Collimator optimization for lesion detection incorporating prior information about lesion size

1995 ◽  
Vol 22 (6) ◽  
pp. 703-713 ◽  
Author(s):  
Stephen C. Moore ◽  
Daniel J. deVries ◽  
Balgobin Nandram ◽  
Marie Foley Kijewski ◽  
Stefan P. Mueller
Keyword(s):  
Prior Information ◽  
Lesion Size ◽  
Lesion Detection ◽  
Collimator Optimization

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study

2021 ◽  
Author(s):  
Xiao Luo PhD ◽  
Min Xu ◽  
Guoxue Tang ◽  
Yi Wang PhD ◽  
Na Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Tissue ◽  
Test Group ◽  
Lesion Size ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
Detection Sensitivity ◽  
Control Group ◽  
Factors Affecting ◽  
Handheld Ultrasound

Objectives: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy. Methods: Women who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimised to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns. Results: In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8%, 97.2 and 100%, based on volume, lesion and patient, respectively, with 1.9 FPs per volume. In the test group with 247 LRs, the sensitivities were 92.7%, 94.5 and 96.5%, respectively, with 2.4 FPs per volume. The control group, with 900 volumes, showed 0.24 FPs per volume. The sensitivity was 98% for lesions > 1 cm3, but 87% for those ≤1 cm3 (p < 0.05). Similar sensitivities and FPs were observed for different breast tissue compositions (homogeneous, 97.5%, 2.1; heterogeneous, 93.6%, 2.1), lesion morphologies (mass, 96.3%, 2.1; non-mass, 95.8%, 2.0) and echo patterns (homogeneous, 96.1%, 2.1; heterogeneous 96.8%, 2.1). Conclusions: DL had high detection sensitivity with a low FP but was affected by lesion size. Advances in knowledge: DL is technically feasible for the automatic detection of lesions in ABUS.

Real-Time Elastography of Hepatic Thermal Lesions In Vitro: Histopathological Correlation

2011 ◽  
Vol 33 (02) ◽  
pp. 170-174 ◽  
Author(s):  
P. Wiggermann ◽  
E.-M. Jung ◽  
S. Glöckner ◽  
P. Hoffstetter ◽  
W. Uller ◽  
...  
Keyword(s):  
Real Time ◽  
Image Plane ◽  
Lesion Size ◽  
Lesion Detection ◽  
Porcine Liver ◽  
Thermal Lesion ◽  
Thermal Lesions ◽  
Histopathological Correlation ◽  
Thermal Therapies

Abstract Purpose: To evaluate the reliability of elastography, a new ultrasonographic method, for delineating thermal lesion boundaries in porcine liver tissue by comparing lesion dimensions determined by real-time elastography with the findings at gross pathology. Materials and Methods: A total of 15 thermal lesions with diameters ranging from 17 to 60 mm were created using radiofrequency ablation (RFA). Color-coded elastography was performed by one experienced examiner, using a 6 – 15 MHz high frequency linear transducer (LOGIQ E9, GE). Lesions were examined using B-mode and real-time elastography (RTE). Lesion detection, delineation and size were assessed using B-mode and RTE immediately after each thermal ablation ( < 5 min). Measurements of the sections representing the same image plane used for elastography were taken during pathologic examination and compared to the measurements obtained from the elastograms. Results: In our sample a statistically significant correlation in vitro between RTE and pathological measurements with respect to the lesion’s principal axis and area (r2 = 0.9338 long axis, r2 = 0.8998 short axis and r2 = 0.9676 area) was found. Overall, elastography slightly underestimated the lesion size, as judged by the digitalized pathologic images. Conclusion: These results support that RTE outperforms conventional B-mode ultrasound and could potentially be used for the routine assessment of thermal therapies.

scholarly journals Preoperative Staging in Breast Cancer: Intraindividual Comparison of Unenhanced MRI Combined With Digital Breast Tomosynthesis and Dynamic Contrast Enhanced-MRI

2021 ◽  
Vol 11 ◽  
Author(s):  
Veronica Rizzo ◽  
Giuliana Moffa ◽  
Endi Kripa ◽  
Claudia Caramanico ◽  
Federica Pediconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Preoperative Staging ◽  
Digital Breast Tomosynthesis ◽  
Lesion Size ◽  
Lesion Detection ◽  
P Value ◽  
Breast Tomosynthesis ◽  
Dce Mri ◽  
Mcnemar Test ◽  
Adc Values

