Lesion Detection Performance Research Articles

Evaluation of Prostate Cancer with Radiolabeled Amino Acid Analogs.

Conventional imaging of prostate cancer has limitations related to the frequently indolent biology of the disease. PET is a functional imaging method that can exploit various aspects of tumor biology to enable greater detection of prostate cancer than can be provided by morphologic imaging alone. Radiotracers that are in use or under investigation for targeting salient features of prostate cancer include those directed to glucose, choline, acetate, prostate-specific membrane antigen, bombesin, and amino acids. The tumor imaging features of this last class of radiotracers mirror the upregulation of transmembrane amino acid transport that is necessary in carcinomas because of increased amino acid use for energy requirements and protein synthesis. Natural and synthetic amino acids radiolabeled for PET imaging have been investigated in prostate cancer patients. Early work with naturally occurring amino acid-derived radiotracers, such as l-11C-methionine and l-1-11C-5-hydroxytryptophan, demonstrated promising results, including greater sensitivity than 18F-FDG for intraprostatic and extraprostatic cancer detection. However, limitations with naturally occurring amino acid-derived compounds, including metabolism of the radiotracer itself, led to the development of synthetic amino acid radiotracers, which are not metabolized and therefore more accurately reflect transmembrane amino acid transport. Of the synthetic amino acid-derived PET radiotracers, anti-1-amino-3-18F-fluorocyclobutane-1-carboxylic acid (18F-FACBC or 18F-fluciclovine) has undergone the most promising translation to human use, including the availability of simplified radiosynthesis. Several studies have indicated advantageous biodistribution in the abdomen and pelvis with little renal excretion and bladder activity-characteristics beneficial for prostate cancer imaging. Studies have demonstrated improved lesion detection and diagnostic performance of 18F-fluciclovine in comparison with conventional imaging, especially for recurrent prostate cancer, although issues with nonspecific uptake limit the potential role of 18F-fluciclovine in the diagnosis of primary prostate cancer. Although work is ongoing, recently published intrapatient comparisons of 18F-fluciclovine with 11C-choline reported higher overall diagnostic performance of the former, especially for the detection of disease relapse. This review is aimed at providing a detailed overview of amino acid-derived PET compounds that have been studied for use in prostate cancer imaging.

Evaluation of breast software model for X-ray 2D and 3D mammography imaging

Introduction Physical test phantoms are a valuable tool in the assessment of novel breast imaging techniques. In order to optimize issues such as image processing or reconstruction, lesion detection performance, image quality and radiation exposure, dose evaluation, it is important that they reproduce the characteristics of 2D and 3D breast images as precisely as possible. Purpose Recently, a physical phantom with a structured background has been introduced for both 2D mammography and breast tomosynthesis. The purpose of this work is to create the 3D software version of the physical phantom and use it for further phantom optimization and extrapolation to CT imaging. Materials and methods The software breast phantom simulates an acrylic semi-cylinder container of height 48 mm and diameter 200 mm filled with water and acrylic beads of different diameters. Planar projections in mammography and tomosynthesis were simulated for the same conditions as with the Siemens Inspiration system. Tomosynthesis slices were reconstructed with in-house developed reconstruction software. Results Visually, an excellent agreement between simulated and real images is observed. Parameters like fractal dimension, power law exponent β and second order statistics (skewness, kurtosis) of projection and tomosynthesis reconstructed images are under evaluation and will be presented at the conference. Conclusion The software breast phantom showed a close match with its physical version. If the mathematical analysis of the images confirms the agreement, the software platform will be used to predict performance for CT imaging and to study the effect of other background structures than beads.

Lesion Activity Assessment (LAA) in Conjunction With International Caries Detection and Assessment System (ICDAS) for Occlusal Caries Diagnosis in Permanent Teeth.

The aim of this study was to investigate the clinical performance and to validate the Lesion Activity Assessment (LAA) in conjunction with the International Caries Detection and Assessment System (ICDAS) for occlusal caries diagnosis in permanent teeth. Patients with erupted or partially erupted third molars were recruited from the surgery clinic of the School of Dentistry of the Universidade Federal de Minas Gerais, Brazil. A calibrated examiner evaluated 49 teeth using the ICDAS-LAA criteria. The histologic criterion proposed by Ekstrand and others was used to validate severity at the thresholds D1 (outer half of the enamel), D2 (inner half of the enamel and outer third of the dentin), and D3 (inner or middle third of the dentin). Lesion activity was validated using 0.1% methyl red solution. The method demonstrated good reliability (weighted kappa for severity=0.60; unweighted kappa for activity=0.61). The ICDAS presented a higher performance for lesion detection (area under the receiver operating characteristic curve [Az]=0.79) using the threshold D3. At the thresholds D1 and D2, the results for Az were 0.57 and 0.74, respectively. Regarding the ICDAS-LAA, Az = 0.59. Clinical protocols can use ICDAS for the severity diagnosis of occlusal caries, but the LAA performance was poor.

