Soft-tissue lesion and microcalcification detectability in cone-beam breast CT: cascaded system analysis.

The relationship between theoretical descriptions of imaging performance (Fourier-based cascaded systems analysis) and the performance of real human observers was investigated for various detection and discrimination tasks. Dual-energy (DE) imaging provided a useful basis for investigating this relationship, because it presents a host of acquisition and processing parameters that can significantly affect signal and noise transfer characteristics and, correspondingly, human observer performance. The detectability index was computed theoretically using: 1) cascaded systems analysis of the modulation transfer function (MTF), and noise-power spectrum (NPS) for DE imaging; 2) a Fourier description of imaging task; and 3.) integration of MTF, NPS, and task function according to various observer models, including Fisher-Hotelling and non-prewhitening with and without an eye filter and internal noise. Three idealized tasks were considered: sphere detection, shape discrimination (sphere vs. disk), and texture discrimination (uniform vs. textured disk). Using images of phantoms acquired on a prototype DE imaging system, human observer performance was assessed in multiple-alternative forced choice (MAFC) tests, giving an estimate of area under the ROC curve (A_Ζ). The degree to which the theoretical detectability index correlated with human observer performance was investigated, and results agreed well over a broad range of imaging conditions, depending on the choice of observer model. Results demonstrated that optimal DE image acquisition and decomposition parameters depend significantly on the imaging task. These studies provide important initial validation that the detectability index derived theoretically by Fourier-based cascaded systems analysis correlates well with actual human observer performance and represents a meaningful metric for system optimization.

Photon counting detectors (PCDs) are an emerging technology with applications in spectral and low-dose radiographic and tomographic imaging. This paper develops an analytical model of PCD imaging performance, including the system gain, modulation transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE). A cascaded systems analysis model describing the propagation of quanta through the imaging chain was developed. The model was validated in comparison to the physical performance of a silicon-strip PCD implemented on an experimental imaging bench. The signal response, MTF, and NPS were measured and compared to theory as a function of exposure conditions (70 kVp, 1-7 mA), detector threshold, and readout mode (i.e., the option for coincidence detection). The model sheds new light on the dependence of spatial resolution, charge sharing, and additive noise effects on threshold selection and was used to investigate the factors governing PCD performance, including the fundamental advantages and limitations of PCDs in comparison to energy-integrating detectors (EIDs) in the linear regime for which pulse pileup can be ignored. The detector exhibited highly linear mean signal response across the system operating range and agreed well with theoretical prediction, as did the system MTF and NPS. The DQE analyzed as a function of kilovolt (peak), exposure, detector threshold, and readout mode revealed important considerations for system optimization. The model also demonstrated the important implications of false counts from both additive electronic noise and charge sharing and highlighted the system design and operational parameters that most affect detector performance in the presence of such factors: for example, increasing the detector threshold from 0 to 100 (arbitrary units of pulse height threshold roughly equivalent to 0.5 and 6 keV energy threshold, respectively), increased the f50 (spatial-frequency at which the MTF falls to a value of 0.50) by ∼30% with corresponding improvement in DQE. The range in exposure and additive noise for which PCDs yield intrinsically higher DQE was quantified, showing performance advantages under conditions of very low-dose, high additive noise, and high fidelity rejection of coincident photons. The model for PCD signal and noise performance agreed with measurements of detector signal, MTF, and NPS and provided a useful basis for understanding complex dependencies in PCD imaging performance and the potential advantages (and disadvantages) in comparison to EIDs as well as an important guide to task-based optimization in developing new PCD imaging systems.

