Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment

PurposeTo assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform.Materials and methodsAcquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDI_vol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d′) was computed to model the detection task of two enhanced iodine lesions as function of keV.ResultsNoise-magnitude was significantly reduced between 40 to 70 keV by ?56 ± 0% (SD) (range: ?56%–?55%) with FBP; ?56 ± 0% (SD) (?56%–?56%) with A3; and ?57 ± 1% (SD) (range: ?57%–?56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2 mg/mL, d’ values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d’ peaked at 60 keV. For the other simulated lesion, d’ values were highest at 40 keV and decreased beyond.ConclusionAt low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.     

From the Cover: Communication consumes 35 times more energy than computation in the human cortex,but both costs are needed to predict synapse number

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843–1864 (1960)] and [J. Sawada, D. S. Modha, “Synapse: Scalable energy-efficient neurosynaptic computing” in Application of Concurrency to System Design (ACSD) (2013), pp. 14–15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a 108-fold discrepancy between biological and lowest possible values of a neuron’s computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

The purpose of the brain is to process information, but that leaves us with the problem of finding appropriate definitions of information processing. We assume that given enough time and given a sufficiently stable environment (e.g., the common internals of the mammalian brain), then Nature’s constructions approach an optimum. The problem is to find which function or combined set of functions is optimal when incorporating empirical values into these function(s). The initial example in neuroscience is ref. 1, which shows that information capacity is far from optimized, especially in comparison to the optimal information per joule which is in much closer agreement with empirical values. Whenever we find such an agreement between theory and experiment, we conclude that this optimization, or near optimization, is Nature’s perspective. Using this strategy, we and others seek quantified relationships with particular forms of information processing and require that these relationships are approximately optimal (1–7). At the level of a single neuron, a recent theoretical development identifies a potentially optimal computation (8). To apply this conjecture requires understanding certain neuronal energy expenditures. Here the focus is on the energy budget of the human cerebral cortex and its primary neurons. The energy audit here differs from the premier earlier work (9) in two ways: The brain considered here is human not rodent, and the audit here uses a partitioning motivated by the information-efficiency calculations rather than the classical partitions of cell biology and neuroscience (9). Importantly, our audit reveals greater energy use by communication than by computation. This observation in turn generates additional insights into the optimal synapse number. Specifically, the bits per joule optimized computation must provide sufficient bits per second to the axon and presynaptic mechanism to justify the great expense of timely communication. Simply put from the optimization perspective, we assume evolution would not build a costly communication system and then not supply it with appropriate bits per second to justify its costs. The bits per joule are optimized with respect to N, the number of synaptic activations per interpulse interval (IPI) for one neuron, where N happens to equal the number of synapses per neuron times the success rate of synaptic transmission (below).To measure computation, and to partition out its cost, requires a suitable definition at the single-neuron level. Rather than the generic definition “any signal transformation” (3) or the neural-like “converting a multivariate signal to a scalar signal,” we conjecture a more detailed definition (8). To move toward this definition, note two important brain functions: estimating what is present in the sensed world and predicting what will be present, including what will occur as the brain commands manipulations. Then, assume that such macroscopic inferences arise by combining single-neuron inferences. That is, conjecture a neuron performing microscopic estimation or prediction. Instead of sensing the world, a neuron’s sensing is merely its capacitive charging due to recently active synapses. Using this sampling of total accumulated charge over a particular elapsed time, a neuron implicitly estimates the value of its local latent variable, a variable defined by evolution and developmental construction (8). Applying an optimization perspective, which includes implicit Bayesian inference, a sufficient statistic, and maximum-likelihood unbiasedness, as well as energy costs (8), produces a quantified theory of single-neuron computation. This theory implies the optimal IPI probability distribution. Motivating IPI coding is this fact: The use of constant amplitude signaling, e.g., action potentials, implies that all information can only be in IPIs. Therefore, no code can outperform an IPI code, and it can equal an IPI code in bit rate only if it is one to one with an IPI code. In neuroscience, an equivalent to IPI codes is the instantaneous rate code where each message is IPI−1. In communication theory, a discrete form of IPI coding is called differential pulse position modulation (10); ref. 11 explicitly introduced a continuous form of this coding as a neuron communication hypothesis, and it receives further development in ref. 12.Results recall and further develop earlier work concerning a certain optimization that defines IPI probabilities (8). An energy audit is required to use these developments. Combining the theory with the audit leads to two outcomes: 1) The optimizing N serves as a consistency check on the audit and 2) future energy audits for individual cell types will predict N for that cell type, a test of the theory. Specialized approximations here that are not present in earlier work (9) include the assumptions that 1) all neurons of cortex are pyramidal neurons, 2) pyramidal neurons are the inputs to pyramidal neurons, 3) a neuron is under constant synaptic bombardment, and 4) a neuron’s capacitance must be charged 16 mV from reset potential to threshold to fire.Following the audit, the reader is given a perspective that may be obvious to some, but it is rarely discussed and seemingly contradicts the engineering literature (but see ref. 6). In particular, a neuron is an incredibly inefficient computational device in comparison to an idealized physical analog. It is not just a few bits per joule away from optimal predicted by the Landauer limit, but off by a huge amount, a factor of 108. The theory here resolves the efficiency issue using a modified optimization perspective. Activity-dependent communication and synaptic modification costs force upward optimal computational costs. In turn, the bit value of the computational energy expenditure is constrained to a central limit like the result: Every doubling of N can produce no more than 0.5 bits. In addition to 1) explaining the 108 excessive energy use, other results here include 2) identifying the largest “noise” source limiting computation, which is the signal itself, and 3) partitioning the relevant costs, which may help engineers redirect focus toward computation and communication costs rather than the 20-W total brain consumption as their design goal.     

