Solitary Fibrous Tumor With an Acute Subdural Hematoma: A Case Report and Review of the Literature.

Solitary fibrous tumor (SFT) is a rare interstitial tumor that originates from various soft tissues, and SFTs occurring within the cranium are extremely rare. While intracranial SFTs with cerebral hemorrhage or subarachnoid hemorrhage have been reported, there have been no reports of intracranial SFTs causing subdural hematoma. In this case, we report on an intracranial SFT accompanied by a subdural hematoma. A 29-year-old female was emergently transported due to the sudden onset of persistent headache and vomiting that began the night before. CT and MRI imaging revealed a hemorrhagic tumor under the tentorium and an acute subdural hematoma extending along the tentorium. The excised tumor was diagnosed as an SFT through histopathological examination. After undergoing radiation therapy, no recurrence has been observed. This is the first case report of an SFT accompanied by a subdural hematoma, and it is vital to recognize that SFTs can be associated with subdural hematomas for proper diagnosis and treatment planning.

Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data.

BACKGROUND: Deep learning (DL) has been widely used for diagnosis and prognosis prediction of numerous frequently occurring diseases. Generally, DL models require large datasets to perform accurate and reliable prognosis prediction and avoid overlearning. However, prognosis prediction of rare diseases is still limited owing to the small number of cases, resulting in small datasets. PURPOSE: This paper proposes a multimodal DL method to predict the prognosis of patients with malignant pleural mesothelioma (MPM) with a small number of 3D positron emission tomography-computed tomography (PET/CT) images and clinical data. METHODS: A 3D convolutional conditional variational autoencoder (3D-CCVAE), which adds a 3D-convolutional layer and conditional VAE to process 3D images, was used for dimensionality reduction of PET images. We developed a two-step model that performs dimensionality reduction using the 3D-CCVAE, which is resistant to overlearning. In the first step, clinical data were input to condition the model and perform dimensionality reduction of PET images, resulting in more efficient dimension reduction. In the second step, a subset of the dimensionally reduced features and clinical data were combined to predict 1-year survival of patients using the random forest classifier. To demonstrate the usefulness of the 3D-CCVAE, we created a model without the conditional mechanism (3D-CVAE), one without the variational mechanism (3D-CCAE), and one without an autoencoder (without AE), and compared their prediction results. We used PET images and clinical data of 520 patients with histologically proven MPM. The data were randomly split in a 2:1 ratio (train : test) and three-fold cross-validation was performed. The models were trained on the training set and evaluated based on the test set results. The area under the receiver operating characteristic curve (AUC) for all models was calculated using their 1-year survival predictions, and the results were compared. RESULTS: We obtained AUC values of 0.76 (95% confidence interval [CI], 0.72-0.80) for the 3D-CCVAE model, 0.72 (95% CI, 0.68-0.77) for the 3D-CVAE model, 0.70 (95% CI, 0.66-0.75) for the 3D-CCAE model, and 0.69 (95% CI 0.65-0.74) for the without AE model. The 3D-CCVAE model performed better than the other models (3D-CVAE, p = 0.039; 3D-CCAE, p = 0.0032; and without AE, p = 0.0011). CONCLUSIONS: This study demonstrates the usefulness of the 3D-CCVAE in multimodal DL models learned using a small number of datasets. Additionally, it shows that dimensionality reduction via AE can be used to learn a DL model without increasing the overlearning risk. Moreover, the VAE mechanism can overcome the uncertainty of the model parameters that commonly occurs for small datasets, thereby eliminating the risk of overlearning. Additionally, more efficient dimensionality reduction of PET images can be performed by providing clinical data as conditions and ignoring clinical data-related features.

Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks.

The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupervised generative attentional networks with adaptive layer-instance normalisation for image-to-image translation (U-GAT-IT), which specialised in unpaired image transformation based on attention maps for image transformation. We added the modality-independent neighbourhood descriptor (MIND) to the loss of U-GAT-IT to guarantee anatomical consistency in the image transformation between different domains. Our proposed method obtained a synthesised computed tomography of the chest. Experimental results showed that our method outperforms current approaches. The study findings suggest the possibility of synthesising clinically acceptable computed tomography images from chest MRI with minimal changes in anatomical structures without human annotation.