Mammography is the only technique currently used for detecting microcalcification (MC) clusters, an early indicator of breast cancer. However, mammographic images superimpose a three-dimensional compressed breast image onto two-dimensional projection views, resulting in overlapped anatomical breast structures that may obscure the detection and visualization of MCs. One possible solution to this problem is the use of cone beam computed tomography (CBCT) with a flat-panel (FP) digital detector. Although feasibility studies of CBCT techniques for breast imaging have yielded promising results, they have not shown how radiation dose and x-ray tube voltage affect the accuracy with which MCs are detected by CBCT experimentally. We therefore conducted a phantom study using a FP-based CBCT system with various mean glandular doses and kVp values. An experimental CBCT scanner was constructed with a data acquisition rate of 7.5 frames/s. 10.5 and 14.5 cm diameter breast phantoms made of gelatin were used to simulate uncompressed breasts consisting of 100% glandular tissue. Eight different MC sizes of calcium carbonate grains, ranging from 180-200 microm to 355-425 microm, were used to simulate MCs. MCs of the same size were arranged to form a 5 x 5 MC cluster and embedded in the breast phantoms. These MC clusters were positioned at 2.8 cm away from the center of the breast phantoms. The phantoms were imaged at 60, 80, and 100 kVp. With a single scan (360 degrees), 300 projection images were acquired with 0.5 x, 1x, and 2x mean glandular dose limit for 10.5 cm phantom and with 1x, 2x, and 4x for 14.5 cm phantom. A Feldkamp algorithm with a pure ramp filter was used for image reconstruction. The normalized noise level was calculated for each x-ray tube voltage and dose level. The image quality of the CBCT images was evaluated by counting the number of visible MCs for each MC cluster for various conditions. The average percentage of the visible MCs was computed and plotted as a function of the MGD, the kVp, and the average MC size. The results showed that the MC visibility increased with the MGD significantly but decreased with the breast size. The results also showed that the x-ray tube voltage affects the detection of MCs under different circumstances. With a 50% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 348(+/-2), 288(+/-7), 257(+/-2) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 355 (+/-1), 307 (+/-7), 275 (+/-5) microm at 6, 12, and 24 mGy, respectively. With a 75% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 367 (+/-1), 316 (+/-7), 265 (+/-3) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 377 (+/-3), 334 (+/-5), 300 (+/-2) microm at 6, 12, and 24 mGy, respectively.

IntroductionLe but de notre étude était de déterminer le profil clinique et évolutif des lésions de la peau et des parties molles des sujets diabétiques suivis à la salle de pansement.MéthodesIl s'agissait d'une étude observationnelle descriptive et analytique menée du 1er janvier au 31 décembre 2017 à la salle de pansement du centre Marc Sankale de Dakar. Notre étude a porté sur les sujets diabétiques ayant consultés à la salle de pansement.RésultatsAu total, 37173 actes de soins ont été enregistrés au centre Marc Sankale. Les activés de soins à la salle de pansement représentaient 16418 cas soit une prévalence de 14,16%. L'âge moyen était de 56,6 ± 12 ans et le sex ratio (H/F) de 0,88. Le diabète de type 2 prédominait (78,97%) et la durée moyenne du diabète était de 8,06 ± 7,9 ans. La glycémie capillaire moyenne était de 2,4 ± 1 g/l. La neuropathie diabétique était présente chez 72,33% des cas. Les lésions se situait aux membres dans 93,98% (1185 cas). Les lésions les plus représentatives étaient l'ulcère (46,76%), l'abcès (13,46%), le phlegmon (13,20%), la gangrène (8,41%), l'érysipèle (3,78%), le mal perforant (3,53%), l'intertrigo (3,95%). Les lésions étaient infectieuses (61,41), non infectieuses (33,50%), vasculaires pures (1,57%) et Mixtes (3,70%). Sur les 1189 patients 7,57% avaient présentés une ostéite. Les germes retrouvés étaient des bactéries grams positifs (12,70%), grams négatifs (23,80%). L'amputation était corrélée à la topographie de la lésion (p=0.00), au type de lésion (p=0.000), à l'ancienneté du diabète (p=0,02), au type de diabète (p=0,008), à la présence d'ostéite (p=0,006). L'amputation etait mineur (43,33%), et majeur (37,43%). Nous avons enregistré 70 décès (5,89%).ConclusionLes lésions de la peau et des tissus mous restent dominées par le pied diabétique. La mortalité est non négligeable et le risque d'amputation était statistiquement corrélé à la topographie, au type de lésion, à l'ancienneté et le type de diabète et à l'existence d'ostéite.

This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called <i>relevance vectors</i>. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

Microcalcification (MC) clusters in mammograms are one of the primary signs of breast cancer. In the literature, most MC detection methods follow a two-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1 mm), brightness, and shape (with diverse surroundings). In this paper, we propose a novel self-adversarial learning framework to differentiate and delineate the MC clusters in an end-to-end manner. The class activation mapping (CAM) mechanism is employed to directly generate the contours of MC clusters with the guidance of MC cluster classification and box annotations. We also propose the self-adversarial learning strategy to equip CAM with better detection capability of MC clusters by using the backbone network itself as a discriminator. Experimental results suggest that our method can achieve better performance for MC cluster detection with the contouring of MC clusters and classification of MC types.

