ＣＴ−ＡＥＣ使用下の上腹部撮影に対する画質変動

Digital breast tomosynthesis (DBT) is becoming an important part of breast cancer screening and diagnosis. Compared to two-dimensional mammography, tomosynthesis introduces limited three-dimensional (3D) resolution, but maintains high in-plane resolution, low dose, and allows for similar clinical protocols. The scanning motion and oblique projections of tomosynthesis acquisitions introduce shift-variance to the image quality, in addition to effects such as source blurring and geometric magnification. Shift-variant detector response caused by oblique incidence has been extensively studied previously and is most easily mitigated by letting the source and detector move in sync. In addition, conical reconstruction grids, that is, a grid aligned with the central tomosynthesis projection, have been proposed to compensate for magnification effects. This paper introduces a shift-variant cascaded systems model for tomosynthesis and validates it against measurements. As an example, the model was used to investigate the shift-variance of a tomosynthesis system. The shift-variant cascaded systems model was validated on a slit-scanning photon-counting DBT system, with synchronous source-detector movement, using simple back-projection in a conical reconstruction volume. The modulation transfer function (MTF), normalized noise-power spectrum (NNPS), and detective quantum efficiency (DQE) were used as figures of merit. Simulations were performed for single points while measurements were done over a finite volume, assuming local shift invariance. To investigate the full extent of shift-variance, 80 locations across the volume were simulated, and the MTF and DQE at 2.5lp/mm were calculated as a function of position. The simulated metrics generally agreed well with their corresponding measurements. The frequency at 50% MTF along the scan direction showed a relatively small variation, ranging from 2.1 to 2.4lp/mm for the different locations. The frequency at 50% MTF along the chest-mammilla direction showed a larger variation, ranging from 2.9 to 3.8lp/mm. All points exhibited a similarly shaped NNPS but the noise magnitude varied with slice height. The zero-frequency DQE in reconstructed slices was lower than that of the projections, an effect likely caused by noise-aliasing increasing the zero-frequency noise. A shift-variant cascaded systems model has been developed for slit-scanning tomosynthesis using simple back-projection. The model was successfully validated against measurements. Even though the study was performed on a slit-scanning system, several parts of the framework can be applied and extended to other tomosynthesis geometries. The conical reconstruction grid has low variation in image quality in the scan direction where the 3D information is acquired, but source and geometric magnification still dominate in the slit direction, causing a larger variation in image quality. We conclude that image quality is close to shift-invariant in the scan direction, but not in the height and chest-mammilla directions, and we recommend that small measurement volumes are used when measuring image quality in these directions to minimize the effects of shift variance.

The original JPEG compression standard is efficient at low to medium levels of compression with relatively low levels of loss in visual image quality and has found widespread use in the imaging industry. Excessive compression using JPEG however, results in well-known artifacts such as blocking and ringing, and the variation in image quality as a result of differing scene content is well documented. JPEG 2000 has been developed to improve on JPEG in terms of functionality and image quality at lower bit rates. One of the more fundamental changes is the use of a discrete wavelet transform instead of a discrete cosine transform, which provides several advantages both in terms of the way in which the image is encoded and overall image quality. This study involves a comparison of subjective image quality between JPEG and JPEG 2000 to establish whether JPEG 2000 does indeed demonstrate significant improvements in visual quality. A particular focus of this work is the inherent scene dependency of the two algorithms and their influence on subjective image quality results. Further work on the characterization of scene content is carried out in a connected study [S. Triantaphillidou, E. Allen, and R. E. Jacobson, Image quality comparison between JPEG and JPEG2000. II. Scene dependency, scene analysis, and classification

