scholarly journals Evaluation of the clinical utility of maximum intensity projections of 3D contrast‐enhanced , T1 ‐weighted imaging for the detection of brain metastases

2020 ◽  
Vol 3 (5) ◽  
Author(s):  
Nicolin Hainc ◽  
Christian Federau ◽  
Anthony Tyndall ◽  
Andreas Mittermeier ◽  
Andrea Bink ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Clinical Utility ◽  
Maximum Intensity ◽  
Contrast Enhanced ◽  
Maximum Intensity Projections

Coarctation of the abdominal aorta: Demonstration of a rare anomaly using contrast-enhanced MR-angiography

VASA ◽  
2009 ◽  
Vol 38 (1) ◽  
pp. 66-71 ◽  
Author(s):  
Schubert
Keyword(s):  
Abdominal Aorta ◽  
Mr Angiography ◽  
Parallel Imaging ◽  
Left Renal Vein ◽  
Systemic Hypertension ◽  
Contrast Enhanced ◽  
Younger Age ◽  
Maximum Intensity Projections ◽  
Collateral Vessels ◽  
First Time

We describe a case of aortic coarctation at the level of the infrarenal abdominal aorta which is encountered in less than six individuals in one million. In contrast to aortic narrowing above or including the renal arteries, this seems to be a relatively benign anomaly without systemic hypertension or impaired renal function. For the first time in this type of anomaly, contrast-enhanced MR angiography (ce-MRA) on a multi-receiver channel MR system, with an 8-channel phased array coil and parallel imaging was used. Ce-MRA displayed a tortuous, narrowed aortic segment that was found to be associated with mesenteric artery stenosis and compression of the orthotopic left renal vein, also known as the nutcracker phenomenon. All major aortic branches could be depicted using 3D surface-shaded displays and subvolume maximum intensity projections (MIPs). Collateral vessels of the abdominal wall were identified using whole-volume MIPs. Since the majority of aortic malformations are diagnosed at a younger age, and many suffer from renal insufficiency, we conclude that ce-MRA will eventually place conventional DSA as the modality of choice in malformations of the abdominal aorta.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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