Arrangement of Excitatory Synaptic Inputs on Dendrites of the Medial Superior Olive

Neurons in the medial superior olive (MSO) detect 10 µs differences in the arrival times of a sound at the two ears. Such acuity requires exquisitely precise integration of binaural synaptic inputs. There is substantial understanding of how neuronal phase locking of afferent MSO structures, and MSO membrane biophysics subserve such high precision. However, we still lack insight into how the entirety of excitatory inputs is integrated along the MSO dendrite under sound stimulation. To understand how the dendrite integrates excitatory inputs as a whole, we combined anatomic quantifications of the afferent innervation in gerbils of both sexes with computational modeling of a single cell. We present anatomic data from confocal and transmission electron microscopy showing that single afferent fibers follow a single dendrite mostly up to the soma and contact it at multiple (median 4) synaptic sites, each containing multiple independent active zones (the overall density of active zones is estimated as 1.375 per μm²). Thus, any presynaptic action potential may elicit temporally highly coordinated synaptic vesicle release at tens of active zones, thereby achieving secure transmission. Computer simulations suggest that such an anatomic arrangement boosts the amplitude and sharpens the time course of excitatory postsynaptic potentials by reducing current sinks and more efficiently recruiting subthreshold potassium channels. Both effects improve binaural coincidence detection compared with single large synapses at the soma. Our anatomic data further allow for estimation of a lower bound of 7 and an upper bound of 70 excitatory fibers per dendrite.SIGNIFICANCE STATEMENT Passive dendritic propagation attenuates the amplitude of postsynaptic potentials and widens their temporal spread. Neurons in the medial superior olive, with their large bilateral dendrites, however, can detect coincidence of binaural auditory inputs with submillisecond precision, a computation that is in stark contrast to passive dendritic processing. Here, we show that dendrites can counteract amplitude attenuation and even decrease the temporal spread of postsynaptic potentials, if active subthreshold potassium conductances are triggered in temporal coordination along the whole dendrite. Our anatomic finding that axons run in parallel to the dendrites and make multiple synaptic contacts support such coordination since incoming action potentials would depolarize the dendrite at multiple sites within a brief time interval.     

Closed‐form solutions of a fishery harvesting model with state constraint

We derive a closed‐form solution for a well‐known fisheries harvesting model with an additional state constraint. The problem is linear in the control and previous solutions appearing in the literature have been numerical in nature. The so‐called direct adjoining approach is used in our derivation and the optimal solutions turn out to be a mixture of bang‐bang and boundary arcs. Copyright © 2013 John Wiley & Sons, Ltd.     