Efficacy of myocardial washout of 99mTc-MIBI/Tetrofosmin for the evaluation of inflammation in patients with cardiac sarcoidosis: comparison with 18F-fluorodeoxyglucose positron emission tomography findings.

OBJECTIVE: Both myocardial perfusion scintigraphy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) are useful for the diagnosis of cardiac sarcoidosis (CS). However, the association between the washout of 99mTc-labeled tracer and FDG PET has not been established. This study aimed to evaluate the association between the washout of 99mTc-labeled tracer and FDG PET findings in patients with CS. METHODS: We retrospectively analyzed 64 patients (65.0 ± 11.2 years, 53% male) with suspected CS who underwent myocardial single-photon emission computed tomography (SPECT) with 99mTc-labeled tracer and FDG PET. The SPECT images were acquired at 15 min (early images) and 3 h (delayed images) after injection and scored visually using a 17-segment model with a 5-point scoring system. The washout score was defined as the difference between the early and delayed total defect scores. FDG positivity was considered as focal or focal on diffuse patterns on visual assessment, and FDG uptake was quantified by measuring the standardized uptake value (SUV) of each of the 17 segments. RESULTS: The washout score was significantly higher for the CS group than for the non-CS group (3.0 [-1.0-5.0] vs. 0.0 [-0.5-1.0], p = 0.010). Receiver operating characteristic analysis showed that a washout score of ≥ 2 had the best accuracy for detecting CS (88% sensitivity and 56% specificity) and FDG positivity (71% sensitivity and 89% specificity). In the segment-based analysis of 833 segments from 49 patients, excluding 15 patients with diffuse FDG uptake, the median SUVs for FDG uptake for the washout scores of ≤ 0, 1, and 2 were 2.3 (1.8-3.6), 4.2 (2.9-7.8), and 8.3 (6.5-9.4), respectively (p < 0.001). CONCLUSIONS: The washout of 99mTc-labeled tracer can be a useful marker for the evaluation of FDG PET findings in patients with CS.

Diversity and determinants of the sigmoid septum and its impact on morphology of the outflow tract as revealed using cardiac computed tomography.

BACKGROUND: The sigmoid septum has been generally evaluated subjectively and qualitatively, without detailed examination of its diversity, impact on the morphology of the left ventricular outflow tract (LVOT), and anatomical background. METHODS: We enrolled 100 patients without any background cardiac diseases (67.5 ± 12.8 years old; 43% women) who underwent cardiac computed tomography. Basal septal morphology was evaluated using antero-superior and medial bulging angles (bidirectional angulation of the basal septum relative to the LVOT). The eccentricity index of the LVOT, area narrowing ratio (LVOT/virtual basal ring area), aortic-to-left ventricular axial angle (angulation of the aortic root relative to the left ventricle), and wedged height (non-coronary aortic sinus to inferior epicardium distance) were also quantified. RESULTS: The antero-superior bulging, medial bulging, aortic-to-left ventricular axial angles, LVOT eccentricity index, area narrowing ratio, and wedged height were 76° ± 17°, 166° ± 27°, 127° ± 9°, 1.8 ± 0.5, 1.0 ± 0.2, and 41.2 ± 9.1 mm, respectively. Both bulging angles were correlated with each other and contributed to the narrowing and deformation of the LVOT. Angulated aortic root was not correlated with either bidirectional septal bulge or LVOT narrowing. Clockwise rotation of the aortic root rotation was an independent predictor of prominent antero-superior septal bulge. Deeper aortic wedging was a common independent predictor of bidirectional septal bulge. CONCLUSIONS: The extent of septal bulge varies in normal hearts. Along with deep aortic wedging, the bidirectional bulge of the basal septum deforms and narrows the LVOT without affecting the virtual basal ring morphology.

Varied Extent of Mitral Annular Disjunction Among Cases With Different Phenotypes of Mitral Valve Prolapse.

Detailed 3-dimensional analysis of mitral annular disjunction was undertaken in 3 comparative cases of mitral valve prolapse. A case of Barlow disease showed extensive disjunction, whereas cases of fibroelastic deficiency and forme fruste demonstrated less extensive disjunction. Considering the current controversies surrounding disjunction, these observations call for detailed research in the future. (Level of Difficulty: Advanced.).