Segmented slant hole collimator for stationary cardiac SPECT: Monte Carlo simulations

This work is a preliminary study of a stationary cardiac SPECT system. The goal of this research is to propose a stationary cardiac SPECT system using segmented slant-hole collimators and to perform computer simulations to test the feasibility. Compared to the rotational SPECT, a stationary system has a benefit of acquiring temporally consistent projections. The most challenging issue in building a stationary system is to provide sufficient projection view-angles. A GATE (GEANT4 application for tomographic emission) Monte Carlo model was developed to simulate a two-detector stationary cardiac SPECT that uses segmented slant-hole collimators. Each detector contains seven segmented slant-hole sections that slant to a common volume at the rotation center. Consequently, 14 view-angles over 180° were acquired without any gantry rotation. The NCAT phantom was used for data generation and a tailored maximum-likelihood expectation-maximization algorithm was used for image reconstruction. Effects of limited number of view-angles and data truncation were carefully evaluated in the paper. Simulation results indicated that the proposed segmented slant-hole stationary cardiac SPECT system is able to acquire sufficient data for cardiac imaging without a loss of image quality, even when the uptakes in the liver and kidneys are high. Seven views are acquired simultaneously at each detector, leading to 5-fold sensitivity gain over the conventional dual-head system at the same total acquisition time, which in turn increases the signal-to-noise ratio by 19%. The segmented slant-hole SPECT system also showed a good performance in lesion detection. In our prototype system, a short hole-length was used to reduce the dead zone between neighboring collimator segments. The measured sensitivity gain is about 17-fold over the conventional dual-head system. The gate Monte Carlo simulations confirm the feasibility of the proposed stationary cardiac SPECT system with segmented slant-hole collimators. The proposed collimator consists of combined parallel and slant holes, and the image on the detector is not reduced in size.

Assessment of metastatic colorectal cancer with hybrid imaging: comparison of reading performance using different combinations of anatomical and functional imaging techniques in PET/MRI and PET/CT in a short case series.

The purpose was to investigate the diagnostic performance of different combinations of anatomical and functional imaging techniques in PET/MRI and PET/CT for the evaluation of metastatic colorectal cancer lesions. Image data of 15 colorectal cancer patients (FDG-PET/CT and subsequent FDG-PET/MRI) were retrospectively evaluated by two readers in five reading sessions: MRI (morphology) alone, MRI/diffusion-weighted MRI (DWI), MRI/PET, MRI/DWI/PET; and PET/CT. Diagnostic performance of lesion detection with each combination was assessed in general and organ-based. The reference standard was given by histology and/or follow-up imaging. Separate analysis of mucinous tumours was performed. One hundred and eighty lesions (110 malignant) were evaluated (intestine n = 6, liver n = 37, lymph nodes n = 55, lung n = 4, and peritoneal n = 74). The overall lesion-based diagnostic accuracy was 0.46 for MRI, 0.47 for MRI/DWI, 0.57 for MRI/PET, 0.69 for MRI/DWI/PET and 0.66 for PET/CT. In the organ-based assessment, MRI/DWI/PET showed the highest accuracy for liver metastases (0.74), a comparable accuracy to PET/CT in peritoneal lesions (0.55), and in lymph node metastases (0.84). The accuracy in mucinous tumour lesions was limited in all modalities (MRI/DWI/PET = 0.52). PET/MRI including DWI is comparable to PET/CT in the evaluation of colorectal cancer metastases, with a markedly higher accuracy when using combined imaging data than the modalities separately. Further improvement is needed in the imaging of peritoneal carcinomatosis and mucinous tumours.

Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast

The objective of this study was to compare the lesion detection performance of human observers between thin-section computed tomography images of the breast, with thick-section (>40 mm) simulated projection images of the breast.Three radiologists and six physicists each executed a two alterative force choice (2AFC) study involving simulated spherical lesions placed mathematically into breast images produced on a prototype dedicated breast CT scanner. The breast image data sets from 88 patients were used to create 352 pairs of image data. Spherical lesions with diameters of 1, 2, 3, 5, and 11 mm were simulated and adaptively positioned into 3D breast CT image data sets; the native thin section (0.33 mm) images were averaged to produce images with different slice thicknesses; average section thicknesses of 0.33, 0.71, 1.5 and 2.9 mm were representative of breast CT; the average 43 mm slice thickness served to simulate simulated projection images of the breast.The percent correct of the human observer’s responses were evaluated in the 2AFC experiments. Radiologists lesion detection performance was significantly (p < 0.05) better in the case of thin-section images, compared to thick section images similar to mammography, for all but the 1 mm lesion diameter lesions. For example, the average of three radiologist’s performance for 3 mm diameter lesions was 92% correct for thin section breast CT images while it was 67% for the simulated projection images. A gradual reduction in observer performance was observed as the section thickness increased beyond about 1 mm. While a performance difference based on breast density was seen in both breast CT and the projection image results, the average radiologist performance using breast CT images in dense breasts outperformed the performance using simulated projection images in fatty breasts for all lesion diameters except 11 mm. The average radiologist performance outperformed that of the average physicist observer, however trends in performance were similar.Human observers demonstrate significantly better mass-lesion detection performance on thin-section CT images of the breast, compared to thick-section simulated projection images of the breast.

Influence of professional experience on detection and treatment decision of occlusal caries lesions in primary teeth.

To evaluate the influence of examiner's clinical experience on detection and treatment decision of caries lesions in primary molars. Three experienced dentists (Group A) and three undergraduate students (Group B) used the International Caries Detection and Assessment System (ICDAS) criteria and bitewing radiographs (BW) to perform examinations twice in 77 primary molars that presented a sound or carious occlusal surface. For the treatment decision (TD), the examiners attributed scores, analyzing the teeth in conjunction with the radiographs. The presence and the depth of lesion were validated histologically, and reproducibility was evaluated. The sensitivity, specificity, accuracy, and area under the ROC curve values were calculated for ICDAS and BW. The associations between ICDAS, BW, and TD were analyzed by means of contingency tables. Interexaminer agreement for ICDAS, BW, and TD were excellent for Group B and moderate for Group A. The two groups presented similar and satisfactory performance for caries lesion detection using ICDAS and BW. In the treatment decision, Group A was shown to have a less invasive approach than Group B. The examiner's experience was not determinant for the clinical and radiographic detection of occlusal lesions in primary teeth but influenced the treatment decision of initial lesions.

Color Image Decomposition and Fuzzy Clustering for Dermoscopic Image Segmentation

This paper proposes a new approach for automatic and effective segmentation of dermoscopic images. The method of segmentation is based on a pre-processing using the color structure-texture image decomposition based on Partial Differential Equations, and a step of segmentation by fuzzy clustering based algorithm. The proposed approach has been implemented and compared with other recent algorithms for dermoscopic image segmentation in order to demonstrate sufficiently good results, the proposed technique was be evaluated as performance for lesion detection in dermoscopic images, and the results are 78% of specificity, 99% of sensitivity and 0.99 of the AUC metric.

4D numerical observer for lesion detection in respiratory-gated PET.

Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic (18)F-fluorodeoxyglucose activity distributions were simulated using a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using geant4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6-31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was compared to the conventional 3D approach applied on the nongated and motion-corrected images. On average, the proposed 4D numerical observer improved the detection SNR by 48.6% (p < 0.005), whereas the 3D methods on motion-corrected images improved by 31.0% (p < 0.005) as compared to the nongated method. For all different conditions of the lesions, the relative SNR measurement (Gain = SNRObserved/SNRNongated) of the 4D method was significantly higher than one from the motion-corrected 3D method by 13.8% (p < 0.02), where Gain4D was 1.49 ± 0.21 and Gain3D was 1.31 ± 0.15. For the lesion with the highest amplitude of motion, the 4D numerical observer yielded the highest observer-performance improvement (176%). For the lesion undergoing the smallest motion amplitude, the 4D method provided superior lesion detectability compared with the 3D method, which provided a detection SNR close to the nongated method. The investigation on a structure of the 4D numerical observer showed that a Laguerre-Gaussian channel matrix with a volumetric 3D function yielded higher lesion-detection performance than one with a 2D-stack-channelized function, whereas a different kind of channels that have the ability to mimic the human visual system, i.e., difference-of-Gaussian, showed similar performance in detecting uniform and spherical lesions. The investigation of the detection performance when increasing noise levels yielded decreasing detection SNR by 27.6% and 41.5% for the nongated and gated methods, respectively. The investigation of lesion contrast and diameter showed that the proposed 4D observer preserved the linearity property of an optimal-linear observer while the motion was present. Furthermore, the investigation of the iteration and subset numbers of the OSEM algorithm demonstrated that these parameters had impact on the lesion detectability and the selection of the optimal parameters could provide the maximum lesion-detection performance. The proposed 4D numerical observer outperformed the other observers for the lesion-detection task in various lesion conditions and motions. The 4D numerical observer shows substantial improvement in lesion detectability over the 3D observer method. The proposed 4D approach could potentially provide a more reliable objective assessment of the impact of respiratory-gated PET improvement for lesion-detection tasks. On the other hand, the 4D approach may be used as an upper bound to investigate the performance of the motion correction method. In future work, the authors will validate the proposed 4D approach on clinical data for detection tasks in pulmonary oncology.

