DESIGN

Delivery shafts for minimally invasive devices

Image: iStockphoto

Since minimally invasive (MI) approaches to disease diagnosis, treatment and management first emerged in the early 1980s, the MI sector has witnessed meteoric growth. It is now valued in the region of US$12 billion per year.¹ The benefits associated with MI medicine have ensured its rapid adoption throughout the health care system by everyone, from physicians through to medical insurers. Benefits such as improved patient outcomes, faster patient recovery and reduced health care costs mean that the MI sector looks set to remain one of the fastest growing fields of medicine for the foreseeable future.

The introduction of new devices to facilitate the diagnosis and treatment of a more complex range of diseases is the focus of research and development by medical device manufacturers in this sector. As treatment boundaries are expanded, devices are required to travel through more challenging anatomies to reach their point of use. This means that the delivery systems that carry these devices must stay ahead of an increasingly diverse range of design requirements, while remaining cost-effective and aligning with ever decreasing project timelines.

Metal shafts offer a range of performance characteristics that has firmly established them as the platform of choice for use in the delivery systems of many mainstream MI applications. Innovations in metal shaft design have kept pace with the performance requirements demanded of them by each successive generation of new treatment device. The ability of metal shafts to evolve at a pace that matches or exceeds medical device manufacturers’ demands has ensured the continued adoption of these shafts in new application areas.

Shaft performance requirements

Table I: Desired shaft performance characteristics. (click image to enlarge)

Table I lists the desirable performance requirements that most designers will seek in any shaft that is used as part of a delivery device. In addition to these, unit cost, development cost and development timelines must be considered in conjunction with performance. These factors will usually have a major influence on the final design solution that is selected for progression.

Design elements

A common approach to achieve the optimum combination of the characteristics outlined in Table I is to apply a cut geometry of helixes or slots to the delivery shaft. Figure 1 illustrates a typical spiral cut pattern and identifies the important variables that define the cut geometry. These variables are as follows.

• Pitch: longitudinal movement when the shaft undergoes one full revolution during application of the cut pattern.
• Slot length: also known as the cut section length, this measures the length of the cut area.
• Gap: also known as the uncut portion of the pattern or an “interruption” to the cut geometry, the gap will always follow the same helical profile of the cut section; gaps act as stiffeners to the shaft and the choice of gap design will determine the torque and compression properties of the design.

Figure 1: Design elements of a cut pattern. (click image to enlarge)

In addition to the cut geometry, there are other dimensions that can be varied to change the mechanical dimensions of the shaft. These include the shaft material, the shaft circumference and the single wall thickness of the shaft. There is an almost infinite number of variations of shaft mechanical properties and cut geometries, each resulting in a different performance profile for the shaft.

Finding the balance

The challenge when designing delivery system shafts is how to balance the various performance characteristics so that they all work in harmony to achieve the desired system performance, while at the same time keeping costs and timelines within the boundaries set by the given project.

Achieving good performance in one characteristic often negatively affects other characteristics. For example, using a cut geometry to achieve excellent shaft flexibility can present a trade-off in the ability of the shaft to resist longitudinal compression. A skilled designer will be able to modify the cut pattern so that it compensates for this and thus will eventually find the balance point between the two characteristics that gives the optimum performance of both.

This is an iterative process that involves the design engineer using his/her experience to take a “best guess” at the initial cut geometry, then gradually refining the geometry by physically cutting and testing numerous iterations of the design until the desired result is achieved. This approach will eventually achieve an optimised design, however, it has several drawbacks:

• It is time consuming, often monopolising technical resources and expensive capital equipment for extended periods of time while iteration after iteration is cut and tested in search of an optimal solution. This has an affect on time to market and on development costs for the shaft and the final delivery device.
• The approach generally leads to a design solution, but it may not lead to the optimal design solution. In any given design problem there are almost infinite combinations of cut geometries, tube diameters, wall thicknesses and material types that could be tested for a solution. It is simply not possible to cut and test every combination and still remain within the cost and timeline constraints imposed by most projects. Therefore, even though a design solution is generally found, there is always the possibility that better solutions exist, but have escaped detection.
• The opportunity to increase organisational understanding of the factors underlying each design approach may be missed. A busy design engineer is focused on seeking an optimal design for a specified set of performance characteristics and once that design is reached he/she will generally be deployed to the next task or project. It is, therefore, highly unlikely that a full representation of the sensitivity of design variants at the various points of design iteration will be analysed or documented. “Tribal knowledge” is employed and the success of each new design challenge will equate with the experience of the designer.