Prognostic Impact of Myocardial Extracellular Volume Fraction Assessment Using Dual-Energy Computed Tomography in Patients Treated With Aortic Valve Replacement for Severe Aortic Stenosis.

Background Myocardial extracellular volume fraction (ECV), measured by cardiac magnetic resonance imaging, is a useful prognostic marker for patients who have undergone aortic valve replacement (AVR) for aortic stenosis. However, the prognostic significance of ECV measurements based on computed tomography (CT) is unclear. This study evaluated the association between ECV measured with dual-energy CT and clinical outcomes in patients with aortic stenosis who underwent transcatheter or surgical AVR. Methods and Results We retrospectively enrolled 95 consecutive patients (age, 84.0±5.0 years; 75% women) with severe aortic stenosis who underwent preprocedural CT for transcatheter AVR planning. ECV was measured using iodine density images obtained by delayed enhancement dual-energy CT. The primary end point was a composite outcome of all-cause death and hospitalization for heart failure after AVR. The mean ECV measured with CT was 28.1±3.8%. During a median follow-up of 2.6 years, 22 composite outcomes were observed, including 15 all-cause deaths and 11 hospitalizations for heart failure. In Kaplan-Meier analysis, the high ECV group (≥27.8% [median value]) had significantly higher rates of composite outcomes than the low ECV group (<27.8%) (log-rank test, P=0.012). ECV was the only independent predictor of adverse outcomes on multivariable Cox regression analysis (hazards ratio, 1.25; 95% CI, 1.10‒1.41; P<0.001). Conclusions Myocardial ECV measured with dual-energy CT in patients who underwent aortic valve intervention was an independent predictor of adverse outcomes after AVR.

Deep learning model for predicting gestational age after the first trimester using fetal MRI.

OBJECTIVES: To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD). MATERIALS AND METHODS: Our Institutional Review Board approved this retrospective study, and a total of 184 T2-weighted MRI acquisitions from 184 fetuses (mean gestational age: 29.4 weeks) who underwent MRI between January 2014 and June 2019 were included. The reference standard gestational age was based on the last menstruation and ultrasonography measurements in the first trimester. The deep learning model was trained with T2-weighted images from 126 training cases and 29 validation cases. The remaining 29 cases were used as test data, with fetal age estimated by both the model and BPD measurement. The relationship between the estimated gestational age and the reference standard was evaluated with Lin's concordance correlation coefficient (ρc) and a Bland-Altman plot. The ρc was assessed with McBride's definition. RESULTS: The ρc of the model prediction was substantial (ρc = 0.964), but the ρc of the BPD prediction was moderate (ρc = 0.920). Both the model and BPD predictions had greater differences from the reference standard at increasing gestational age. However, the upper limit of the model's prediction (2.45 weeks) was significantly shorter than that of BPD (5.62 weeks). CONCLUSIONS: Deep learning can accurately predict gestational age from fetal brain MR acquired after the first trimester. KEY POINTS: • The prediction of gestational age using ultrasound is accurate in the first trimester but becomes inaccurate as gestational age increases. • Deep learning can accurately predict gestational age from fetal brain MRI acquired in the second and third trimester. • Prediction of gestational age by deep learning may have benefits for prenatal care in pregnancies that are underserved during the first trimester.

Physiological skin FDG uptake: A quantitative and regional distribution assessment using PET/MRI.

PURPOSE: To retrospectively assess the repeatability of physiological F-18 labeled fluorodeoxyglucose (FDG) uptake in the skin on positron emission tomography/magnetic resonance imaging (PET/MRI) and explore its regional distribution and relationship with sex and age. METHODS: Out of 562 examinations with normal FDG distribution on whole-body PET/MRI, 74 repeated examinations were evaluated to assess the repeatability and regional distribution of physiological skin uptake. Furthermore, 224 examinations were evaluated to compare differences in the uptake due to sex and age. Skin segmentation on PET was performed as body-surface contouring on an MR-based attenuation correction map using an off-line reconstruction software. Bland-Altman plots were created for the repeatability assessment. Kruskal-Wallis test was performed to compare the maximum standardized uptake value (SUVmax) with regional distribution, age, and sex. RESULTS: The limits of agreement for the difference in SUVmean and SUVmax of the skin were less than 30%. The highest SUVmax was observed in the face (3.09±1.04), followed by the scalp (2.07±0.53). The SUVmax in the face of boys aged 0-9 years and 10-20 years (1.33±0.64 and 2.05±1.00, respectively) and girls aged 0-9 years (0.98±0.38) was significantly lower than that of men aged ≥20 years and girls aged ≥10 years (p<0.001). In women, the SUVmax of the face (2.31±0.71) of ≥70-year-olds was significantly lower than that of 30-39-year-olds (3.83±0.82) (p<0.05). CONCLUSION: PET/MRI enabled the quantitative analysis of skin FDG uptake with repeatability. The degree of physiological FDG uptake in the skin was the highest in the face and varied between sexes. Although attention to differences in body habitus between age groups is needed, skin FDG uptake also depended on age.

