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.

Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography.

RATIONALE AND OBJECTIVES: Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. MATERIALS AND METHODS: We reviewed data of 269 consecutive patients who underwent coronary computed tomography (CT). We excluded patients with metal implants, pleural effusion, history of thoracic surgery, or malignancy. Thus, the data of 191 patients were used. We generated PFCIs from the projection of three-dimensional CT images, wherein fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. RESULTS: The mean SSIM, MSE, and MAE were 8.56 × 10-1, 1.28 × 10-2, and 3.57 × 10-2, respectively, for the proposed model, and 7.62 × 10-1, 1.98 × 10-2, and 5.04 × 10-2, respectively, for the single CycleGAN-based model. CONCLUSION: PFCIs generated from CXRs with the proposed model showed better performance than those generated with the single model. The evaluation of PF without CT may be possible using the proposed method.

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.

Comparison of Conventional Imaging and 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in the Diagnostic Accuracy of Staging in Patients with Intrahepatic Cholangiocarcinoma.

We aimed to examine the accuracy of tumor staging of intrahepatic cholangiocarcinoma (ICC) by using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT). From January 2001 to December 2021, 202 patients underwent PET-CT, CT, and MRI for the initial staging of ICC in two institutions. Among them, 102 patients had undergone surgical treatment. Ninety patients who had a histopathological diagnosis of ICC were retrospectively reviewed. The sensitivity and specificity of 18F-FDG PET-CT, CT, and magnetic resonance imaging (MRI) in detecting tumors, satellite focus, vascular invasion, and lymph node metastases were analyzed. Ninety patients with histologically diagnosed ICC were included. PET-CT demonstrated no statistically significant advantage over CT and MR in the diagnosis of multiple tumors and macrovascular invasion, and bile duct invasion. The overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET-CT in lymph node metastases were 84%, 86%, 91%, 84%, and 86%, respectively. PET-CT revealed a significantly higher accuracy compared to CT or MRI (86%, 67%, and 76%, p < 0.01, respectively) in the diagnosis of regional lymph node metastases. The accuracy of tumor staging by PET-CT was higher than that by CT/MRI (PET-CT vs. CT vs. MRI: 68/90 vs. 47/90 vs. 51/90, p < 0.05). 18F-FDG PET-CT had sensitivity and specificity values for diagnosing satellite focus and vascular and bile duct invasion similar to those of CT or MRI; however, PET-CT showed higher accuracy in diagnosing regional lymph node metastases. 18F-FDG PET-CT exhibited higher tumor staging accuracy than that of CT/MRI. Thus, 18FDG PET-CT may support tumor staging in ICC.

Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis.

OBJECTIVES: This study analyzed an artificial intelligence (AI) deep learning method with a three-dimensional deep convolutional neural network (3D DCNN) in regard to diagnostic accuracy to differentiate malignant pleural mesothelioma (MPM) from benign pleural disease using FDG-PET/CT results. RESULTS: For protocol A, the area under the ROC curve (AUC)/sensitivity/specificity/accuracy values were 0.825/77.9% (81/104)/76.4% (55/72)/77.3% (136/176), while those for protocol B were 0.854/80.8% (84/104)/77.8% (56/72)/79.5% (140/176), for protocol C were 0.881/85.6% (89/104)/75.0% (54/72)/81.3% (143/176), and for protocol D were 0.896/88.5% (92/104)/73.6% (53/72)/82.4% (145/176). Protocol D showed significantly better diagnostic performance as compared to A, B, and C in ROC analysis (p = 0.031, p = 0.0020, p = 0.041, respectively). MATERIALS AND METHODS: Eight hundred seventy-five consecutive patients with histologically proven or suspected MPM, shown by history, physical examination findings, and chest CT results, who underwent FDG-PET/CT examinations between 2007 and 2017 were investigated in a retrospective manner. There were 525 patients (314 MPM, 211 benign pleural disease) in the deep learning training set, 174 (102 MPM, 72 benign pleural disease) in the validation set, and 176 (104 MPM, 72 benign pleural disease) in the test set. Using AI with PET/CT alone (protocol A), human visual reading (protocol B), a quantitative method that incorporated maximum standardized uptake value (SUVmax) (protocol C), and a combination of PET/CT, SUVmax, gender, and age (protocol D), obtained data were subjected to ROC curve analyses. CONCLUSIONS: Deep learning with 3D DCNN in combination with FDG-PET/CT imaging results as well as clinical features comprise a novel potential tool shows flexibility for differential diagnosis of MPM.

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.

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.

Genome-wide study on the polysomic genetic factors conferring plasticity of flower sexuality in hexaploid persimmon.