The influence of experience and training in a group of novice observers: A jackknife alternative free-response receiver operating characteristic analysis

PurposeThe study evaluates the pre- and post-training lesion localisation ability of a group of novice observers. Parallels are drawn with the performance of inexperienced radiographers taking part in preliminary clinical evaluation (PCE) and ‘red-dot’ systems, operating within radiography practice. Materials and methodsThirty-four novice observers searched 92 images for simulated lesions. Pre-training and post-training evaluations were completed following the free-response the receiver operating characteristic (FROC) method. Training consisted of observer performance methodology, the characteristics of the simulated lesions and information on lesion frequency. Jackknife alternative FROC (JAFROC) and highest rating inferred ROC analyses were performed to evaluate performance difference on lesion-based and case-based decisions. The significance level of the test was set at 0.05 to control the probability of Type I error. ResultsJAFROC analysis (F(3,33) = 26.34, p < 0.0001) and highest-rating inferred ROC analysis (F(3,33) = 10.65, p = 0.0026) revealed a statistically significant difference in lesion detection performance. The JAFROC figure-of-merit was 0.563 (95% CI 0.512,0.614) pre-training and 0.677 (95% CI 0.639,0.715) post-training. Highest rating inferred ROC figure-of-merit was 0.728 (95% CI 0.701,0.755) pre-training and 0.772 (95% CI 0.750,0.793) post-training. ConclusionsThis study has demonstrated that novice observer performance can improve significantly. This study design may have relevance in the assessment of inexperienced radiographers taking part in PCE or commenting scheme for trauma.

MR arthrography of the shoulder: Optimizing pulse sequence protocols for the evaluation of cartilage and labrum

ObjectivesTo compare axial T1weighted fat-saturated (T1w fs) and T1w non-fs sequences, and coronal T1w-fs and T2w-fs sequences, for evaluation of cartilage and labrum using CT arthrography (CTA) as the reference. MethodsPatients had MR arthrography (MRA) and CTA of the shoulder on the same day. Cartilage was assessed for superficial and full thickness focal and diffuse damage. Labral lesions were graded for Bankart variants and SLAP lesions. CTA images were read for the same features. The diagnostic performance of MRA including area under the curve (AUC) was evaluated against CTA. ResultsWhen comparing axial sequences, the diagnostic performance for cartilage lesion detection on T1w non-fs was 61.9% (sensitivity) 93.6% (specificity) and 89.5% (accuracy) with AUC 0.782, while that for T1w fs was 61.9%, 94.0%, 89.8% and 0.783. For labral assessment, it was 89.1%, 93.0%, 91.4% and 0.919 for T1w non-fs, and 89.9%, 94.0%, 92.6% and 0.922 for T1w fs. Comparing coronal sequences, diagnostic performance for cartilage was 42.5%, 97.5%, 89.8% and 0.702 for T1w fs, and 38.4%, 98.7%, 90.2%, and 0.686 for T2w fs. For the labrum it was 85.1%, 87.5%, 86.2%, and 0.868 for T1w fs, and 75.7%, 97.5%, 80.8% and 0.816 for T2w fs. ConclusionsAxial T1w fs and T1w non-fs sequences are comparable in their ability to diagnose cartilage and labral lesions. Coronal T1w fs sequence offers slightly higher sensitivity but slightly lower specificity than T2w fs sequence for diagnosis of cartilage and labral lesions.