PurposeOur purpose was to perform an in vivo validation of ultrasound imaging for intrafraction motion estimation using the Elekta Clarity Autoscan system during prostate radiation therapy. The study was conducted as part of the Clarity-Pro trial (NCT02388308).Methods and MaterialsInitial locations of intraprostatic fiducial markers were identified from cone beam computed tomography scans. Marker positions were translated according to Clarity intrafraction 3-dimensional prostate motion estimates. The updated locations were projected onto the 2-dimensional electronic portal imager plane. These Clarity-based estimates were compared with the actual portal-imaged 2-dimensional marker positions. Images from 16 patients encompassing 80 fractions were analyzed. To investigate the influence of intraprostatic markers and image quality on ultrasound motion estimation, 3 observers rated image quality, and the marker visibility on ultrasound images was assessed.ResultsThe median difference between Clarity-defined intrafraction marker locations and portal-imaged marker locations was 0.6 mm (with 95% limit of agreement at 2.5 mm). Markers were identified on ultrasound in only 3 of a possible 240 instances. No linear relationship between image quality and Clarity motion estimation confidence was identified. The difference between Clarity-based motion estimates and electronic portal–imaged marker location was also independent of image quality. Clarity estimation confidence was degraded in a single fraction owing to poor probe placement.ConclusionsThe accuracy of Clarity intrafraction prostate motion estimation is comparable with that of other motion-monitoring systems in radiation therapy. The effect of fiducial markers in the study was deemed negligible as they were rarely visible on ultrasound images compared with intrinsic anatomic features. Clarity motion estimation confidence was robust to variations in image quality and the number of ultrasound-imaged anatomic features; however, it was degraded as a result of poor probe placement.

Respiratory Motional Effect on Cone-Beam CT in Lung Radiation Surgery

No-reference image quality assessment in contourlet domain

Photographic identification is an essential research and management tool for marine mammal scientists. However, manual identification of individuals is time‐consuming. To shorten processing times, we developed finFindR, an open‐source application that uses a series of neural networks to autonomously locate dorsal fins in unedited field images, quantify an individual's unique fin characteristics, and match them to an existing photograph catalog. During a blind test comparing manual searching to finFindR for common bottlenose dolphin (Tursiops Tursiops truncatus) photographs, experienced photo‐identification technicians achieved similar match rates but examined an order of magnitude fewer photographs using finFindR (an average of 10 required with finFindR versus 124 with manual search). In those tests, the correct identity was ranked in the first position in 88% of cases and was within the top 50 ranked positions in 97% of cases. Our observations suggest that finFindR's matching capabilities are robust to moderate variation in image quality and fin distinctiveness. Importantly, finFindR allows users to build a catalog of known individuals through time and match an unlimited number of individuals instead of being restricted to a predefined set. finFindR's convolutional neural networks could be re‐trained to identify members of many marine mammal species without altering finFindR's inherent structure.

To benchmark the dose from paediatric head and chest examinations on computed tomography (CT) scanners throughout Scotland, to identify scanners that may require optimisation and to provide optimisation advice based on the protocols from better performing scanners. Anthropomorphic phantoms corresponding to 1, 5 and 10 year olds were sent to 50 CT scanners around Scotland. Head and chest examinations were undertaken by local staff using local techniques on each scanner with each phantom, and details of the protocols used were recorded. Computed tomography dose index (CTDI)vol and dose length product (DLP) were recorded post-scan. There is a significant variation in performance throughout Scotland. For head examinations, the highest DLP is 13 times the lowest for an equivalent sized phantom. For chest examinations, the highest is 128 times the lowest for an equivalent sized phantom. The wide range of CT dose measurements indicates the potential for variation in image quality across Scotland. Feedback has been provided to all participating sites on their individual results compared to the national data set. Specific feedback was provided where relevant on potential considerations for optimisation. Scanners that may be undertaking paediatric CT head and chest examinations in a sub-optimal manner throughout Scotland have been identified along with those aspects of a scan protocol that are most likely to lead to sub-optimal performance.