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

The optimization of dual-energy computed tomography (DE-CT) is challenged by the lack of a theoretical foundation for image quality. This work reports a cascaded systems analysis model that was used to derive signal and noise propagation in DE-CBCT in prevalent Fourier metrics such as the noise-power spectrum (NPS) and noise-equivalent quanta (NEQ). The model was validated in comparison to measurements of the 3D NPS and NEQ in DE-CBCT images acquired using an experimental imaging bench. Task-based detectability index was derived using DE-NPS and NEQ as an objective function in optimizing DE imaging parameters such as the dose allocation factor (DA) and kVp pair. The resulting dose allocation optimization is in agreement with the practice of assigning more dose to the high-energy image (DA < 0.5), and the model provides a quantitative basis for examining the optimal dose allocation as a function of total dose, kVp pair, the presence of electronics noise, and the imaging task. An example optimization is shown for a breast tumor detection task. Using DE decomposition to cancel fibroglandular tissue (rendering a DE-CBCT image of breast tumor against an adipose tissue background) and assuming a total dose of 15mGy, the optimal kVp pair is identified at [45, 105]kVp with DA=0.46. The model is sufficiently general for applications beyond this example, demonstrating utility in the optimization in a broad range of imaging parameters. The model provides a new, valuable framework for understanding the theoretical limits of DE-CBCT imaging performance and maximizing image quality while minimizing radiation dose.

Mammography and finding of suspicious masses during self-examinations and clinical breast examinations form the primary screening tools for early detection of breast cancer. Mammography is essential for early detection of cancer, prior to any other means. Many abnormalities other than cancer are shown by screening mammography, leading to biopsy of the suspicious defect. Any enhancement that reduces the number of unnecessary biopsies is welcomed. Digital enhancement may aid in early detection of some patterns, such as microcalcification clusters, indicating the onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers, and early detection may enable a complete cure. The individual calcifications are hard to detect, due to their size and shape variability and the inhomogeneous background structure. Our study addresses only early detection of microcalcifications. We present an algorithm which locates microcalcifications based on local gray-scale variability, tissue structures and image statistics. The mammographs are digitally enhanced to accent textures and a previously developed threshold filter creates a binary mask of the calcification's spatial location.

Clustered microcalcifications are often the first sign of breast cancer in a mammogram. Nevertheless, all clustered microcalcifications are not found by an individual radiologist reading a mammogram. The use of a second reader may find those clusters of microcalcifications not found by the first reader, thereby improving the sensitivity of detecting clustered microcalcifications. Our laboratory has developed a computerized scheme for the detection of clustered microcalcifications, which can act like a second reader, that is undergoing clinical evaluation. This paper concerns the feature analysis stage of the computer scheme, which is designed to remove some of the false-computer detections. We have examined three methods of feature analysis, namely, rule based (the method currently used), an artificial neural network (ANN), and a combined method. In an independent database of 50 images, at a sensitivity of 83%, the average number of false positive (FP) detections per image was: 1.9 for rule-based, 1.6 for ANN, and 0.8 for the combined method. We demonstrate that the combined method performs best because each of the two stages eliminates different types of false positives.

Design and optimization of medical imaging systems benefit from accurate theoretical modeling that identifies the physical factors governing image quality, particularly in the early stages of system development. This work extends Fourier metrics of imaging performance and detectability index (d') to tomosynthesis and cone-beam CT (CBCT) and investigates the extent to which d' is a valid descriptor of task-based imaging performance as assessed by human observers, The detectability index for tasks presented in 2D slices (d'(slice)) was derived from 3D cascaded systems analysis of tomosynthesis and CBCT. Anatomical background noise measured in a physical phantom presenting power-law spectral density was incorporated in the "generalized" noise-equivalent quanta. Theoretical calculations of d'(slice) were performed as a function of total angular extent (theta(tot)) of source-detector orbit ranging 10 degrees - 360 degrees under two acquisition schemes: (i) Constant angular separation between projections (constant-delta theta), giving variable number of projections (N(proj)) and dose vs theta(tot) and (ii) constant number of projections (constant-N(proj)), giving constant dose (but variable angular sampling) with theta(tot). Five simple observer models were investigated: Prewhitening (PW), prewhitening with eye filter and internal noise (PWEi), nonprewhitening (NPW), nonprewhitening with eye filter (NPWE), and nonprewhitening with eye filter and internal noise (NPWEi). Human observer performance was measured in 9AFC tests for five simple imaging tasks presented within uniform and power-law clutter backgrounds. Measurements (from 9AFC tests) and theoretical calculations (from cascaded systems analysis of d'(slice)) were compared in terms of area under the ROC curve (A(z)) Reasonable correspondence between theoretical calculations and human observer performance was achieved for all imaging tasks over the broad range of experimental conditions and acquisition schemes. The PW and PWEi observer models tended to overestimate detectability, while the various NPW models predicted observer performance fairly well, with NPWEi giving the best overall agreement. Detectability was shown to increase with theta(tot) due to the reduction of out-of-plane clutter, reaching a plateau after a particular theta(tot) that depended on the imaging task. Depending on the acquisition scheme, however (i.e., constant-N(proj) or delta theta), detectability was seen in some cases to decline at higher theta(tot) due to tradeoffs among quantum noise, background clutter, and view sampling. Generalized detectability index derived from a 3D cascaded systems model shows reasonable correspondence with human observer performance over a fairly broad range of imaging tasks and conditions, although discrepancies were observed in cases relating to orbits intermediate to 180 degrees and 360 degrees. The basic correspondence of theoretical and measured performance supports the application of such a theoretical framework for system design and optimization of tomosynthesis and CBCT.