The Effects of Applying Breast Compression in Dynamic Contrast Material–enhanced MR Imaging

To evaluate the effects of breast compression on breast cancer masses, contrast material enhancement of glandular tissue, and quality of magnetic resonance (MR) images in the identification and characterization of breast lesions. This was a HIPAA-compliant, institutional review board-approved retrospective study, with waiver of informed consent. Images from 300 MR imaging examinations in 149 women (mean age ± standard deviation, 51.5 years ± 10.9; age range, 22-76 years) were evaluated. The women underwent diagnostic MR imaging (no compression) and MR-guided biopsy (with compression) between June 2008 and February 2013. Breast compression was expressed as a percentage relative to the noncompressed breast. Percentage enhancement difference was calculated between noncompressed- and compressed-breast images obtained in early and delayed contrast-enhanced phases. Breast density, lesion type (mass vs non-masslike enhancement [NMLE]), lesion size, percentage compression, and kinetic curve type were evaluated. Linear regression, receiver operating characteristic (ROC) curve analysis, and κ test were performed. Mean percentage compression was 31.3% ± 9.2 (range, 5.8%-53.2%). Percentage enhancement was higher in noncompressed- versus compressed-breast studies in early (146% ± 66 vs 107% ± 42, respectively; P < .001) and delayed (158% ± 68 vs 107% ± 42, respectively; P = .1) phases. Among breast lesions, 12% (seven of 59) were significantly smaller when compressed, which led to underestimation of TNM classification (P < .001). Breast masses (n = 35) showed significantly higher early percentage enhancement (157% ± 71) than lesions with NMLE (n = 15, 120% ± 40; P = .02) and a percentage enhancement difference (47.5% ± 64 vs 17% ± 28, respectively; P = .023). Kinetic curve performance for identifying invasive cancer decreased after compression (area under ROC curve = 0.53 vs 0.71, respectively; P = .02). Breast compression resulted in complete loss of enhancement of nine of 210 lesions (4%). Breast compression during biopsy affected breast lesion detection, lesion size, and dynamic contrast-enhanced MR imaging interpretation and performance. Limiting the application of breast compression is recommended, except when clinically necessary.

A Free-response Evaluation Determining Value in the Computed Tomography Attenuation Correction Image for Revealing Pulmonary Incidental Findings: A Phantom Study

The purpose of this study was to compare lesion-detection performance when interpreting computed tomography (CT) images that are acquired for attenuation correction when performing single photon emission computed tomography/computed tomography (SPECT/CT) myocardial perfusion studies. In the United Kingdom, there is a requirement that these images be interpreted; thus, it is necessary to understand observer performance on these images.An anthropomorphic chest phantom with inserted spherical lesions of different sizes and contrasts was scanned on five different SPECT/CT systems using site-specific CT protocols for SPECT/CT myocardial perfusion imaging. Twenty-one observers (0-4 years of CT experience) searched 26 image slices (17 abnormal, containing 1-3 lesions, and 9 normal, containing no lesions) for each CT acquisition. The observers marked and rated perceived lesions under the free-response paradigm. Four analyses were conducted using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis: (1) 20-pixel acceptance radius (AR) with all 21 readers, abbreviated to 20/ALL analysis, (2) 40-pixel AR with 21 readers (40/ALL), (3) 20-pixel AR with 14 readers experienced in CT (20/EXP), and (4) 20-pixel AR with 7 readers with no CT experience (20/NOT). The significance level of the test was set so as to conservatively control the overall probability of a type I error to <0.05.The mean JAFROC figure of merit (FOM) for the five CT acquisitions for the 20/ALL study were 0.602, 0.639, 0.372, 0.475, and 0.719 with a significant difference in lesion-detection performance evident between all individual treatment pairs (P < .0001) with the exception of the 1-2 pairing, which was not significant (these differed only in milliamp seconds). System 5, which had the highest performance, had the smallest slice thickness and the largest matrix size. For the other analyses, the system orderings remained unchanged, and the significance of FOM difference findings remained identical to those for 20/ALL, with one exception: for 20/EXP analysis the 1-2 difference became significant with the higher milliamp seconds superior. Improved detection performance was associated with a smaller slice thickness, increased matrix size, and, to a lesser extent, increased tube charge.Protocol variations for CT-based attenuation correction (AC) in SPECT/CT imaging have a measurable impact on lesion-detection performance. The results imply that z-axis resolution and matrix size had the greatest impact on lesion detection, with a weaker but detectable dependence on the product of milliamp and seconds.