The flowering plant genus Protea is a dominant representative for the biodiversity of the Cape Floristic Region in South Africa, and from a conservation point of view important to monitor. The recent surge in popularity of crowd-sourced wildlife monitoring platforms presents both challenges and opportunities for automatic image based species identification. We consider the problem of identifying the Protea species in a given image with additional (but optional) attributes linked to the observation, such as location and date. We collect training and test data from a crowd-sourced platform, and find that the Protea identification problem is exacerbated by considerable inter-class similarity, data scarcity, class imbalance, as well as large variations in image quality, composition and background. Our proposed solution consists of three parts. The first part incorporates a variant of multi-region attention into a pretrained convolutional neural network, to focus on the flower-head in the image. The second part performs coarser-grained classification on subgenera (superclasses) and then rescales the output of the first part. The third part conditions a probabilistic model on the additional attributes associated with the observation. We perform an ablation study on the proposed model and its constituents, and find that all three components together outperform our baselines and all other variants quite significantly.

Extracting coronary arteries is important in clinical applications of cardiac Computed Tomography Angiography (CTA). However, obtaining an accurate segmentation of coronary artery is challenging because of complex anatomy, variation in image quality, as well as motion induced by heart beating and respiration. This paper presented a semi-automatic framework dedicated to coronary artery segmentation in 3D+t Computed Tomography Angiography (CTA) sequence. The proposed segmentation scheme consists of two parts: vessel centerline extraction and vessel shape recovering. For the starting phase, spherical flux based minimal path method was employed to extract the main centerlines of coronary artery tree. To implement similar centerline extraction scheme automatically in subsequent phases, a novel method key-points detecting method was proposed and an endpoints searching procedure was taken. In this process, the key-points on distal vessels are easily got mismatched. A centerline tracking algorithm constrained by patient-specific vessel length was used to complete the extraction. The the overlap (OV) results of proposed framework are 93.56% (LAD), 90.96% (LCX) and 93.76% (RCA). We also achieved accuracy (AC) r:esult: 0.6508mm (LAD) 0.6141mm (LCX) and 0.6297mm (RCA) which are less than the data voxel size (0.68mm).

Quality assessment of prenatal ultrasonography is essential for the screening of fetal central nervous system (CNS) anomalies. The interpretation of fetal brain structures is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. With recent advancement in Artificial Intelligence (AI), computer assisted diagnosis has shown promising results, being able to provide expert level diagnosis in matter of seconds and therefore has the potential to improve access to quality and standardized care for all. Specifically, with advent of deep learning (DL), assistance in precise anatomy identification through semantic segmentation essential for the reliable assessment of growth and neurodevelopment, and detection of structural abnormalities have been proposed. However, existing works only identify certain structures (e.g., cavum septum pellucidum [CSP], lateral ventricles [LV], cerebellum ) from either of the axial views (transventricular [TV], transcerebellar [TC]), limiting the scope for a thorough anatomical assessment as per practice guidelines necessary for the screening of CNS anomalies. Further, existing works do not analyze the generalizability of these DL algorithms across images from multiple ultrasound devices and centers, thus, limiting their real-world clinical impact. In this study, we propose a deep learning (DL) based segmentation framework for the automated segmentation of 10 key fetal brain structures from 2 axial planes from fetal brain USG images (2D). We developed a custom U-Net variant that uses inceptionv4 block as a feature extractor and leverages custom domain-specific data augmentation. Quantitatively, the mean (10 structures; test sets 1/2/3/4) Dice-coefficients were: 0.827, 0.802, 0.731, 0.783. Irrespective of the USG device/center, the DL segmentations were qualitatively comparable to their manual segmentations. The proposed DL system offered a promising and generalizable performance (multi-centers, multi-device) and also presents evidence in support of device-induced variation in image quality (a challenge to generalizibility) by using UMAP analysis. Its clinical translation can assist a wide range of users across settings to deliver standardized and quality prenatal examinations.

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively.