ObjectivesTo evaluate the accuracy in lesion detection and size assessment of Unenhanced Magnetic Resonance Imaging combined with Digital Breast Tomosynthesis (UE-MRI+DBT) and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), in women with known breast cancer.MethodsA retrospective analysis was performed on 84 patients with histological diagnosis of breast cancer, who underwent MRI on a 3T scanner and DBT over 2018-2019, in our Institution. Two radiologists, with 15 and 7 years of experience in breast imaging respectively, reviewed DCE-MRI and UE-MRI (including DWI and T2-w) + DBT images in separate reading sections, unaware of the final histological examination. DCE-MRI and UE-MRI+DBT sensitivity, positive predictive value (PPV) and accuracy were calculated, using histology as the gold standard. Spearman correlation and regression analyses were performed to evaluate lesion size agreement between DCE-MRI vs Histology, UE-MRI+DBT vs Histology, and DCE-MRI vs UE-MRI+DBT. Inter-reader agreement was evaluated using Cohen’s κ coefficient. McNemar test was used to identify differences in terms of detection rate between the two methodological approaches. Spearman’s correlation analysis was also performed to evaluate the correlation between ADC values and histological features.Results109 lesions were confirmed on histological examination. DCE-MRI showed high sensitivity (100% Reader 1, 98% Reader 2), good PPV (89% Reader 1, 90% Reader 2) and accuracy (90% for both readers). UE-MRI+DBT showed 97% sensitivity, 91% PPV and 92% accuracy, for both readers. Lesion size Spearman coefficient were 0.94 (Reader 1) and 0.91 (Reader 2) for DCE-MRI vs Histology; 0.91 (Reader 1) and 0.90 (Reader 2) for UE-MRI+DBT vs Histology (p-value &lt;0.001). DCE-MRI vs UE-MRI+DBT regression coefficient was 0.96 for Reader 1 and 0.94 for Reader 2. Inter-reader agreement was 0.79 for DCE-MRI and 0.94 for UE-MRI+DBT. McNemar test did not show a statistically significant difference between DCE-MRI and UE-MRI+DBT (McNemar test p-value &gt;0.05). Spearman analyses showed an inverse correlation between ADC values and histological grade (p-value &lt;0.001).ConclusionsDCE-MRI was the most sensitive imaging technique in breast cancer preoperative staging. However, UE-MRI+DBT demonstrated good sensitivity and accuracy in lesion detection and tumor size assessment. Thus, UE-MRI could be a valid alternative when patients have already performed DBT.

scholarly journals A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines

2021 ◽  
Vol 15 ◽  
Author(s):  
Mona OmidYeganeh ◽  
Najmeh Khalili-Mahani ◽  
Patrick Bermudez ◽  
Alison Ross ◽  
Claude Lepage ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Imaging Study ◽  
Cumulative Effect ◽  
Simulation Method ◽  
Lesion Size ◽  
Volume Effect ◽  
Lesion Detection ◽  
Adult Individual ◽  
Important Concern ◽  
Random Groups

In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset) and in a within-subject pre-/post-lesion design [using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS)]. The simulation method is sensitive to partial volume effect and lesion size. Comparisons between pipelines illustrate the ability of this method to uncover differences in sensitivity and specificity of lesion detection. We propose that this method be adopted in the workflow of software development and release.

An Image Reconstruction System Applied to High Voltage Microscopy

1975 ◽  
Vol 33 ◽  
pp. 292-293
Author(s):  
D. E. Johnson
Keyword(s):  
High Voltage ◽  
Prior Information ◽  
Block Diagram ◽  
Three Dimensional ◽  
Real Space ◽  
Two Dimensional ◽  
Efficient Manner ◽  
Fourier Methods ◽  
Tilt Series ◽  
Methods Of Reconstruction

Increased specimen penetration; the principle advantage of high voltage microscopy, is accompanied by an increased need to utilize information on three dimensional specimen structure available in the form of two dimensional projections (i.e. micrographs). We are engaged in a program to develop methods which allow the maximum use of information contained in a through tilt series of micrographs to determine three dimensional speciman structure.In general, we are dealing with structures lacking in symmetry and with projections available from only a limited span of angles (±60°). For these reasons, we must make maximum use of any prior information available about the specimen. To do this in the most efficient manner, we have concentrated on iterative, real space methods rather than Fourier methods of reconstruction. The particular iterative algorithm we have developed is given in detail in ref. 3. A block diagram of the complete reconstruction system is shown in fig. 1.

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