Postreconstruction Nonlocal Means Filtering of Whole-Body PET With an Anatomical Prior

Positron emission tomography (PET) images usually suffer from poor signal-to-noise ratio (SNR) due to the high level of noise and low spatial resolution, which adversely affect its performance for lesion detection and quantification. The complementary information present in high-resolution anatomical images from multi-modality imaging systems could potentially be used to improve the ability to detect and/or quantify lesions. However, previous methods that use anatomical priors usually require matched organ/lesion boundaries. In this study, we investigated the use of anatomical information to suppress noise in PET images while preserving both quantitative accuracy and the amplitude of prominent signals that do not have corresponding boundaries on computerized tomography (CT). The proposed approach was realized through a postreconstruction filter based on the nonlocal means (NLM) filter, which reduces noise by computing the weighted average of voxels based on the similarity measurement between patches of voxels within the image. Anatomical knowledge obtained from CT was incorporated to constrain the similarity measurement within a subset of voxels. In contrast to other methods that use anatomical priors, the actual number of neighboring voxels and weights used for smoothing were determined from a robust measurement on PET images within the subset. Thus, the proposed approach can be robust to signal mismatches between PET and CT. A 3-D search scheme was also investigated for the volumetric PET/CT data. The proposed anatomically guided median nonlocal means filter (AMNLM) was first evaluated using a computer phantom and a physical phantom to simulate realistic but challenging situations where small lesions are located in homogeneous regions, which can be detected on PET but not on CT. The proposed method was further assessed with a clinical study of a patient with lung lesions. The performance of the proposed method was compared to Gaussian, edge-preserving bilateral and NLM filters, as well as median nonlocal means (MNLM) filtering without an anatomical prior. The proposed AMNLM method yielded improved lesion contrast and SNR compared with other methods even with imperfect anatomical knowledge, such as missing lesion boundaries and mismatched organ boundaries.

Diagnostic value of elastography using acoustic radiation force impulse imaging and strain ratio for breast tumors.

PurposeThe aim of this study was to determine whether the combination of B-mode ultrasonography (BUS), acoustic radiation force impulse (ARFI) elastography, and strain ratio (SR) provides better diagnostic performance of breast lesion differentiation than BUS alone.MethodsARFI elastography and SR evaluations were performed on patients with 157 breast lesions diagnosed by BUS from June to September 2013. BUS images were classified according to the Breast Imaging-Reporting and Data System. ARFI elastography was performed using Virtual Touch™ tissue imaging (VTI) and Virtual Touch™ tissue quantification (VTQ). In VTI mode, we evaluated the color-mapped patterns of the breast lesion and surrounding tissue. The lesions were classified into five categories by elasticity score. In VTQ mode, each lesion was assessed using shear wave velocity (SWV) measurements. SR was calculated from the lesion and comparable lateral fatty tissue. We compared the diagnostic performance of BUS alone and the combination of BUS, ARFI elastography, and SR evaluations.ResultsAmong the 157 lesions, 40 were malignant and 117 were benign. The mean elasticity score (3.7±1.0 vs. 1.6±0.8, p<0.01), SWV (4.23±1.09 m/sec vs. 2.22±0.88 m/sec, p<0.01), and SR (5.69±1.63 vs. 2.69±1.40, p<0.01) were significantly higher for malignant lesions than benign lesions. The results for BUS combined with ARFI elastography and SR values were 97.5% sensitivity, 92.3% specificity, 93.6% accuracy, a 79.6% positive predictive value (PPV), and a 99.1% negative predictive value. The combination of the 3 radiologic examinations yielded superior specificity, accuracy, and PPV compared to BUS alone (p<0.01 for each).ConclusionARFI elastography and SR evaluations showed significantly different mean values for benign and malignant lesions. Moreover, these two modalities complemented BUS and improved the diagnostic performance of breast lesion detection. Therefore, ARFI elastography and SR evaluations can be used as complementary modalities to make more accurate breast lesion diagnoses.