Prevalence and extent of mitral annular disjunction in structurally normal hearts: comprehensive 3D analysis using cardiac computed tomography.

AIMS: Mitral annular disjunction is fibrous separation between the attachment of the posterior mitral leaflet and the basal left ventricular myocardium initially described in dissected hearts. Currently, it is commonly evaluated by echocardiography, and potential relationships with mitral valve prolapse and ventricular arrhythmia have been suggested. However, controversy remains as its prevalence and extent have not been fully elucidated in normal living subjects. METHODS AND RESULTS: Systolic datasets of cardiac computed tomography obtained from 98 patients (mean age, 69.1 ± 12.6 years; 81% men) with structurally normal hearts were assessed retrospectively. Circumferential extent of both mitral leaflets and disjunction was determined by rotating orthogonal multiplanar reconstruction images around the central axis of the mitral valvar orifice. Distribution angle within the circumference of the mitral valvar attachment and maximal height of disjunction were quantified. In total, 96.0% of patients demonstrated disjunction. Average distribution angles of the anterior and posterior mitral leaflets were 91.3 ± 9.4° and 269.8 ± 9.7°, respectively. Average distribution angle of the disjunction was 105.1 ± 49.2°, corresponding to 39.0 ± 18.2% of the entire posterior mitral valvar attachment. Median value of the maximal height of disjunction was 3.0 (1.5-7.0) mm. Distribution prevalence map of the disjunction revealed characteristic double peaks, with frequent sites of the disjunction located at the anterior to antero-lateral and inferior to infero-septal regions. CONCLUSION: Mitral annular disjunction is a rather common finding in the normal adult heart with bimodal distribution predominantly observed involving the P1 and P3 scallops of the posterior mitral leaflet.

Normative Aortic Valvar Measurements in Adults Using Cardiac Computed Tomography　- A Potential Guide to Further Sophisticate Aortic Valve-Sparing Surgery.

BACKGROUND: A thorough understanding of the anatomy of the aortic valve is necessary for aortic valve-sparing surgery. Normal valvar dimensions and their relationships in the living heart, however, have yet to be fully investigated in a 3-dimensional fashion.Methods and Results:In total, 123 consecutive patients (66±12 years, Men 63%) who underwent coronary computed tomographic angiography were enrolled. Mid-diastolic morphology of the aortic roots, including height of the interleaflet triangles, geometric height, free margin length of each leaflet, effective height, and coaptation length were measured using multiplanar reconstruction images. Average height of the interleaflet triangle, geometric height, free margin length, effective height, and the coaptation length were 17.3±1.8, 14.7±1.3, 32.6±3.6, 8.6±1.4, and 3.2±0.8 mm, respectively. The right coronary aortic leaflet displayed the longest free margin length and shortest geometric height. Geometric height, free margin length, and effective height showed positive correlations with aortic root dimensions. Coaptation length, however, remained constant regardless of aortic root dimensions. CONCLUSIONS: Diversities, as well as characteristic relationships among each value involving the aortic root, were identified using living-heart datasets. The aortic leaflets demonstrated compensatory elongation along with aortic root dilatation to maintain constant coaptation length. These measurements will serve as the standard value for revealing the underlying mechanism of aortic regurgitation to plan optimal aortic valve-sparing surgery.

Feasibility of morphological assessment of coronary artery calcification with electrocardiography-gated non-contrast computed tomography: a comparative study with optical coherence tomography.

