Manual Regions Of Interest Placement Research Articles

Inter- and intra-rater reproducibility of quantitative T1 measurement using semiautomatic region of interest placement in myometrium.

This study aimed to investigate the inter- and intraobserver reproducibility of quantitative T1 (qT1) measurements using manual and semiautomatic region of interest (ROI) placements. We hypothesized the usefulness of the semiautomatic method, which utilizes a three-dimensional (3D) anatomical relationship between the myometrium and other tissues, for minimizing ROI placement variation, thereby improving qT1 reproducibility compared to the manual approach. The semiautomatic approach, which considered anatomical relationships, was expected to enhance reproducibility by reducing ROI placement variabilities. This study recruited 23 healthy female volunteers. Data with variable flip angle (VFA) and inversion recovery were acquired using 3D-spoiled gradient echo and spin echo sequences, respectively. T1 maps were generated with VFA. Manual and semiautomatic ROI placements were independently conducted. Mean qT1 values were calculated from the T1 maps using the corresponding pixel values of the myometrial ROI. Inter- and intraobserver reproducibility of qT1 values was investigated. The inter- and intraobserver reproducibility of qT1 values was evaluated by calculating the coefficient of variation (CoV). Further, reproducibility was evaluated with inter- and intraobserver errors and intraclass correlation coefficients (ICCs). Bland-Altman analysis was utilized to compare the results, estimate bias, and determine the limits of agreement. The mean inter- and intraobserver CoV of the qT1 values for semiautomatic ROI placement was significantly lower than those for manual ROI placement (p < 0.05 and p < 0.01, respectively). ICCs for semiautomatic ROI placement were greater than those for manual ROI placement. Further, the mean inter- and intraobserver errors for semiautomatic ROI placement were significantly lower than those for manual ROI placement (p < 0.05 and p < 0.01, respectively). Semiautomatic ROI placement demonstrated high reproducibility of qT1 measurements compared with manual methods. Semiautomatic ROI placement may be useful for evaluating uterine qT1 with high reproducibility.

Influence of the area of the aqueduct and region of interest on quantification of stroke volume in healthy volunteers using phase-contrast cine magnetic resonance imaging.

Phase-contrast cine magnetic resonance imaging (PC-MRI) has been used to measure cerebrospinal fluid (CSF) flow dynamics, but the influence of the area of the aqueduct and region of interest (ROI) on quantification of stroke volume (SV) has not been assessed. To assess the influence of the area of the ROI in quantifying the aqueductal SV measured with PC-MRI within the cerebral aqueduct. Nine healthy volunteers (mean age = 29.6 years) were enrolled in the study, and brain MRI examinations were performed on a 3.0-T system. Quantitative analysis of the aqueductal CSF flow was performed using manual ROI placement. ROIs were separately drawn for each of the 12 phases of the cardiac cycle, and changes in aqueduct size during the cardiac cycle were determined. The SV was calculated using 12 different aqueductal ROIs and compared with the SV calculated using a fixed ROI size. There was variation in the size of the aqueduct during the cardiac cycle. In addition, the measured SV increased with a greater area of the ROI. A significant difference in the calculated SVs with the 12 variable ROIs was observed compared with that using a fixed ROI throughout the cardiac cycle. To establish reliable reference values for the SV in future studies, a variable ROI should be considered.

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis

Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This study aims to develop an automated method of aqueduct CSF flow analysis using convolution neural networks (CNNs), which can replace the current standard involving manual segmentation of aqueduct region of interest (ROI). Retrospective analysis was performed on 333 patients who underwent PC-MRI, totaling 353 imaging studies. Aqueduct flow measurements using manual ROI placement was performed independently by two radiologists. Two types of CNNs, MultiResUNet and UNet, were trained using ROI data from the senior radiologist, with PC-MRI studies being randomly divided into training (80%) and validation (20%) datasets. Segmentation performance was assessed using Dice similarity coefficient (DSC), and CSF flow parameters were calculated from both manual and CNN-derived ROIs. MultiResUNet, UNet and second radiologist (Rater 2) had DSCs of 0.933, 0.928, and 0.867, respectively, with p < 0.001 between CNNs and Rater 2. Comparison of CSF flow parameters showed excellent intraclass correlation coefficients (ICCs) for MultiResUNet, with lowest correlation being 0.67. For UNet, lower ICCs of −0.01 to 0.56 were observed. Only 3/353 (0.8%) studies failed to have appropriate ROIs placed by MultiResUNet, compared to 12/353 (3.4%) failed cases for UNet. In conclusion, CNNs were able to measure aqueductal CSF flow with similar performance to a senior neuroradiologist. MultiResUNet demonstrated fewer cases of segmentation failure and more consistent flow measurements compared to the widely adopted UNet.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

BackgroundOur aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.MethodsThis ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions.ResultsSkewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii).ConclusionsTA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

Automatic ROI Placement in the Upper Trapezius Muscle in B-mode Ultrasound Images.