Sexuality is one of the fundamental mechanisms that work towards maintaining genetic diversity within a species. In diploid persimmons (Diospyros spp.), separated sexuality, the presence of separate male and female individuals (dioecy), is controlled by the Y chromosome-encoded small-RNA gene, OGI. On the other hand, sexuality in hexaploid Oriental persimmon (Diospyros kaki) is more plastic, with OGI-bearing genetically male individuals, able to produce both male and female flowers (monoecy). This is thought to be linked to the partial inactivation of OGI by a retrotransposon insertion, resulting in DNA methylation of the OGI promoter region. To identify the genetic factors regulating branch sexual conversion, genome-wide correlation/association analyses were conducted using ddRAD-Seq data from an F1 segregating population, and using both quantitative and diploidized genotypes, respectively. We found that allelic ratio at the Y-chromosomal region, including OGI, was correlated with male conversion based on quantitative genotypes, suggesting that OGI can be activated in cis in a dosage-dependent manner. Genome-wide association analysis based on diploidized genotypes, normalized for the effect of OGI allele dosage, detected three fundamental loci associated with male conversion. These loci underlie candidate genes, which could potentially act epigenetically for the activation of OGI expression.

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.

Application of dual-energy CT to suppression of metal artefact caused by pedicle screw fixation in radiotherapy: a feasibility study using original phantom.

The objective of the present study was the determination of the potential dosimetric benefits of using metal-artefact-suppressed dual-energy computed tomography (DECT) images for cases involving pedicle screw implants in spinal sites. A heterogeneous spinal phantom was designed for the investigation of the dosimetric effect of the pedicle-screw-related artefacts. The dosimetric comparisons were first performed using a conventional two-directional opposed (AP-PA) plan, and then a volumetric modulated arc therapy (VMAT) plan, which are both used for the treatment of spinal metastases in our institution. The results of Acuros® XB dose-to-medium (Dm) and dose-to-water (Dw) calculations using different imaging options were compared with experimental measurements including the chamber and film dosimetries in the spinal phantom. A dual-energy composition image with a weight factor of -0.2 and a dual-energy monochromatic image (DEMI) with an energy level of 180 keV were found to have superior abilities for artefact suppression. The Dm calculations revealed greater dosimetric effects of the pedicle screw-related artefacts compared to the Dw calculations. The results of conventional single-energy computed tomography showed that, although the pedicle screws were made from low-Z titanium alloy, the metal artefacts still have dosimetric effects, namely, an average (maximum) Dm error of 4.4% (5.6%) inside the spinal cord for a complex VMAT treatment plan. Our findings indicate that metal-artefact suppression using the proposed DECT (DEMI) approach is promising for improving the dosimetric accuracy near the implants and inside the spinal cord (average (maximum) Dm error of 1.1% (2.0%)).

Relationship between cardiac calcification and left ventricular hypertrophy in patients with chronic kidney disease at hemodialysis initiation.

Coronary artery calcification (CAC), cardiac valve calcification (CVC) and left ventricular hypertrophy (LVH) are frequently observed in chronic kidney disease (CKD) patients. These abnormalities significantly affect morbidity and mortality. The aim of this study was to investigate the relationship between CAC, CVC and LVH in CKD patients. This study included 96 patients who were hospitalized and initiated hemodialysis between December 2011 and July 2014 at our five institutions. Multi-detector computed tomography for the quantification of CAC using the Agatston score and transthoracic echocardiography for assessing CVC and LVH were performed for all patients included in the study. We semi-quantitatively evaluated the severity of CVC as a valvular calcification score. We also assessed the presence of LVH in patients with CAC and/or CVC. Among the 96 patients, the prevalence of CAC was 81.3% and CVC was 65.0%. The severity of CAC was closely and significantly associated with that of CVC. The percentage of patients with LVH was the greatest in those with both severe CAC and CVC. CAC was significantly more severe in patients with concentric hypertrophy compared to those with normal geometry. At the initiation of hemodialysis, most CKD patients had CAC, CVC and LVH. In addition, cardiac calcification was significantly associated with LVH in these patients.

An Isolated Case of Late-onset Amyloidogenic Transthyretin Type Familial Amyloid Polyneuropathy Associated with a Mutant Transthyretin Substituting Methionine for Valine at Position 30 Showing Latent Progressive Cardiac Involvement Confirmed by Serial Annual Electrocardiograms.

Late-onset amyloidogenic transthyretin (ATTR) type familial amyloid polyneuropathy (FAP) shows features distinct from those of early-onset hereditary ATTR type FAP. We herein describe an asymptomatic 68-year-old man with late-onset ATTR type FAP whose serial annual electrocardiograms demonstrated progressive left bundle branch block. Latent but severe cardiac involvement seems to be one feature of late-onset ATTR type FAP, similar to senile systemic amyloidosis (SSA). Early differential diagnosis of late-onset ATTR type FAP from SSA is important because, currently, only the former has new therapeutic options available in Japan. The present case report, therefore, highlights the necessity of careful observation for periodic electrocardiograms.