To investigate the feasibility of pre-procedural morphological assessment of coronary artery calcification in severely calcified lesions with electrocardiography (ECG)-gated non-contrast computed tomography (CT). Severely calcified coronary arteries in patients who underwent ECG-gated non-contrast CT prior to optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) were studied retrospectively. CT and OCT data were co-registered by marking landmark structures such as side branches and reviewed side by side with cross-sectional images. The maximum calcium angle (MCA) and presence of nodular calcification (NC) were evaluated. A total of 496 cross-sections in 16 lesions were included in this analysis. The Pearson correlation coefficient between CT- and OCT-derived MCA was 0.92 (p < 0.001). Bland-Altman plots of OCT-derived MCA in relation to CT-derived MCA showed a mean bias of 4.8 degrees with 95% limits of agreement of - 69.7 to 79.4 degrees. Sensitivity, specificity, and positive and negative predictive values of CT in identifying MCA > 270 degrees were 90.3%, 79.7%, 92.1%, and 97.4%, respectively. Sensitivity, specificity, and positive and negative predictive values of CT in identifying NC were 73.3%, 97.5%, 47.8%, and 99.2%, respectively. ECG-gated non-contrast coronary CT might be helpful to obtain detailed information of severe coronary artery calcification before PCI.

Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI.

We hypothesized that, in discrimination between benign and malignant parotid gland tumors, high diagnostic accuracy could be obtained with a small amount of imbalanced data when anomaly detection (AD) was combined with deep leaning (DL) model and the L2-constrained softmax loss. The purpose of this study was to evaluate whether the proposed method was more accurate than other commonly used DL or AD methods. Magnetic resonance (MR) images of 245 parotid tumors (22.5% malignant) were retrospectively collected. We evaluated the diagnostic accuracy of the proposed method (VGG16-based DL and AD) and that of classification models using conventional DL and AD methods. A radiologist also evaluated the MR images. ROC and precision-recall (PR) analyses were performed, and the area under the curve (AUC) was calculated. In terms of diagnostic performance, the VGG16-based model with the L2-constrained softmax loss and AD (local outlier factor) outperformed conventional DL and AD methods and a radiologist (ROC-AUC = 0.86 and PR-ROC = 0.77). The proposed method could discriminate between benign and malignant parotid tumors in MR images even when only a small amount of data with imbalanced distribution is available.

Revisiting the prevalence and diversity of localized thinning of the left ventricular apex.

BACKGROUND: The left ventricular apex commonly has a paper-thin structure. However, available data about its structure are limited to variable samples, methodologies, and results. OBJECTIVE: To investigate the structural anatomy of the left ventricular apex using living heart datasets with the latest computed tomography scanner. METHODS: One hundred thirty-one consecutive patients (median age, 73 years; 58% men) who underwent cardiac computed tomography were retrospectively analyzed. Patients with severe aortic stenosis were analyzed separately. Thickness and diameters of the thinnest part of the left ventricular apex during mid-diastole were measured using orthogonal multiplanar reconstruction images. The area of thinning was estimated using the formula for the ellipse. RESULTS: In 88 patients without severe aortic stenosis, the median thickness of the thinnest area of the left ventricular apex was only 0.9 mm. Among them, 74%, 99%, and 100% of cases displayed a left ventricular apex thinner than 1.0, 3.0, and 5.0 mm, respectively. The median area of the thinnest region was 5.6 mm2 . In 43 patients with severe aortic stenosis, the median thickness of the thinnest area of the left ventricular apex was 1.2 mm. Among them, 51%, 93%, and 100% of cases displayed a left ventricular apex thinner than 1.0, 3.0, and 5.0 mm, respectively. The median area of the thinnest region was 3.9 mm2 . CONCLUSIONS: Localized thinning of the left ventricular apex is unexceptional, regardless of aortic stenosis with concentric left ventricular hypertrophy, thus highlighting the need for a reappreciation of this feature to avoid inadvertent catastrophic complications.

Does Endovascular Abdominal Aortic Repair Change Psoas Muscle Volume?