Research involving B-mode ultrasound imaging often requires user defined regions of interest (ROIs) for analysis, traditionally drawn/selected by a trained operator. This manual process is incredibly time consuming and subjective. Here, we propose a fast and simple method of detecting the average location of aponeurosis layers in ultrasound images of the upper trapezius to place a rectangular ROI for quantitative image analysis. A total of 56 B-mode ultrasound images were analyzed, where rectangular ROIs were manually placed in the skeletal muscle by two trained operators. Interoperator agreement was determined between the ROI border locations using intercorrelation coefficient (ICC). Next, our automatic algorithm was applied (image thresholding, binary masking, and pixel intensity peak detection), estimating the mean position of both aponeurosis layers for rectangular ROI placement. The automatic estimation method was compared with the manual (visual) method by various statistics ( t test, linear correlation, Bland-Altman plot). The performance was also evaluated under additive noise conditions (Speckle). Finally, agreement of the overlapping ROI area between the manual and automatic methods was also computed. Performance of the automatic method compared with manual placement was excellent for both the superficial and deep ROI borders, performing consistently even with additive noise (error <0.674 ± 1.69 mm). Manual measurements indicated excellent consensus (ICC = 0.902) between operators. The overlapping area between the manual and automatic measurements demonstrated good agreement (90.65 ± 11.3%). With constraints, our method is robust even under large levels of noise addition making the automatic algorithm an acceptable replacement for manual ROI placement in the upper trapezius.