Integrating CT Myocardial Perfusion and CT-FFR in the Work-Up of Coronary Artery Disease.

OBJECTIVES: The aim of this study was to investigate the individual and combined accuracy of dynamic computed tomography (CT) myocardial perfusion imaging (MPI) and computed tomography angiography (CTA) fractional flow reserve (FFR) for the identification of functionally relevant coronary artery disease (CAD). BACKGROUND: Coronary CTA has become an established diagnostic test for ruling out CAD, but it does not allow interpretation of the hemodynamic severity of stenotic lesions. Two recently introduced functional CT techniques are dynamic MPI and CTA FFR using computational fluid dynamics. METHODS: From 2 institutions, 74 patients (n = 62 men, mean age 61 years) planned for invasive angiography with invasive FFR measurement in 142 vessels underwent CTA imaging and dynamic CT MPI during adenosine vasodilation. A patient-specific myocardial blood flow index was calculated, normalized to remote myocardial global left ventricular blood flow. CTA FFR was computed using an on-site, clinician-operated application. Using binary regression, a single functional CT variable was created combining both CT MPI and CTA FFR. Finally, stepwise diagnostic work-up of CTA FFR with selective use of CT MPI was simulated. The diagnostic performance of CT MPI, CTA FFR, and CT MPI integrated with CTA FFR was evaluated using C statistics with invasive FFR, with a threshold of 0.80 as a reference. RESULTS: Sensitivity, specificity, and accuracy were 73% (95% confidence interval [CI]: 61% to 86%), 68% (95% CI: 56% to 80%), and 70% (95% CI: 62% to 79%) for CT MPI and 82% (95% CI: 72% to 92%), 60% (95% CI: 48% to 72%), and 70% (63% to 80%) for CTA FFR. For CT MPI integrated with CTA FFR, diagnostic accuracy was 79% (95% CI: 71% to 87%), with improvement of the area under the curve from 0.78 to 0.85 (p < 0.05). Accuracy of the stepwise approach was 77%. CONCLUSIONS: CT MPI and CTA FFR both identify functionally significant CAD, with comparable accuracy. Diagnostic performance can be improved by combining the techniques. A stepwise approach, reserving CT MPI for intermediate CTA FFR results, also improves diagnostic performance while omitting nearly one-half of the population from CT MPI examinations.

Serum phosphate is an independent predictor of the total aortic calcification volume in non-hemodialysis patients undergoing cardiovascular surgery.

BACKGROUND: A high serum phosphate level is a well-known risk factor for vascular calcification (VC) in patients on hemodialysis (HD). However, the association between the serum phosphate level and VC in non-HD patients is unclear. Our aim was to assess the impact of serum phosphate level on aortic calcification (AC) volume in non-HD patients undergoing cardiovascular surgery. METHODS: A total of 117 patients who underwent thoracoabdominal computed tomography as a preoperative general evaluation before cardiovascular surgery were enrolled. The total AC volume was quantified using the volume-rendering method by extracting the area ≥130HU within the entire aorta. The total AC volume index (AC-VI) was estimated as the total AC volume divided by the body surface area. RESULTS: In the 117 patients (64.7±13.1 years, 39% women), the median total AC-VI was 1.23mL/m(2). The mean estimated glomerular filtration rate (eGFR), adjusted serum calcium levels, and serum phosphate levels were 63.8±19.9mL/min/1.73m(2), 9.1±0.4mg/dL, and 3.6±0.6mg/dL, respectively. When the patients were classified into four quartiles based on their total AC-VI value, the serum phosphate level showed a positive correlation with a probability of being in the highest AC-VI quartile (R(2)=0.0146, p=0.0383) whereas the adjusted serum calcium level did not show a significant correlation (R(2)=0.0040, p=0.2615). A similar relationship between the serum phosphate level, adjusted serum calcium level, and AC-VI was confirmed when the total AC-VI was divided into the thoracic AC-VI and abdominal AC-VI. Multivariate analysis indicated that the serum phosphate level was an independent positive predictor of higher total AC-VI quartiles (ß=0.8013, p=0.0160). CONCLUSIONS: An increase in serum phosphate level was associated with an increased AC burden in non-HD patients undergoing cardiovascular surgery.

Dynamic Blood Oxygen Level-dependent MR Imaging of Muscle: Comparison of Postocclusive Reactive Hyperemia in Young Smokers and Nonsmokers.