BACKGROUND: Because endovascular abdominal aortic repair (EVAR) lowers the lumbar arterial blood flow, we hypothesized that the volume of the psoas muscle decreases after surgery. When internal iliac artery (IIA) embolization is performed, the lumbar arterial blood flow further decreases; therefore, we also hypothesized that the decrease in the volume of the psoas muscle becomes more significant. This study was performed to assess the volume change in the psoas muscle after EVAR. METHODS: Fifty-three consecutive patients who underwent EVAR from January 2016 to December 2016 were included. The psoas muscle volume was measured by preoperative and postoperative computed tomography (CT). Postoperative CT scans were performed 6-12 months after EVAR. Axial CT images with a 2-mm slice thickness were used to measure the psoas muscle volume. Data were transferred to a 3-dimensional workstation, and the psoas muscle volume was measured. RESULTS: In the EVAR group, the volume of the psoas muscle decreased by an average of 5.8 mL (4.6%) from 114.8 ± 32.0 mL preoperatively to 109.0 ± 30.3 mL postoperatively (P < 0.01). There was a significant difference in the change in the psoas muscle volume between patients with and without IIA embolization (embolization group: preoperative 118.1 ± 31.0 mL, postoperative 107.5 ± 29.2 mL, mean volume change rate -8.8%; nonembolization group: preoperative 114.0 ± 32.3 mL, postoperative 109.4 ± 30.7 mL, mean volume change rate -3.6%; P < 0.05). CONCLUSIONS: The psoas muscle volume is reduced with EVAR. Moreover, when the IIA is embolized, the psoas muscle volume is further reduced.

Filtered back projection revisited in low-kilovolt computed tomography angiography: sharp filter kernel enhances visualization of the artery of Adamkiewicz.

PURPOSE: The reliability of assessment of the artery of Adamkiewicz before the aortic repair is highly dependent on the display of the continuity of this artery with the aorta, mainly around the vertebral pedicle, by computed tomography angiography (CTA). We hypothesized that the sharp filter kernel can improve visualization of this continuity of the vessel structure because of its edge enhancement and high-spatial resolution. This study was performed to compare the subjective and objective image quality of spinal CTA reconstructed with sharp and smooth filter kernels. METHODS: We retrospectively reviewed 40 consecutive patients who had undergone 80-kV CTA to detect the artery of Adamkiewicz before aortic repair. We measured the CT number and the contrast-to-noise ratio of the anterior spinal artery to the spinal cord. Furthermore, the continuity of the artery of Adamkiewicz was evaluated using a 3-point scale (2 points, absolute; 0 points, undetectable). RESULTS: CTA with the sharp filter kernel showed a significantly higher CT number and contrast-to-noise ratio of the spinal artery than did CTA with the smooth filter kernel (P < .001 for both). Moreover, the sharp filter kernel showed a significantly higher continuity of the artery of Adamkiewicz with the aorta than did the smooth filter kernel (P < .001). CONCLUSIONS: The sharp filter kernel significantly improved the image quality in low-tube-voltage CTA for the assessment of the artery of Adamkiewicz. Thus, CTA with the sharp filter kernel can generate a high-confidence level in the evaluation of the artery of Adamkiewicz.

Focal Myocardial Damage in Cardiac Sarcoidosis Characterized by Strain Analysis on Magnetic Resonance Tagged Imaging in Comparison with Fluorodeoxyglucose Positron Emission Tomography Accumulation and Magnetic Resonance Late Gadolinium Enhancement.

OBJECTIVE: The aims of this study were to characterize focal myocardial damage of cardiac sarcoidosis by strain analysis and to compare it with late gadolinium enhancement (LGE) and fluorodeoxyglucose (FDG) positron emission tomography. METHODS: We reviewed 208 segments from 13 cardiac sarcoidosis patients and measured the circumferential strain (Ecc) and the strain change per second (Ecc rate). The mean Ecc and Ecc rate values were compared between the FDG(+) and FDG(-), and the LGE(+) and LGE(-) segments using Welch's t test. RESULTS: The peak and max Ecc rates were better in the LGE(-) segments than in the LGE(+) segments (-11.8 vs -8.9%, 40.5 vs 29.7%/s, both P < 0.001). The max Ecc rate was higher in the FDG(-) segments than in the FDG(+) segments (39.2 vs 31.7%/s, P < 0.001), but the peak Ecc did not differ between the FDG(+) and FDG(-) segments (-11.2 vs -10.1%, P = 0.17). CONCLUSIONS: Strain analysis could reveal focal myocardial damage in the FDG(+) or the LGE(+) segments.