Reproducibility of Dynamic Contrast-enhanced MR Imaging: Why We Should Care

HomeRadiologyVol. 266, No. 3 PreviousNext Reviews and CommentaryEditorialsReproducibility of Dynamic Contrast-enhanced MR Imaging: Why We Should CareVicky Goh , Tobias Schaeffter, Martin LeachVicky Goh , Tobias Schaeffter, Martin LeachAuthor AffiliationsFrom the Division of Imaging Sciences and Biomedical Engineering, King’s College London, Imaging 2, Level 1, Lambeth Wing, St Thomas’s Hospital, Lambeth Palace Rd, London SE1 7EH, England (V.G., T.S.); Department of Radiology, Guy’s and St Thomas’ NHS Foundation Trust, London, England (V.G.); and Cancer Research UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey, England (M.L.).Address correspondence to V.G. (e-mail: [email protected]).Vicky Goh Tobias SchaeffterMartin LeachPublished Online:Mar 1 2013https://doi.org/10.1148/radiol.12122447MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In AbstractThe results of two studies published in this issue of Radiology stress the difficulties of performing dynamic contrast-enhanced MR imaging across different commercially available software platforms in clinical practice, but more important suggest a potential solution.References1 Messiou C, Orton M, Ang JEet al.. Advanced solid tumors treated with cediranib: comparison of dynamic contrast-enhanced MR imaging and CT as markers of vascular activity. Radiology 2012;265(2):426–436. Link, Google Scholar2 Nathan P, Zweifel M, Padhani ARet al.. Phase I trial of combretastatin A4 phosphate (CA4P) in combination with bevacizumab in patients with advanced cancer. Clin Cancer Res 2012;18(12):3428–3439. Crossref, Medline, Google Scholar3 Leach MO, Brindle KM, Evelhoch JLet al.. The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 2005;92(9):1599–1610. Crossref, Medline, Google Scholar4 Leach MO, Morgan B, Tofts PSet al.. Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging. Eur Radiol 2012;22(7):1451–1464. Crossref, Medline, Google Scholar5 Quantitative Imaging Biomarkers Alliance. Profile: DCE MRI Quantification Version 1.6. Quantitative Imaging Biomarkers Alliance [serial online]. http://qibawiki.rsna.org/images/7/7b/DCEMRIProfile_v1_6-20111213.pdf. Published 2011. Accessed December 19, 2012. Google Scholar6 Heye T, Davenport MS, Horvath JJet al.. Reproducibility of dynamic contrast-enhanced MR imaging. I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology 2013;266(3):801–811. Link, Google Scholar7 Heye T, Merkle EM, Reiner CSet al.. Reproducibility of dynamic contrast-enhanced MR imaging. II. Comparison of intra- and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. Radiology 2013;266(3):812–821. Link, Google Scholar8 Goh V, Halligan S, Bartram CI. Quantitative tumor perfusion assessment with multidetector CT: are measurements from two commercial software packages interchangeable? Radiology 2007;242(3):777–782. Link, Google Scholar9 Goh V, Halligan S, Gharpuray A, Wellsted D, Sundin J, Bartram CI. Quantitative assessment of colorectal cancer tumor vascular parameters by using perfusion CT: influence of tumor region of interest. Radiology 2008;247(3):726–732. Link, Google Scholar10 Miles KA, Lee TY, Goh Vet al.. Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography. Eur Radiol 2012;22(7):1430–1441. Crossref, Medline, Google ScholarArticle HistoryReceived November 1, 2012; revision requested November 3; final revision received and accepted November 19.Published online: Mar 2013Published in print: Mar 2013 FiguresReferencesRelatedDetailsCited ByBritish Journal of Cancer, Vol. 125, No. 5Interreader Variability of Dynamic Contrast-enhanced MRI of Recurrent Glioblastoma: The Multicenter ACRIN 6677/RTOG 0625 StudyDaniel P. Barboriak, Zheng Zhang, Pratikkumar Desai, Bradley S. Snyder, Yair Safriel, Robert C. McKinstry, Felix Bokstein, Gregory Sorensen, Mark R. Gilbert, Jerrold L. Boxerman, 27 November 2018 | Radiology, Vol. 290, No. 2BMC Bioinformatics, Vol. 20, No. 1The British Journal of Radiology, Vol. 92, No. 1096European Radiology, Vol. 28, No. 9European Radiology, Vol. 27, No. 12La radiologia medica, Vol. 122, No. 4Lung Cancer, Vol. 93BMC Medical Imaging, Vol. 16, No. 1Journal of Magnetic Resonance Imaging, Vol. 42, No. 5Echocardiography, Vol. 32, No. 8Magnetic Resonance Imaging, Vol. 32, No. 3Recommended Articles Spontaneous Pregnancy with a Live Birth after Conventional and Partial Uterine Fibroid EmbolizationRadiology2017Volume: 285Issue: 1pp. 302-310The Blood–Cerebrospinal Fluid Barrier May Play a Role in Alzheimer Disease PathogenesisRadiology2022Volume: 304Issue: 3pp. 646-647Subfertility: What the Radiologist Needs to KnowRadioGraphics2017Volume: 37Issue: 5pp. 1587-1602Monitoring Neoadjuvant Chemotherapy for Breast Cancer by Using Three-dimensional Subharmonic Aided Pressure Estimation and Imaging with US Contrast Agents: Preliminary ExperienceRadiology2017Volume: 285Issue: 1pp. 53-624D Contrast-enhanced MR Angiography with the Keyhole Technique in Children: Technique and Clinical ApplicationsRadioGraphics2016Volume: 36Issue: 2pp. 523-537See More RSNA Education Exhibits Uterine Fibroid Embolization: Preparing for and Treating Post-Procedure PainDigital Posters2018Clinical Factors Related to Technical Success of Magnetic Resonance-Guided High-Intensity Focused Ultrasound Treatment of Uterine FibroidDigital Posters2020Oncologic Magnetic Resonance ElastographyDigital Posters2020 RSNA Case Collection Recurrent small bowel leiomyosarcomaRSNA Case Collection2021Esophageal Granular Cell Tumor RSNA Case Collection2022Gastric LeiomyomaRSNA Case Collection2021 Vol. 266, No. 3 Metrics Altmetric Score PDF download

Reproducibility of Dynamic Contrast-enhanced MR Imaging. Part II. Comparison of Intra- and Interobserver Variability with Manual Region of Interest Placement versus Semiautomatic Lesion Segmentation and Histogram Analysis

