Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such as that of the ...prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. To address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools-DenseNet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-weighted images in a four-fold cross-validation fashion. The networks were trained and tested on weakly and accurately annotated masks separately to test the hypothesis that the network can learn even when the labels are not accurate. The network successfully detects the prostate region and segments the gland and its zones. Compared with U-net, the second version of our algorithm, Dense-2 U-net, achieved an average Dice score for the whole prostate of 92.1± 0.8% vs. 90.7 ± 2%, for the central zone of Formula: see text% vs. Formula: see text %, and for the peripheral zone of 78.1± 2.5% vs. Formula: see text%. Our initial results show Dense-2 U-net to be more accurate than state-of-the-art U-net for automatic segmentation of the prostate and prostate zones.
Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the ...clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.
Objectives
Multiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting ...the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade.
Methods
We retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements.
Results
Fractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2–5) cancer with a sensitivity of 91% (confidence interval CI: 83–96%) and a specificity of 86% (CI: 73–94%). FD correlated linearly with ISUP groups (
r
2
= 0.874,
p
< 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1–4 (
p
≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUC
FD
= 0.97 versus AUC
ADC
= 0.77,
p
< 0.001).
Conclusion
Fractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors.
Key Points
• In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1–4)
.
• Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83–96%) and a specificity of 86% (73–94%)
.
• Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading
.
Objectives
To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and ...model-based iterative reconstruction (FIRST).
Methods
Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient’s abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient ≥ 0.75), discriminative power (Kruskal-Wallis test
p
value < 0.05), and repeatability (overall concordance correlation coefficient ≥ 0.75).
Results
The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses ≥ 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features.
Conclusions
AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features.
Key Points
•
Image quality of CT images reconstructed with filtered back projection and iterative methods is inadequate for the majority of radiomics features due to inconsistent tissue characterization, low discriminative power, or low repeatability.
•
Deep learning reconstruction enhances image quality for radiomics and more than doubled the feature yield at doses that are typically used in clinical CT imaging.
•
Image reconstruction algorithms can optimize image quality for more reliable quantification of tissues in CT images.
Objectives
To introduce a novel hypothesis and method to characterise pathomechanisms underlying myocardial ischemia in chronic ischemic heart disease by local fractal analysis (FA) of the ischemic ...myocardial transition region in perfusion imaging.
Methods
Vascular mechanisms to compensate ischemia are regulated at various vascular scales with their superimposed perfusion pattern being hypothetically self-similar. Dedicated FA software (“FraktalWandler”) has been developed. Fractal dimensions during first-pass (FD
first-pass
) and recirculation (FD
recirculation
) are hypothesised to indicate the predominating pathomechanism and ischemic severity, respectively.
Results
Twenty-six patients with evidence of myocardial ischemia in 108 ischemic myocardial segments on magnetic resonance imaging (MRI) were analysed. The 40th and 60th percentiles of FD
first-pass
were used for pathomechanical classification, assigning lesions with FD
first-pass
≤ 2.335 to predominating coronary microvascular dysfunction (CMD) and ≥2.387 to predominating coronary artery disease (CAD). Optimal classification point in ROC analysis was FD
first-pass
= 2.358. FD
recirculation
correlated moderately with per cent diameter stenosis in invasive coronary angiography in lesions classified CAD (
r
= 0.472,
p
= 0.001) but not CMD (
r
= 0.082,
p
= 0.600).
Conclusions
The ischemic transition region may provide information on pathomechanical composition and severity of myocardial ischemia. FA of this region is feasible and may improve diagnosis compared to traditional noninvasive myocardial perfusion analysis.
Key Points
•
A novel hypothesis and method is introduced to pathophysiologically characterise myocardial ischemia.
•
The ischemic transition region appears a meaningful diagnostic target in perfusion imaging.
•
Fractal analysis may characterise pathomechanical composition and severity of myocardial ischemia.
Objectives
To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography ...(CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application.
Methods
A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science.
Results
Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis.
Conclusions
Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting.
Key Points
•
Fractal analysis of perfusion images can be successfully performed.
•
Tumour, pulmonary, myocardial, renal, skeletal muscle and cerebral perfusion have already been examined.
•
Clinical applications of fractal analysis include tumour and brain perfusion assessment.
•
Fractal analysis is a suitable method for quantifying perfusion heterogeneity.
•
Fractal analysis requires further research concerning the development of clinical applications.
Fractal analysis of dynamic, four-dimensional computed tomography myocardial perfusion (4D-CTP) imaging might have potential for noninvasive differentiation of microvascular ischemia and ...macrovascular coronary artery disease (CAD) using fractal dimension (FD) as quantitative parameter for perfusion complexity. This multi-center proof-of-concept study included 30 rigorously characterized patients from the AMPLIFiED trial with nonoverlapping and confirmed microvascular ischemia (n
= 10), macrovascular CAD (n
= 10), or normal myocardial perfusion (n
= 10) with invasive coronary angiography and fractional flow reserve (FFR) measurements as reference standard. Perfusion complexity was comparatively high in normal perfusion (FD
= 4.49, interquartile range IQR:4.46-4.53), moderately reduced in microvascular ischemia (FD
= 4.37, IQR:4.36-4.37), and strongly reduced in macrovascular CAD (FD
= 4.26, IQR:4.24-4.27), which allowed to differentiate both ischemia types, p < 0.001. Fractal analysis agreed excellently with perfusion state (κ = 0.96, AUC = 0.98), whereas myocardial blood flow (MBF) showed moderate agreement (κ = 0.77, AUC = 0.78). For detecting CAD patients, fractal analysis outperformed MBF estimation with sensitivity and specificity of 100% and 85% versus 100% and 25%, p = 0.02. In conclusion, fractal analysis of 4D-CTP allows to differentiate microvascular from macrovascular ischemia and improves detection of hemodynamically significant CAD in comparison to MBF estimation.
