was confirmed by the T 2 * measurements in global myocardium 19–21,25,27,41,43,45,49,57–59 . Different iron overload patterns in different myocardial segments beyond the influence of susceptibility artifact were observed.…”

Section: T2* Mapping Techniquesmentioning

confidence: 58%

“…Therefore, the first, the second, [21][22][23][24] or any optimum contrast TE image 16 has been in use as a template to draw the LV ROI. The reproducibility of T 2 * measurement was verified and validated on different scanners, 24,25 MR centers, [26][27][28] and software packages, 22,29 ensuring widespread implementation 1,2,7,30 of the method.…”

Section: Mri Acquisitionmentioning

confidence: 93%

“…4). 25,43,46,57 Iron distribution identification in postmortem heart samples with severe iron overload revealed higher iron concentration in the epicardial than in the endocardial region 10,11,13 . It has been recommended to use midventricular septum as a comparatively artifact‐free region to measure iron overload 31 .…”

Section: T2* Mapping Techniquesmentioning

confidence: 99%

“…For patients having difficulties maintaining breath‐hold, it is advised to increase the number of excitations 41 . Flip angle in this technique is typically set between 20°–35° 19–21,24–26,28,40,42,43 . When poor breath‐holding becomes a problem, such as for the cardiac arrhythmia patient, free‐breathing T 2 * measurement has been an alternative, even though used sparingly up to now 44,45 …”

Section: Mri Acquisitionmentioning

confidence: 99%

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Cardiac T₂* mapping: Techniques and clinical applications

Triadyaksa

Oudkerk

Sijens

2019

Magnetic Resonance Imaging

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Cardiac T2* mapping is a noninvasive MRI method that is used to identify myocardial iron accumulation in several iron storage diseases such as hereditary hemochromatosis, sickle cell disease, and β‐thalassemia major. The method has improved over the years in terms of MR acquisition, focus on relative artifact‐free myocardium regions, and T2* quantification. Several improvement factors involved include blood pool signal suppression, the reproducibility of T2* measurement as affected by scanner hardware, and acquisition software. Regarding the T2* quantification, improvement factors include the applied curve‐fitting method with or without truncation of the signals acquired at longer echo times and whether or not T2* measurement focuses on multiple segmental regions or the midventricular septum only. Although already widely applied in clinical practice, data processing still differs between centers, contributing to measurement outcome variations. State of the art T2* measurement involves pixelwise quantification providing better spatial iron loading information than region of interest‐based quantification. Improvements have been proposed, such as on MR acquisition for free‐breathing mapping, the generation of fast mapping, noise reduction, automatic myocardial contour delineation, and different T2* quantification methods. This review deals with the pro and cons of different methods used to quantify T2* and generate T2* maps. The purpose is to recommend a combination of MR acquisition and T2* mapping quantification techniques for reliable outcomes in measuring and follow‐up of myocardial iron overload. The clinical application of cardiac T2* mapping for iron overload's early detection, monitoring, and treatment is addressed. The prospects of T2* mapping combined with different MR acquisition methods, such as cardiac T1 mapping, are also described. Level of Evidence: 4 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019.

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Section: T2* Mapping Techniquesmentioning

confidence: 58%

Section: Mri Acquisitionmentioning

confidence: 93%

Section: T2* Mapping Techniquesmentioning

confidence: 99%

Section: Mri Acquisitionmentioning

confidence: 99%

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Cardiac T₂* mapping: Techniques and clinical applications

Triadyaksa

Oudkerk

Sijens

2019

Magnetic Resonance Imaging

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“…Baseline LV dysfunction and the presence of LGE were predictive of MACE. Differences in T2* values were found in various myocardial segments in patients being evaluated for myocardial iron deposition [70].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 98%

Cardiovascular imaging 2019 in the International Journal of Cardiovascular Imaging

Reiber

Pereira

Dallan

et al. 2020

Int J Cardiovasc Imaging

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MRI Deep Learning‐Based Automatic Segmentation of Interventricular Septum for Black‐Blood Myocardial T2* Measurement in Thalassemia

Lian,

Lu,

Lin

et al. 2023

Magnetic Resonance Imaging

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BackgroundThe 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.PurposeTo develop a deep learning‐based method for automatic septum segmentation from black‐blood MR images for the myocardial T2* measurement of thalassemia patients.Study TypeRetrospective.Population/SubjectsOne 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).Field Strength/Sequence1.5 T and 3.0 T, multiecho gradient‐echo sequence.AssessmentA 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.Statistical TestsAgreement 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).ResultsThe 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.Data ConclusionsThe 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.Evidence Level4Technical EfficacyStage 1

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Cardiac T2 star mapping: standardized inline analysis of long and short axis at three identical 1.5 T MRI scanners

Cited by 4 publications

References 22 publications

Cardiac T₂* mapping: Techniques and clinical applications

Cardiac T₂* mapping: Techniques and clinical applications

Cardiovascular imaging 2019 in the International Journal of Cardiovascular Imaging

MRI Deep Learning‐Based Automatic Segmentation of Interventricular Septum for Black‐Blood Myocardial T2* Measurement in Thalassemia

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Cardiac T2 star mapping: standardized inline analysis of long and short axis at three identical 1.5 T MRI scanners

Cited by 4 publications

References 22 publications

Cardiac T2* mapping: Techniques and clinical applications

Cardiac T2* mapping: Techniques and clinical applications

Cardiovascular imaging 2019 in the International Journal of Cardiovascular Imaging

MRI Deep Learning‐Based Automatic Segmentation of Interventricular Septum for Black‐Blood Myocardial T2* Measurement in Thalassemia

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Product

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Cardiac T₂* mapping: Techniques and clinical applications

Cardiac T₂* mapping: Techniques and clinical applications