Prostate Histoscanning in Clinically Localized Biopsy Proven Prostate Cancer: An Accuracy Study

To assess the accuracy of prostate histoscanning (PHS) for spatial detection and localization of prostate cancer (PCa). Prospective, single center study from January to September 2012 was conducted. Inclusion criterion was biopsy confirmed PCa in patients scheduled for radical prostatectomy. In total, 98 patients were included in the study. Results of PHS were compared against whole-mount step sectioning by the Stanford technique. A lower limit of 0.1 cm(3) was used for PHS. A dedicated 12-sector form was used for spatial correlation. The urologist and pathologist were blinded for each other's results. Sensitivity, specificity, and receiver operating characteristic curves were calculated with a logistic regression model for covariates. PHS performance for detection of PCa lesions ≥0.1 cm(3) had sensitivity of 60%, specificity of 66%, and area under the curve (AUC) of 0.63. Posterior and anterior sectors achieved sensitivity of 77%, specificity of 39%, and 28% and 84%, respectively. The model containing PHS positivity within a given sector reached sensitivity of 73.4%, specificity of 65.7%, and AUC of 0.75. In a ogistic regression model, the performance of PHS was affected by sector location, rectal distance, index, and total cancer volume (all P<0.0001) and bladder fullness (P=0.02). The best PHS accuracy was present in midposterior sectors. PHS has a potential for clinical practice, especially if PHS positivity within given sectors is taken into account. A trained operator is important. More studies are necessary to test different detection limits in various clinical settings, such as targeted biopsies and image guided focal therapy.

Effect of Varying Number of OSEM Subsets on PET Lesion Detectability

Iterative reconstruction has become the standard for routine clinical PET imaging. However, iterative reconstruction is computationally expensive, especially for time-of-flight (TOF) data. Block-iterative algorithms such as ordered-subsets expectation maximization (OSEM) are commonly used to accelerate the reconstruction. There is a tradeoff between the number of subsets and reconstructed image quality. The objective of this work was to evaluate the effect of varying the number of OSEM subsets on lesion detection for general oncologic PET imaging. Experimental phantom data were taken from the Utah PET Lesion Detection Database, modeling whole-body oncologic (18)F-FDG PET imaging of a 92-kg patient. The experiment consisted of 24 scans over 4 d on a TOF PET/CT scanner, with up to 23 lesions (diameter, 6-16 mm) distributed throughout the thorax, abdomen, and pelvis. Images were reconstructed with maximum-likelihood expectation maximization (MLEM) and with OSEM using 2-84 subsets. The reconstructions were repeated both with and without TOF. Localization receiver-operating-characteristic (LROC) analysis was applied using the channelized nonprewhitened observer. The observer was first used to optimize the number of iterations and smoothing filter for each case that maximized lesion-detection performance for these data; this was done to ensure that fair comparisons were made with each test case operating near its optimal performance. The probability of correct localization and the area under the LROC curve were then analyzed as functions of the number of subsets to characterize the effect of OSEM on lesion-detection performance. Compared with the baseline MLEM algorithm, lesion-detection performance with OSEM declined as the number of subsets increased. The decline was moderate out to about 12-14 subsets and then became progressively steeper as the number of subsets increased. Comparing TOF with non-TOF results, the magnitude of the performance drop was larger for TOF reconstructions. PET lesion-detection performance is degraded when OSEM is used with a large number of subsets. This loss of image quality can be controlled using a moderate number of subsets (e.g., 12-14 or fewer), retaining a large degree of acceleration while maintaining high image quality. The use of more aggressive subsetting can result in image quality degradations that offset the benefits of using TOF or longer scan times.

Diagnostic performance and inter-observer concordance in lesion detection with the automated breast volume scanner (ABVS)

BackgroundAutomated whole breast ultrasound scanners of the latest generation have reached a level of comfortable application and high quality volume acquisition. Nevertheless, there is a lack of data concerning this technology. We investigated the diagnostic performance and inter-observer concordance of the Automated Breast Volume Scanner (ABVS) ACUSON S2000™ and questioned its implications in breast cancer diagnostics.MethodsWe collected 100 volume data sets and created a database containing 52 scans with no detectable lesions in conventional ultrasound (BI-RADS®-US 1), 30 scans with benign lesions (BI-RADS®-US 2) and 18 scans with breast cancer (BI-RADS®-US 5).Two independent examiners evaluated the ABVS data on a separate workstation without any prior knowledge of the patients’ histories.ResultsThe inter-rater reliability reached fair agreement (κ=0.36; 95% confidence interval (CI): 0.19-0.53). With respect to the true category, the conditional inter-rater validity coefficient was κ=0.18 (95% CI: 0.00-0.26) for the benign cases and κ=0.80 (95% CI: 0.61-1.00) for the malignant cases.Combining the assessments of examiner 1 and examiner 2, the diagnostic accuracy (AC), sensitivity (SE) and specificity (SP) for the experimental ABVS were AC = 79.0% (95% CI: 67.3-86.1), SE = 83.3% (95% CI: 57.7-95.6) and SP = 78.1% (% CI: 67.3-86.1), respectively.However, after the ABVS examination, there were a high number of requests for second-look ultrasounds in up to 48.8% of the healthy women due to assumed suspicious findings in the volume data.In an exploratory analysis, we estimated that an ABVS examination in addition to mammography alone could detect a relevant number of previously occult breast cancers (about 1 cancer in 300 screened and otherwise healthy women).ConclusionsThe ABVS is a reliable imaging method for the evaluation of the breast with high sensitivity and a fair inter-observer concordance. However, we have to overcome the problem of the high number of false-positive results. Therefore, further prospective studies in larger collectives are necessary to define standard procedures in image acquisition and interpretation. Nevertheless, we consider the ABVS as being suitable for integration into breast diagnostics as a beneficial and reliable imaging method.