三点法非对称回波水脂分离技术的基底动脉高分辨磁共振成像的可行性研究

目的比较不同频率编码方向FSE-T2WI和IDEAL FSE-T2WI技术的高分辨磁共振成像(high-resolution magnetic resonance imaging,HRMRI)对于基底动脉管壁显示的效果。材料与方法选择30名磁共振颅脑检查患者,采用不同频率编码方向FSE-T2WI(A/P)、FSE-T2WI(R/L)和IDEAL FSE-T2WI(A/P)3组序列行基底动脉同断面扫描,基底动脉管壁显示情况行4分法评价,并比较3组序列基底动脉的图像质量。结果基底动脉图像质量评分FSE-T2WI(A/P)、FSE-T2WI(R/L)、IDEAL FSE-T2WI(A/P)分别为81分、92分、115分。3种序列基底动脉图像质量两两比较,FSE-T2WI(A/P)和FSE-T2WI(R/L)差异具有统计学意义(Z=-3.317,P=0.001)。FSE-T2WI(R/L)和IDEAL FSE-T2WI(A/P)差异具有统计学意义(Z=-4.600,P=0.000)。FSE-T2WI(A/P)和IDEAL FSE-T2WI(A/P)差异具有统计学意义(Z=-4.540,P=0.000),IDEAL FSE-T2WI(A/P)对于基底动脉管壁结构显示效果最好。结论 IDEAL技术能显著消除颅底磁敏感伪影,使基底动脉管壁更清晰显示。     

Iterative reconstruction reduces abdominal CT dose

Objective

In medical imaging, lowering radiation dose from computed tomography scanning, without reducing diagnostic performance is a desired achievement. Iterative image reconstruction may be one tool to achieve dose reduction. This study reports the diagnostic performance using a blending of 50% statistical iterative reconstruction (ASIR) and filtered back projection reconstruction (FBP) compared to standard FBP image reconstruction at different dose levels for liver phantom examinations.

Methods

An anthropomorphic liver phantom was scanned at 250, 185, 155, 140, 120 and 100 mA s, on a 64-slice GE Lightspeed VCT scanner. All scans were reconstructed with ASIR and FBP. Four readers evaluated independently on a 5-point scale 21 images, each containing 32 test sectors. In total 672 areas were assessed. ROC analysis was used to evaluate the differences.

Results

There was a difference in AUC between the 250 mA s FBP images and the 120 and 100 mA s FBP images. ASIR reconstruction gave a significantly higher diagnostic performance compared to standard reconstruction at 100 mA s.

Conclusion

A blending of 50–90% ASIR and FBP may improve image quality of low dose CT examinations of the liver, and thus give a potential for reducing radiation dose.     

自适应统计迭代重建算法在儿童头部CT扫描中的应用

目的 探讨自适应统计迭代重建(ASiR)算法在儿童头部CT扫描中的应用价值。方法 对1个水模进行CT扫描,管电压100 kV,管电流分别为200、180、160、140、120和100 mA,ASiR比例分别设置为0、10%、20%、30%、40%、50%,采用FBP和ASiR迭代重建两种重建算法进行图像重建,比较不同条件下图像CNR、SNR和图像噪声。将80例接受头部CT扫描的患儿分为对照组(n=40)和试验组(n=40)。对照组采用管电压100 kV、管电流200 mA,FBP重建算法进行图像重建;试验组采用管电压100 kV、管电流140 mA,分别采用FBP和ASiR两种重建法进行图像重建;将重建后的图像分别记为试验FBP亚组和试验ASiR亚组;对两组中图像的CNR、SNR、图像噪声、CTDIvol、DLP、ED进行比较。结果 水模研究中,采用ASiR(30%)、管电流140 mA、ASiR算法重建图像的SNR、CNR及空气噪声值与管电流 200 mA、FBP重建图像最为接近。对照组与试验ASiR亚组图像的图像噪声、灰白质CNR和灰质SNR值差异均无统计学意义(P均>0.05);对照组和试验ASiR亚组图像的噪声、灰质SNR、灰白质CNR均优于试验FBP亚组,且差异均有统计学意义(P均<0.001)。结论 采用ASiR重建算法的头部CT扫描,既可降低患儿接受的辐射剂量,又保证了图像质量,具有较高的临床应用价值。     

Radiation Dose Reduction of Chest CT with Iterative Reconstruction in Image Space - Part I: Studies on Image Quality Using Dual Source CT

Objective

To determine whether the image quality (IQ) is improved with iterative reconstruction in image space (IRIS), and whether IRIS can be used for radiation reduction in chest CT.