To compare the inter- and intraobserver variability with manual region of interest (ROI) placement versus that with software-assisted semiautomatic lesion segmentation and histogram analysis with respect to quantitative dynamic contrast material-enhanced (DCE) MR imaging determinations of the volume transfer constant (K(trans)). The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Fifteen DCE MR imaging studies of the female pelvis defined the study group. Uterine fibroids were used as a perfusion model. Three varying types of lesion measurements were performed by five readers on each study by using DCE MR imaging perfusion analysis software with manual ROI placement and a semiautomatic lesion segmentation and histogram analysis solution. Intra- and interreader variability of measurements of K(trans) with the different measurement types was calculated. The overall interobserver variability of K(trans) with manual ROI placement (mean, 28.5% ± 9.3) was reduced by 42.5% when the semiautomatic, software-assisted lesion measurement method was used (16.4% ± 6.2). Whole-lesion measurement showed the lowest interobserver variability with both measurement methods (20.1% ± 4.3 with the manual method vs 10.8% ± 2.6 with the semiautomatic method). The overall intrareader variability with the manual ROI method (7.6% ± 10.6) was not significantly different from that with the semiautomatic method (7.3% ± 10.8), but the intraclass correlation coefficient for intrareader reproducibility improved from 0.86 overall with the manual method to 0.99 with the semiautomatic method. A semiautomatic lesion segmentation and histogram analysis approach can provide a significant reduction in interobserver variability for DCE MR imaging measurements of K(trans) when compared with manual ROI methods, whereas intraobserver reproducibility is improved to some extent.

Kinetic characteristics of ductal carcinoma in situ (DCIS) in dynamic breast MRI using computer-assisted analysis

Enhancement characteristics of breast lesions are regarded as a major criterion for their differential diagnosis in dynamic breast MRI (bMRI). However, ductal carcinoma in situ (DCIS) exhibits a highly heterogeneous enhancement pattern when kinetic analysis is performed conventionally by manual placement of region of interest (ROI) and therefore its diagnosis remains challenging. To compare enhancement characteristics of DCIS lesions on dynamic bMRI using manual ROI placement with computer-aided analysis and to evaluate whether the latter might increase the detection rate of kinetic features suspicious for malignancy. The enhancement patterns of 47 histopathologically verified pure DCIS lesions were evaluated on bMRI images using manual ROI placement as well as a commercially available computer analysis software. The latter is able to automatically assess enhancement characteristics of a whole lesion pixelwise. Kinetic features evaluated included classification of lesion enhancement pattern into washout, plateau or persistent curve type. A washout and plateau enhancement pattern are regarded as suggestive for malignancy. Morphological classification revealed focus-like enhancement in 2 lesions, mass enhancement in 11, and non-mass enhancement in 34. Manual placement of ROI demonstrated a suspicious enhancement pattern in 51.1% of the DCIS lesions, which could not be significantly increased using computer-aided analysis. Of the mass and non-mass-enhancing DCIS lesions, 90.9% and 38.3%, respectively, demonstrated suspicious kinetic curves. After application of the automated analysis software, the detection rate of suspicious enhancement patterns was unchanged in mass DCIS lesions and increased to 52.9% in non-mass DCIS lesions (P=0.33). However, the increase in the detection of washout curves alone was significant (P=0.02). In all, 40% of G1, 41.1% of G2, and 60% of G3 lesions demonstrated a suspicious curve type with manual evaluation. Computer analysis increased the detection of suspicious enhancement patterns in a non-significant manner to 50%, 58.8%, and 70%, respectively. The detection of suspicious enhancement curves could not be significantly increased in DCIS lesions when using computer-aided analysis despite a significantly higher detection rate of washout curves alone.

Variability in Tissue Doppler Echocardiographic Measures of Dyssynchrony Is Reduced With Use of a Larger Region of Interest

Doppler tissue imaging (DTI)-based dyssynchrony parameters failed to predict response to cardiac resynchronization therapy (CRT) in the multicenter Predictors of Response to Cardiac Resynchronization Therapy (PROSPECT) trial. Large variability during the interpretation of DTI data was one of several factors thought to contribute to this failure. In this study, the authors hypothesized that using larger regions of interest (ROIs) to generate velocity curves from Doppler tissue images would significantly reduce the variability of DTI dyssynchrony parameters. The variability of 3 ROI sizes (6 x 6, 18 x 6, and 30 x 6 mm) was compared in 30 patients undergoing CRT. Variability due to manual ROI placement was determined for each ROI size by placing 3 ROIs in each myocardial segment, 6mm apart from one another. Thus, 3 velocity curves were generated for each segment and each ROI size. Four published dyssynchrony parameters were calculated from all permutations of the 3 ROI positions per segment. A mean modified coefficient of variation was calculated for each parameter and ROI size. The 6 x 6 mm ROI had a mean coefficient of variation of 27%. The 18 x 6 and 30 x 6 mm ROIs had significantly lower coefficients of variation (17% and 14%, respectively) than the 6 x 6 mm ROI (P < .01 for both). The 30 x 6 mm ROI also reduced the diagnostic inconsistency of dyssynchrony parameters by 44% (P = .024) compared with the 6 x 6 mm ROI. Using a 30 x 6 mm ROI instead of a 6 x 6 mm ROI to quantify tissue Doppler dyssynchrony reduces variability by 47% and diagnostic inconsistency by 44%. The authors recommend using a 30 x 6 mm ROI in future trials to minimize variability.

