Using GPUs for beamforming acceleration on SAFT imaging

SAFT techniques are based on the sequential activation, in emission and reception, of the array elements and the post-processing of all the received signals to compose the image. Thus, the image generation can be divided into two stages: (1) the excitation and acquisition stage, where the signals received by each element or group of elements are stored; and (2) the beamforming stage, where the signals are combined together to obtain the image pixels. The use of Graphics Processing Units (GPUs), which are programmable devices with a high level of parallelism, can accelerate the computations of the beamforming process, that usually includes different functions such as dynamic focusing, band-pass filtering, spatial filtering or envelope detection. This work shows that using GPU technology can accelerate, in more than one order of magnitude with respect to CPU implementations, the beamforming and post-processing algorithms in SAFT imaging. ©2009 IEEE.

CT dose and image quality in the last three scanner generations.

AIM To compare the computed tomography (CT) dose and image quality with the filtered back projection against the iterative reconstruction and CT with a minimal electronic noise detector. METHODS A lung phantom (Chest Phantom N1 by Kyoto Kagaku) was scanned with 3 different CT scanners: the Somatom Sensation, the Definition Flash and the Definition Edge (all from Siemens, Erlangen, Germany). The scan parameters were identical to the Siemens presetting for THORAX ROUTINE (scan length 35 cm and FOV 33 cm). Nine different exposition levels were examined (reference mAs/peek voltage): 100/120, 100/100, 100/80, 50/120, 50/100, 50/80, 25/120, 25/100 and 25 mAs/80 kVp. Images from the SOMATOM Sensation were reconstructed using classic filtered back projection. Iterative reconstruction (SAFIRE, level 3) was performed for the two other scanners. A Stellar detector was used with the Somatom Definition Edge. The CT doses were represented by the dose length products (DLPs) (mGycm) provided by the scanners. Signal, contrast, noise and subjective image quality were recorded by two different radiologists with 10 and 3 years of experience in chest CT radiology. To determine the average dose reduction between two scanners, the integral of the dose difference was calculated from the lowest to the highest noise level. RESULTS When using iterative reconstruction (IR) instead of filtered back projection (FBP), the average dose reduction was 30%, 52% and 80% for bone, soft tissue and air, respectively, for the same image quality (P < 0.0001). The recently introduced Stellar detector (Sd) lowered the radiation dose by an additional 27%, 54% and 70% for bone, soft tissue and air, respectively (P < 0.0001). The benefit of dose reduction was larger at lower dose levels. With the same radiation dose, an average of 34% (22%-37%) and 25% (13%-46%) more contrast to noise was achieved by changing from FBP to IR and from IR to Sd, respectively. For the same contrast to noise level, an average of 59% (46%-71%) and 51% (38%-68%) dose reduction was produced for IR and Sd, respectively. For the same subjective image quality, the dose could be reduced by 25% (2%-42%) and 44% (33%-54%) using IR and Sd, respectively. CONCLUSION This study showed an average dose reduction between 27% and 70% for the new Stellar detector, which is equivalent to using IR instead of FBP.

Feasible Dose Reduction in Routine Chest Computed Tomography Maintaining Constant Image Quality Using the Last Three Scanner Generations: From Filtered Back Projection to Sinogram-affirmed Iterative Reconstruction and Impact of the Novel Fully Integrated Detector Design Minimizing Electronic Noise

OBJECTIVE The aim of the present study was to evaluate a dose reduction in contrast-enhanced chest computed tomography (CT) by comparing the three latest generations of Siemens CT scanners used in clinical practice. We analyzed the amount of radiation used with filtered back projection (FBP) and an iterative reconstruction (IR) algorithm to yield the same image quality. Furthermore, the influence on the radiation dose of the most recent integrated circuit detector (ICD; Stellar detector, Siemens Healthcare, Erlangen, Germany) was investigated. MATERIALS AND METHODS 136 Patients were included. Scan parameters were set to a thorax routine: SOMATOM Sensation 64 (FBP), SOMATOM Definition Flash (IR), and SOMATOM Definition Edge (ICD and IR). Tube current was set constantly to the reference level of 100 mA automated tube current modulation using reference milliamperes. Care kV was used on the Flash and Edge scanner, while tube potential was individually selected between 100 and 140 kVp by the medical technologists at the SOMATOM Sensation. Quality assessment was performed on soft-tissue kernel reconstruction. Dose was represented by the dose length product. RESULTS Dose-length product (DLP) with FBP for the average chest CT was 308 mGy*cm ± 99.6. In contrast, the DLP for the chest CT with IR algorithm was 196.8 mGy*cm ± 68.8 (P = 0.0001). Further decline in dose can be noted with IR and the ICD: DLP: 166.4 mGy*cm ± 54.5 (P = 0.033). The dose reduction compared to FBP was 36.1% with IR and 45.6% with IR/ICD. Signal-to-noise ratio (SNR) was favorable in the aorta, bone, and soft tissue for IR/ICD in combination compared to FBP (the P values ranged from 0.003 to 0.048). Overall contrast-to-noise ratio (CNR) improved with declining DLP. CONCLUSION The most recent technical developments, namely IR in combination with integrated circuit detectors, can significantly lower radiation dose in chest CT examinations.