Materials and Methods

Standard dose chest CT (SDCT) in 50 patients and low dose chest CT (LDCT) in another 50 patients were performed, using a dual-source CT, with 120 kVp and same reference mAs (50 mAs for SDCT and 25 mAs for LDCT) employed to both tubes by modifying a dual-energy scan mode. Full-dose data were obtained by combining the data from both tubes and half-dose data were separated from a single tube. These were reconstructed by using a filtered back projection (FBP) and IRIS: full-dose FBP (F-FBP); full-dose IRIS (F-IRIS); half-dose FBP (H-FBP) and half-dose IRIS (H-IRIS). Objective noise was measured. The subjective IQ was evaluated by radiologists for the followings: noise, contrast and sharpness of mediastinum and lung.

Results

Objective noise was significantly lower in H-IRIS than in F-FBP (p < 0.01). In both SDCT and LDCT, the IQ scores were highest in F-IRIS, followed by F-FBP, H-IRIS and H-FBP, except those for sharpness of mediastinum, which tended to be higher in FBP. When comparing CT images between the same dose and different reconstruction (F-IRIS/F-FBP and H-IRIS/H-FBP) algorithms, scores tended to be higher in IRIS than in FBP, being more distinct in half-dose images. However, despite the use of IRIS, the scores were lower in H-IRIS than in F-FBP.

Conclusion

IRIS generally helps improve the IQ, being more distinct at the reduced radiation. However, reduced radiation by half results in IQ decrease even when using IRIS in chest CT.     

自适应迭代剂量降低重建算法对脑血管体模CT图像质量的影响

目的 探讨脑血管CT成像中自适应迭代剂量降低重建(AIDR)3D算法与图像质量的关系.方法 采用ATOM 701-D型成年男性仿真人头颅模型,配置碘水混合对比剂模拟脑血管.采用320排容积CT以100 kV、300 mA、0.5 s进行颅脑单圈扫描,分别采用AIDR 3D 4个等级(off、mild、standard、strong)进行图像重建.获得轴位血管CT值及噪声、脑组织CT值及噪声、SNR和CNR.不同迭代算法等级重建对图像质量的影响采用单因素方差分析. 结果 不同AIDR 3D等级重建的血管噪声、脑组织噪声、SNR、CNR差异均有统计学意义(P均 <0.05);血管CT值及脑组织CT值差异均无统计学意义(P均 >0.05).结论 不同AIDR 3D等级降低噪声效果和提高图像质量能力不同,等级越高,图像质量越高.     

Observation-driven adaptive differential evolution and its application to accurate and smooth bronchoscope three-dimensional motion tracking

This paper proposes an observation-driven adaptive differential evolution algorithm that fuses bronchoscopic video sequences, electromagnetic sensor measurements, and computed tomography images for accurate and smooth bronchoscope three-dimensional motion tracking. Currently an electromagnetic tracker with a position sensor fixed at the bronchoscope tip is commonly used to estimate bronchoscope movements. The large tracking error from directly using sensor measurements, which may be deteriorated heavily by patient respiratory motion and the magnetic field distortion of the tracker, limits clinical applications. How to effectively use sensor measurements for precise and stable bronchoscope electromagnetic tracking remains challenging. We here exploit an observation-driven adaptive differential evolution framework to address such a challenge and boost the tracking accuracy and smoothness. In our framework, two advantageous points are distinguished from other adaptive differential evolution methods: (1) the current observation including sensor measurements and bronchoscopic video images is used in the mutation equation and the fitness computation, respectively and (2) the mutation factor and the crossover rate are determined adaptively on the basis of the current image observation. The experimental results demonstrate that our framework provides much more accurate and smooth bronchoscope tracking than the state-of-the-art methods. Our approach reduces the tracking error from 3.96 to 2.89 mm, improves the tracking smoothness from 4.08 to 1.62 mm, and increases the visual quality from 0.707 to 0.741.