Inter‐ and intraobserver variability in the evaluation of dynamic breast cancer MRI

To quantify variations within and between observers ascribable to manual region of interest (ROI) placement in patients with breast cancer undergoing dynamic MRI. Expert and nonexpert observers independently outlined tumor ROIs on 30 dynamic T(1)-weighted (T(1)W) MRI scans on five occasions over two months. Lesion size (number of pixels) and kinetic parameter estimates, including the transfer constant (K(trans)), were calculated for each ROI placement. Inter- and intraobserver variability was assessed with respect to the interval between drawings, lesion morphology, and observer experience. For the nonexpert, the variability reduced with decreasing time intervals between ROI drawings (the coefficient of variance (wCV) values at two months, two weeks, one day, and same-day time intervals were respectively 11.6%, 10.7%, 4.8%, and 2.6% for lesion size, and 8.9%, 9.7%, 6.7%, and 3.2% for K(trans)). For the expert observer, the variability was smaller overall and more constant, but improved for same-day ROI placements (region size wCV: 7.5%, 6.2%, 7.1%, and 3.7%; K(trans) wCV: 5.4%, 5.3%, 5.6%, and 4.5%). Significant observer variability in manual ROI placement occurs in dynamic MRI of breast cancer. For serial patient studies, ROI placements should be outlined at the same sitting to minimize observer error.

Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced MR mammography

To evaluate the three-time-point (3TP) method for diagnosis of breast lesions detected on contrast-enhanced MR mammography. MR imaging was performed in 40 women with 120 suspected breast lesions in mammography and/or sonography. The contrast kinetics was converted by 3TP software on a pixel-by-pixel basis into color-coded images. Lesion diagnosis was made by analysis of color intensity and color hue. The 3TP results were compared with the results of the region-of-interest (ROI) method. In 16 patients, we were able to correlate the results with histopathological findings. The 3TP method could successfully be performed in all MR mammographies. Forty (33%) lesions had a diameter of less than 5 mm, 56 (47%) lesions between 5 and 10 mm, and 24 (20%) lesions were greater than 1 cm. Of all 120 lesions, 65 (54%) showed heterogeneous contrast enhancement. In 117 (97%) of all 120 lesions the results of ROI and the automated 3TP method were considered equivalent. However, in three lesions the manual ROI differed from the 3TP method. After a second, repeated manual ROI placement, we were able to confirm equivalent results with the 3TP images as well. The 3TP method automatically and reliably converts contrast kinetic information of the entire breast into a color-coded image. The 3TP method presents kinetic information of the entire dynamic series in an easy-to-interpret format and this automated method may allow to forego time-consuming and sometimes subjective manual ROI placements. This method displays the heterogeneity of the contrast enhancement pattern often observed in malignant lesions and makes it usable as diagnostic criterion.

MRI-guided region of interest placement on emission computed tomograms

The reliability of the placement of regions of interest (ROIs) on emission computed tomograms (ECT) with a semi-automated magnetic resonance image (MRI)-guided method was compared with the reliability of manual ROI placement. MRI-defined regions were transferred to the registered, co-planar ECT image after accounting for inter-image differences in rotation, translation, and scaling. Six single photon emission computed tomographic (SPECT) scans of normal control subjects were rated for caudate perfusion using the radiotracer N-isopropyl[I- 123]-iodoamphetamine (IMP). The MRI-guided method had higher interrater reliability, lower variance of region location and lower variance of region activity than the manual method. The manual method recorded higher mean regional activity than the MRI-guided method, possibly due to sample bias inherent in the manual method.

MRI-Guided region of interest placement on emission computed tomograms

The reliability of the placement of regions of interest (ROIs) on emission computed tomograms (ECT) with a semi-automated magnetic resonance image (MRI)-guided method was compared with the reliability of manual ROI placement. MRI-defined regions were transferred to the registered, co-planar ECT image after accounting for inter-image differences in rotation, translation, and scaling. Six single photon emission computed tomographic (SPECT) scans of normal control subjects were rated for caudate perfusion using the radiotracer N -isopropyl[I- 123 ]-iodoamphetamine (IMP). The MRI-guided method had higher interrater reliability, lower variance of region location and lower variance of region activity than the manual method. The manual method recorded higher mean regional activity than the MRI-guided method, possibly due to sample bias inherent in the manual method.