Study of PET scanner designs using clinical metrics to optimize the scanner axial FOV and crystal thickness

The aim of this study is to understand the trade-off between crystal thickness and scanner axial field-of-view FOV (AFOV) for clinical PET imaging. Clinical scanner design has evolved towards 20–25 mm thick crystals and 16–22 cm long scanner AFOV, as well as time-of-flight (TOF) imaging. While Monte Carlo studies demonstrate that longer AFOV and thicker crystals will lead to higher scanner sensitivity, cost has prohibited the building of commercial scanners with >22 cm AFOV. In this study, we performed a series of system simulations to optimize the use of a given amount of crystal material by evaluating the impact on system sensitivity and noise equivalent counts (NEC), as well as image quality in terms of lesion detectability. We evaluated two crystal types (LSO and LaBr3) and fixed the total crystal volume used for each type (8.2 L of LSO and 17.1 L of LaBr3) while varying the crystal thickness and scanner AFOV. In addition, all imaging times were normalized so that the total scan time needed to scan a 100 cm long object with multiple bed positions was kept constant. Our results show that the highest NEC cm–1 in a 35 cm diameter ×70 cm long line source cylinder is achieved for an LSO scanner with 10 mm long crystals and AFOV of 36 cm, while for LaBr3 scanners, the highest NEC cm–1 is obtained with 20 mm long crystals and an AFOV of 38 cm. Lesion phantom simulations show that the best lesion detection performance is achieved in scanners with long AFOV (≥36 cm) and using thin crystals (≤10 mm of LSO and ≤20 mm of LaBr3). This is due to a combination of improved NEC, as well as improved lesion contrast estimation due to better spatial resolution in thinner crystals. Alternatively, for lesion detection performance similar to that achieved in standard clinical scanner designs, the long AFOV scanners can be used to reduce the total scan time without increasing the amount of crystal used in the scanner. In addition, for LaBr3 based scanners, the reduced lesion contrast relative to LSO based scanners requires improved timing resolution and longer scan times in order to achieve lesion detectability similar to that achieved in an LSO scanner with similar NEC cm–1.

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT

To compare the dose-optimisation potential of a smoothing filtered backprojection (FBP) and a hybrid FBP/iterative algorithm to that of a standard FBP algorithm at three slice thicknesses for hepatic lesion detection with multidetector CT. A liver phantom containing a 9.5-mm opacity with a density of 10 HU below background was scanned at 125, 100, 75, 50 and 25 mAs. Data were reconstructed with standard FBP (B), smoothing FBP (A) and hybrid FBP/iterative (iDose(4)) algorithms at 5-, 3- and 1-mm collimation. 10 observers marked opacities using a four-point confidence scale. Jackknife alternative free-response receiver operating characteristic figure of merit (FOM), sensitivity and noise were calculated. Compared with the 125-mAs/5-mm setting for each algorithm, significant reductions in FOM (p<0.05) and sensitivity (p<0.05) were found for all three algorithms for all exposures at 1-mm thickness and for all slice thicknesses at 25 mAs, with the exception of the 25-mAs/5-mm setting for the B algorithm. Sensitivity was also significantly reduced for all exposures at 3-mm thickness for the A algorithm (p<0.05). Noise for the A and iDose(4) algorithms was approximately 13% and 21% lower, respectively, than for the B algorithm. Superior performance for hepatic lesion detection was not shown with either a smoothing FBP algorithm or a hybrid FBP/iterative algorithm compared with a standard FBP technique, even though noise reduction with thinner slices was demonstrated with the alternative approaches. Reductions in image noise with non-standard CT algorithms do not necessarily translate to an improvement in low-contrast object detection.