PURPOSE: The role of early stage functional assessment of muscle blood flow response (MFR) by dynamic muscle blood oxygen level-dependent (BOLD) magnetic resonance (MR) imaging is unknown. We investigated the effect of smoking on vascular function according to MFR derived from dynamic muscle BOLD MR imaging during postocclusive reactive hyperemia in young smokers and nonsmokers. METHODS: Sixteen healthy male volunteers (8 smokers, 8 nonsmokers; mean age, 30.4 ± 4.6 years) underwent BOLD MR imaging of the left calf muscle. During reactive hyperemia provoked by a cuff-compression technique, we measured muscle BOLD (mB) using a 3-tesla single-shot multi-echo gradient-echo echo-planar imaging sequence. The 2 key mB variables in the reactive hyperemic phase that we studied were times to half hyperemic peak (T(1/2peak)) and peak (TTP), each measured from cuff deflation. We used the Welch test to assess differences in these between smokers and nonsmokers. RESULTS: T(1/2peak) and TTP were significantly longer in smokers (P < 0.05) in reactive hyperemia. T(1/2peak) was 13.8 ± 5.4 s in smokers and 7.6 ± 1.5 s in nonsmokers, and TTP was 67.5 ± 18.8 s in smokers and 45.4 ± 7.1 s in nonsmokers. CONCLUSION: Dynamic BOLD MR imaging of calf muscle during postocclusive reactive hyperemia demonstrated statistically significant differences in T(1/2peak) and TTP between young smokers and nonsmokers, indicating the presence of early stage smoking-related deterioration in MFR.

Bone marrow magnetic resonance imaging of the clivus in pediatric leukemia patients and normal controls.

PURPOSE: An MRI-based pre-test to determine the probability of pediatric leukemia prior to bone marrow aspiration would be useful to prevent unnecessary exposure to this invasive test. We aimed to evaluate the clivus-to-pons signal intensity ratio (CPR) and visual scoring (VS) on T1-weighted images (T1WI) and diffusion-weighted images (DWI) to distinguish pediatric leukemia patients from normal controls. MATERIALS AND METHODS: We retrospectively reviewed 1.5-T brain MR images of 13 consecutive leukemia patients (3 girls, 10 boys; mean age, 8.23 years; range, 1-17 years) and 40 age- and gender-matched normal controls. We evaluated differences between leukemia patients and normal controls using Wilcoxon rank-sum and Mann-Whitney U tests with respect to the following parameters: (1) CPR on T1WI (CPRT1WI); (2) CPR on DWI (CPRDWI); (3) VS on T1WI (VST1WI); and (4) VS on DWI (VSDWI). RESULTS: The CPRT1WI values for leukemia patients and normal controls were 0.77 ± 0.12 and 1.39 ± 0.47, respectively (P < 0.001). The corresponding CPRDWI values were 1.03 ± 0.38 and 0.50 ± 0.17, respectively (P < 0.001). VST1WI and VSDWI were significantly different between the groups (P < 0.001 for both). CONCLUSION: MRI-based quantitative and qualitative analyses of clival bone marrow on T1WI and DWI can distinguish pediatric leukemia patients from normal subjects.

Relative myocardial blood flow by dynamic computed tomographic perfusion imaging predicts hemodynamic significance of coronary stenosis better than absolute blood flow.

OBJECTIVES: Quantitative myocardial perfusion imaging by computed tomography (CT) was recently introduced to calculate myocardial blood flow (MBF). Because absolute MBF thresholds may be affected by technique, methodology, and the microvasculature, we investigated whether a relative measure of MBF improves accuracy to identify hemodynamically significant coronary stenosis. MATERIALS AND METHODS: In this prospective study, 42 patients (mean [SD] age, 62.3[8.7] years; 8 women) with suspected or known coronary disease underwent dynamic CT myocardial perfusion imaging using adenosine vasodilation, before invasive angiography (coronary angiography) with fractional flow reserve (FFR). Within each myocardial territory MBF, the MBF relative to remote myocardium (MBFratio) was calculated and compared with coronary angiography and FFR. RESULTS: Of the 91 vessels interrogated by FFR (median, 0.81; interquartile range, 0.73-0.94), 45 vessels (49%) had an FFR value lower than 0.8 and were considered hemodynamically significant. Hyperemic MBF was lower in ischemic territories: 75.6 ± 22.5 mL per 100 mL/min versus 98.3 ± 23.1 mL per 100 mL/min (P < 0.0001). The MBFratio correlated better with FFR (P = 0.76) than the absolute MBF did (P = 0.52). Receiver operating curve analysis showed better discrimination by MBFratio: area under the curve of 0.85 versus 0.75 (P = 0.02). The MBF of remote myocardium varied between 60.7 and 167.2 mL per 100 mL/min and was lower in patients without heart rate acceleration (P = 0.0035). CONCLUSIONS: The MBFratio seems to better identify hemodynamically significant coronary artery disease than does the absolute MBF determined by dynamic CT perfusion imaging. This may be caused by microvascular status or related to the methodology.