Drivers of technology substitution : successive generations of high tech products

Principal Topic High technology consumer products such as notebooks, digital cameras and DVD players are not introduced into a vacuum. Consumer experience with related earlier generation technologies, such as PCs, film cameras and VCRs, and the installed base of these products strongly impacts the market diffusion of the new generation products. Yet technology substitution has received only sparse attention in the diffusion of innovation literature. Research for consumer durables has been dominated by studies of (first purchase) adoption (c.f. Bass 1969) which do not explicitly consider the presence of an existing product/technology. More recently, considerable attention has also been given to replacement purchases (c.f. Kamakura and Balasubramanian 1987). Only a handful of papers explicitly deal with the diffusion of technology/product substitutes (e.g. Norton and Bass, 1987: Bass and Bass, 2004). They propose diffusion-type aggregate-level sales models that are used to forecast the overall sales for successive generations. Lacking household data, these aggregate models are unable to give insights into the decisions by individual households - whether to adopt generation II, and if so, when and why. This paper makes two contributions. It is the first large-scale empirical study that collects household data for successive generations of technologies in an effort to understand the drivers of adoption. Second, in comparision to traditional analysis that evaluates technology substitution as an ''adoption of innovation'' type process, we propose that from a consumer's perspective, technology substitution combines elements of both adoption (adopting the new generation technology) and replacement (replacing the generation I product with generation II). Based on this proposition, we develop and test a number of hypotheses. Methodology/Key Propositions In some cases, successive generations are clear ''substitutes'' for the earlier generation, in that they have almost identical functionality. For example, successive generations of PCs Pentium I to II to III or flat screen TV substituting for colour TV. More commonly, however, the new technology (generation II) is a ''partial substitute'' for existing technology (generation I). For example, digital cameras substitute for film-based cameras in the sense that they perform the same core function of taking photographs. They have some additional attributes of easier copying and sharing of images. However, the attribute of image quality is inferior. In cases of partial substitution, some consumers will purchase generation II products as substitutes for their generation I product, while other consumers will purchase generation II products as additional products to be used as well as their generation I product. We propose that substitute generation II purchases combine elements of both adoption and replacement, but additional generation II purchases are solely adoption-driven process. Extensive research on innovation adoption has consistently shown consumer innovativeness is the most important consumer characteristic that drives adoption timing (Goldsmith et al. 1995; Gielens and Steenkamp 2007). Hence, we expect consumer innovativeness also to influence both additional and substitute generation II purchases. Hypothesis 1a) More innovative households will make additional generation II purchases earlier. 1 b) More innovative households will make substitute generation II purchases earlier. 1 c) Consumer innovativeness will have a stronger impact on additional generation II purchases than on substitute generation II purchases. As outlined above, substitute generation II purchases act, in part like a replacement purchase for the generation I product. Prior research (Bayus 1991; Grewal et al 2004) identified product age as the most dominant factor influencing replacements. Hence, we hypothesise that: Hypothesis 2: Households with older generation I products will make substitute generation II purchases earlier. Our survey of 8,077 households investigates their adoption of two new generation products: notebooks as a technology change to PCs, and DVD players as a technology shift from VCRs. We employ Cox hazard modelling to study factors influencing the timing of a household's adoption of generation II products. We determine whether this is an additional or substitute purchase by asking whether the generation I product is still used. A separate hazard model is conducted for additional and substitute purchases. Consumer Innovativeness is measured as domain innovativeness adapted from the scales of Goldsmith and Hofacker (1991) and Flynn et al. (1996). The age of the generation I product is calculated based on the most recent household purchase of that product. Control variables include age, size and income of household, and age and education of primary decision-maker. Results and Implications Our preliminary results confirm both our hypotheses. Consumer innovativeness has a strong influence on both additional purchases (exp = 1.11) and substitute purchases (exp = 1.09). Exp is interpreted as the increased probability of purchase for an increase of 1.0 on a 7-point innovativeness scale. Also consistent with our hypotheses, the age of the generation I product has a dramatic influence for substitute purchases of VCR/DVD (exp = 2.92) and a strong influence for PCs/notebooks (exp = 1.30). Exp is interpreted as the increased probability of purchase for an increase of 10 years in the age of the generation I product. Yet, also as hypothesised, there was no influence on additional purchases. The results lead to two key implications. First, there is a clear distinction between additional and substitute purchases of generation II products, each with different drivers. Treating these as a single process will mask the true drivers of adoption. For substitute purchases, product age is a key driver. Hence, implications for marketers of high technology products can utilise data on generation I product age (e.g. from warranty or loyalty programs) to target customers who are more likely to make a purchase.