The increased development and production of Computed Tomography (CT) scanner technology has expanded patient access to advanced and affordable medical imaging technologies but has also introduced sources of variability in the clinical imaging landscape, which may influence patient care. This study examines the impact of intra-scanner and inter-scanner variability on image quality and quantitative imaging tasks, with a focus on the detectability index (d') as a measure of patient-specific task performance. We evaluated 813 clinical phantom image sets from the COPDGene study, aggregated by CT scanner make, model, and acquisition and reconstruction protocol. Each phantom image set was assessed for image quality metrics, including the Noise Power Spectrum (NPS) and in-plane Modulation Transfer Function (MTF). The d' index was calculated for 12 hypothetical lesion detection tasks, emulating clinically relevant lung and liver lesions of varying sizes and contrast levels. Qualitatively, analysis showed intra-scanner variability in NPS and MTF curves measured for identical acquisition and reconstruction settings. Inter-scanner comparisons demonstrated variability in d' measurements across different scanner makes and models, of similar acquisition and reconstruction settings. The study showed an intra-scanner variability of up to 13.7% and an inter-scanner variability of up to 19.3% in the d' index. These findings emphasize the need for considering scanner variability in patient-centered care and indicate that CT technology may influence the reliability of imaging tasks. The results of this study further motivate the development of virtual scanner models to better model and mitigate the variability observed in the clinical imaging landscape.

This study aimed to investigate a method for empirically evaluating 3D imaging task performance of breast tomosynthesis imaging systems. A simulation and experimental approach was used to develop a robust method for performance assessment. To identify a method for experimentally assessing the 3D modulation transfer function (MTF), a breast tomosysnthesis system was first simulated using cascaded system analysis to model the signal and noise characteristics of the projections. A range of spheres with varying contrast and size were reconstructed using filtered back projection from which the 3D MTF was evaluated. Results revealed that smaller spheres result in lower artifacts in the measured MTF, where a sphere of 0.5 mm was found ideal for experimental purposes. A clinical tomosynthesis unit was used as a platform for quantifying the effect of acquisition and processing parameters (e.g., angular extent and sampling, dose, and voxel size) on breast imaging performance. The 3D noise-power spectrum (NPS) was measured using a uniform phantom and 3D MTF was measured using 0.5 mm ruby spheres. These metrics were combined with a mathematical description of imaging task to generate a figure of merit called the detectability index for system evaluation and optimization. Clinically relevant imaging tasks were considered, such as the detection and localization of a spherical mass. The detectability index was found to provide a useful metric that accounts for the complex 3D imaging characteristics of breast tomosynthesis. Results highlighted the dependence of optimal technique on the imaging task. They further provided initial validation of an empirically assessed figure of merit for clinical performance assessment and optimization of breast tomosynthesis systems.

Soft tissue lesions are frequently treated by the physical therapist. Two major problems in treating these types of lesions are identifying the tissue causing the pain and selecting the appropriate modality to diminish the pain. In recent years, physicians and physical therapists have become interested in the evaluation and treatment methods of soft tissue lesions developed by Cyriax.1 Cyriax uses a systematic approach for identifying the exact location of a soft tissue lesion. He advocates using manipulation, deep friction massage, and the injection of various drugs in treating soft tissue lesions. This case report describes the application of the Cyriax approach in treating a patient referred to physical therapy with multiple soft tissue lesions at both ankles. This particular case was selected because it demonstrates the effectiveness of the Cyriax approach for a patient with multiple lesions about one joint.…