Background
Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the ...impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.
Results
After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman’s rho: 0.83,
p
< 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient
K
trans
as surrogate parameter for motion artifacts. Mean
K
trans
decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (
p
< 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5,
p
< 0.001).
Conclusions
Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.
Key points
PCA-based registration improved motion-related image quality according to subjective and objective criteria.
The impact of registration was positively correlated with motion severity.
Registration improved perfusion quantification by reducing model-related uncertainty.
Combined computed tomography–derived myocardial blood flow (CTP-MBF) and computed tomography angiography (CTA) has shown good diagnostic performance for detection of coronary artery disease (CAD). ...However, fractal analysis might provide additional insight into ischemia pathophysiology by characterizing multiscale perfusion patterns and, therefore, may be useful in diagnosing hemodynamically significant CAD.
The purpose of this study was to investigate, in a multicenter setting, whether fractal analysis of perfusion improves detection of hemodynamically relevant CAD over myocardial blood flow quantification (CTP-MBF) using dynamic, 4-dimensional, dynamic stress myocardial computed tomography perfusion (CTP) imaging.
In total, 7 centers participating in the prospective AMPLIFiED (Assessment of Myocardial Perfusion Linked to Infarction and Fibrosis Explored with Dual-source CT) study acquired CTP and CTA data in patients with suspected or known CAD. Hemodynamically relevant CAD was defined as ≥90% stenosis on invasive coronary angiography or fractional flow reserve <0.80. Both fractal analysis and CTP-MBF quantification were performed on CTP images and were combined with CTA results.
This study population included 127 participants, among them 61 patients, or 79 vessels, with CAD as per invasive reference standard. Compared with the combination of CTP-MBF and CTA, combined fractal analysis and CTA improved sensitivity on the per-patient level from 84% (95% CI: 72%-92%) to 95% (95% CI: 86%-99%; P = 0.01) and specificity from 70% (95% CI: 57%-82%) to 89% (95% CI: 78%-96%; P = 0.02). The area under the receiver-operating characteristic curve improved from 0.83 (95% CI: 0.75-0.90) to 0.92 (95% CI: 0.86-0.98; P = 0.01).
Fractal analysis constitutes a quantitative and pathophysiologically meaningful approach to myocardial perfusion analysis using dynamic stress CTP, which improved diagnostic performance over CTP-MBF when combined with anatomical information from CTA.
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Background
To investigate whether fractal analysis of perfusion differentiates hepatocellular adenoma (HCA) subtypes and hepatocellular carcinoma (HCC) in non-cirrhotic liver by quantifying perfusion ...chaos using four-dimensional dynamic contrast-enhanced magnetic resonance imaging (4D-DCE-MRI).
Results
A retrospective population of 63 patients (47 female) with histopathologically characterized HCA and HCC in non-cirrhotic livers was investigated. Our population consisted of 13 hepatocyte nuclear factor (HNF)-1α-inactivated (H-HCAs), 7 β-catenin-exon-3-mutated (b
ex3
-HCAs), 27 inflammatory HCAs (I-HCAs), and 16 HCCs. Four-dimensional fractal analysis was applied to arterial, portal venous, and delayed phases of 4D-DCE-MRI and was performed in lesions as well as remote liver tissue. Diagnostic accuracy of fractal analysis was compared to qualitative MRI features alone and their combination using multi-class diagnostic accuracy testing including kappa-statistics and area under the receiver operating characteristic curve (AUC). Fractal analysis allowed quantification of perfusion chaos, which was significantly different between lesion subtypes (multi-class AUC = 0.90,
p
< 0.001), except between I-HCA and HCC. Qualitative MRI features alone did not allow reliable differentiation between HCA subtypes and HCC (κ = 0.35). However, combining qualitative MRI features and fractal analysis reliably predicted the histopathological diagnosis (κ = 0.89) and improved differentiation of high-risk lesions (i.e., HCCs, b
ex3
-HCAs) and low-risk lesions (H-HCAs, I-HCAs) from sensitivity and specificity of 43% (95% confidence interval CI 23–66%) and 47% (CI 32–64%) for qualitative MRI features to 96% (CI 78–100%) and 68% (CI 51–81%), respectively, when adding fractal analysis.
Conclusions
Combining qualitative MRI features with fractal analysis allows identification of HCA subtypes and HCCs in patients with non-cirrhotic livers and improves differentiation of lesions with high and low risk for malignant transformation.
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