Portrait of Henny Molling nee Meyerhof, Therese Molling and Julie Meyerhof nee Oppenheimer - three generations of women.

from left to right: Henny Molling nee Meyerhof, Therese Molling, and Julie Meyerhof nee Oppenheimer

Portrait of Henny Molling nee Meyerhof, Therese Molling and Julie Meyerhof nee Oppenheimer - three generations of women.

from left to right: Henny Molling nee Meyerhof, Therese Molling, and Julie Meyerhof nee Oppenheimer

Three generations of Therese Gottschalk nee Molling, her daughter Elizabeth Gottschalk and Grandmother Henny Molling; Hannover, Germany.

From left to right: Therese Gottschalk nee Molling, Elizabeth Gottschalk, Henny Molling

Portrait of four generations of women: Henny Molling nee Meyerhof, Elizabeth Gottschalk, Julie Meyerhof nee Oppenheimer and Therese Gottschalk nee Molling Portraits; Family

From left to right: Henny Molling, born Meyerhof, Elizabeth Gottschalk, Julie Meyerhof born Oppenheimer, and Therese Gottschalk, born Molling.

Portrait of Henny Molling nee Meyerhof, Therese Molling and Julie Meyerhof nee Oppenheimer - three generations of women.

from left to right: Henny Molling nee Meyerhof, Therese Molling, and Julie Meyerhof nee Oppenheimer

Portrait of Henny Molling nee Meyerhof, Therese Molling and Julie Meyerhof nee Oppenheimer - three generations of women.

from left to right: Henny Molling nee Meyerhof, Therese Molling, and Julie Meyerhof nee Oppenheimer

Three generations of Therese Gottschalk nee Molling, her daughter Elizabeth Gottschalk and Grandmother Henny Molling; Hannover, Germany.

From left to right: Therese Gottschalk nee Molling, Elizabeth Gottschalk, Henny Molling

Portrait of four generations of women: Henny Molling nee Meyerhof, Elizabeth Gottschalk, Julie Meyerhof nee Oppenheimer and Therese Gottschalk nee Molling Portraits; Family

From left to right: Henny Molling, born Meyerhof, Elizabeth Gottschalk, Julie Meyerhof born Oppenheimer, and Therese Gottschalk, born Molling.

Zucker Family Nine Generations

Genealogy of nine generations of the Zucker family.

Dose Reduction in Standard Head CT: First Results from a New Scanner Using Iterative Reconstruction and a New Detector Type in Comparison with Two Previous Generations of Multi-slice CT

PURPOSE Computed tomography (CT) accounts for more than half of the total radiation exposure from medical procedures, which makes dose reduction in CT an effective means of reducing radiation exposure. We analysed the dose reduction that can be achieved with a new CT scanner [Somatom Edge (E)] that incorporates new developments in hardware (detector) and software (iterative reconstruction). METHODS We compared weighted volume CT dose index (CTDIvol) and dose length product (DLP) values of 25 consecutive patients studied with non-enhanced standard brain CT with the new scanner and with two previous models each, a 64-slice 64-row multi-detector CT (MDCT) scanner with 64 rows (S64) and a 16-slice 16-row MDCT scanner with 16 rows (S16). We analysed signal-to-noise and contrast-to-noise ratios in images from the three scanners and performed a quality rating by three neuroradiologists to analyse whether dose reduction techniques still yield sufficient diagnostic quality. RESULTS CTDIVol of scanner E was 41.5 and 36.4 % less than the values of scanners S16 and S64, respectively; the DLP values were 40 and 38.3 % less. All differences were statistically significant (p < 0.0001). Signal-to-noise and contrast-to-noise ratios were best in S64; these differences also reached statistical significance. Image analysis, however, showed "non-inferiority" of scanner E regarding image quality. CONCLUSIONS The first experience with the new scanner shows that new dose reduction techniques allow for up to 40 % dose reduction while still maintaining image quality at a diagnostically usable level.

Tomb of Amenhotep, excavated artifact, Yuni and His Wife Renenutet, rear view, two scenes illustrating the ancient Egyptian ideal of affection and remembrance among family generations

Asyut, Egypt; painted limestone