The objective of our study was to reduce variation in image quality of orthogonal reformatted images generated from long-z-axis CT angiography (CTA) studies of the upper and lower extremities. Upper and lower extremity CTA studies were targeted at a single health care system. A correctly performed CTA examination was defined as one that met the following three criteria: Sagittal and coronal reformats were obtained, a high-resolution matrix greater than 512 × 512 was used, and reformatted images were available in a distance-measurable format. Baseline data were collected from February 1, 2014, through September 30, 2014. Corrective actions were implemented during three consecutive plan-do-check-act (PDCA) cycles from October 1, 2014, through July 31, 2015, that addressed human, technical, and systematic variations. A 3-month maintenance period followed in which no intervention was performed. Longitudinal data were analyzed monthly using a statistical process control chart (p-chart). The total number of long-z-axis extremity CTA studies analyzed was as follows: 351 CTA studies were analyzed at baseline, 94 at the first PDCA cycle, 92 at the second PDCA cycle, 114 at the third PDCA cycle, and 138 during the maintenance period. The monthly rate of correctly performed studies ranged from 7% to 51% (mean, 38% ± 13% [SD]) during the baseline period, 32-59% (mean, 46% ± 14%) during the first PDCA cycle, 40-81% (mean, 61% ± 21%) during the second PDCA cycle, and 80-82% (mean, 81% ± 0.9%) during the third PDCA cycle. The monthly rate improved to 90-91% (mean, 91% ± 0.5%) during the maintenance period. The upper and lower control limits of the p-chart were upshifted after the second and third PDCA cycles. Correcting systematic and technical variations led to the greatest improvements in reformat accuracy. Obtaining consistently and correctly reformatted images from long-z-axis CTA studies is achievable using iterative PDCA cycles.

To perform a calibration study to provide data to help improve consistency in the pressure that is applied during mammography. Automatic readouts of breast thickness accuracy vary between mammography machines; therefore, one machine was selected for calibration. 250 randomly selected patients were invited to participate; 235 agreed, and 940 compression data sets were recorded (breast thickness, breast density and pressure). Pressure (measured in decanewtons) was increased from 5 daN through 1-daN intervals until the practitioner felt that the pressure was appropriate for imaging; at each pressure increment, breast thickness was recorded. Graphs were generated and equations derived; second-order polynomial trend lines were applied using the method of least squares. No difference existed between breast densities, but a difference did exist between "small" (15×29 cm) and "medium/large" (18×24/24×30 cm) paddles. Accordingly, data were combined. Graphs show changes in thickness from 5-daN pressure for craniocaudal and mediolateral oblique views for the small and medium/large paddles combined. Graphs were colour coded into three segments indicating high, intermediate and low gradients [≤-2 (light grey); -1.99 to -1 (mid-grey); and ≥-0.99 (dark grey)]. We propose that 13 daN could be an appropriate termination pressure on this mammography machine. Using patient compression data we have calibrated a mammography machine to determine its breast compression characteristics. This calibration data could be used to guide practice to minimise pressure variations between practitioners, thereby improving patient experience and reducing potential variation in image quality. For the first time, pressure-thickness graphs are now available to help guide mammographers in the application of pressure.

Optical coherence tomography (OCT) offers new options for imaging the ciliary muscle allowing direct in vivo visualization. However, variation in image quality along the length of the muscle prevents accurate delineation and quantification of the muscle. Quantitative analyses of the muscle are accompanied by variability in segmentation between examiners and between sessions for the same examiner. In processes such as accommodation where changes in muscle thickness may be tens of microns- the equivalent of a small number of image pixels, differences in segmentation can influence the magnitude and potentially the direction of thickness change. A detailed analysis of variability in ciliary muscle thickness measurements was performed to serve as a benchmark for the extent of this variability in studies on the ciliary muscle. Variation between sessions and examiners were found to be insignificant but the magnitude of variation should be considered when interpreting ciliary muscle results.

PurposeTo develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. MethodsInterviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). ResultsTwelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. ConclusionsBy following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.