To address these issues, a more sophisticated approach to optimising shaft design is required.

Finite element analysis

Figure 2 : Simulation of three-point-bend test (a) and column strength test (b) using FEA. Zones of increasing stress are identified sequentially by areas of green, yellow and red shading. (click image to enlarge)

Finite element analysis (FEA) is a computational technique that allows an object and its responses to external forces to be modelled in a virtual environment. FEA software allows the designer to build a virtual model that represents a design solution and then subject the model to a range of conditions that simulate the conditions the design will experience in service. Using the test results generated by the analysis, refinements can be quickly made to the model and the process repeated until an optimal solution is found. This solution then serves as an advanced starting point for generating physical prototypes and significantly reduces the number of prototypes that would have been required to reach this point in the design cycle if FEA had not been employed.

Modern FEA software allows large numbers of “what if” simulations to be run in a relatively short time. This greatly enhances a designer’s understanding of how various design parameters interact and how they influence the performance of the design as a whole, and leads to more robust designs.

An FEA based approach for optimising shaft design

A project to develop an FEA based design methodology for rapidly finding optimal solutions to delivery shaft design problems was undertaken by the authors. The goal of the project was to provide design engineers with a practical tool that would allow them to rapidly define helical or spiral cut patterns for shafts, whose application required high torque transmission combined with optimal compression and flexibility.

Phase one: FEA validation

The first phase of development of the FEA model is to undertake a correlation study. For this study, physical mechanical tests of a delivery shaft are undertaken. Equivalent tests are modelled within the virtual environment. Bench top test outputs are then validated against the FEA model outputs for the same shaft. The shaft tested in this first phase was a stainless steel metal shaft of 4 French with a defined cut pattern. Mechanical testing included the following tests.

Figure 3: (a) Measurements during column strength test (b) Torsion test apparatus. (click image to enlarge)

Bend test. The three-point-bend test measures the flexibility of a delivery shaft using a flexural three-point test. The delivery shaft is rested between two rods and a downward force is applied to bend and compress the part. Test parameters of gauge length, test speed and test span are combined and analysed to produce a measure of flexibility. Figure 2 shows the simulation of a three-point-bend test using FEA. Zones of stress graduation are clearly identifiable, as illustrated by the different colour shading.

Column strength test. During this test a shaft is gripped at two points along its length and a force is applied. As the force is initially applied, negligible displacement of the shaft occurs; this is because of the column strength of the shaft. As greater force is applied the shaft begins to bend until it reaches a point of extreme deformation. This end point is known as the kink point of the tube. The point at which the shaft changes from resisting displacement to bending is known as the critical buckling force of the shaft and is an important performance feature of the shaft during use in the field (Figure 3a).

Torsion test. This test provides a measure of the torque characteristics of a shaft by comparing input and output rotation of a shaft in a simulated anatomical path, as depicted in Figure 3b. Optimal torque response is represented as a 1:1 ratio where the degree of input rotation is matched equally by the degree of output rotation.

Figure 4 : Top: Torsion test results. The grey line indicates optimal 1:1 torque performance where input rotation equals output rotation. The blue line is the torque test result for a benchtop test and the red line is the test result for the FEA model. Bottom: Three-point-bend test results. This test measures the flexural performance of a delivery shaft. The blue line is the three-point-bend test result for a benchtop test and the red line is the test result for the FEA model. In both cases the blue and red lines closely correlate, validating FEA as a reliable predictor of shaft performance. (click image to enlarge)

Figure 4, top image, demonstrates the test results for mechanical and FEA testing of torque performance using the torsion test. Optimum torque transmission is characterised by a 1:1 ratio. The comparison of test results exhibits the correlation of benchtop test outputs and the FEA test model. The FEA model data show a small oscillatory behaviour centred about the benchtop data curve. This is due to the fact that as one end of the model shaft is rotated, the clearance between the model shaft and the model outer tube (the restricted anatomical path) causes the shaft to bend from side-to-side as it twists. Similar correlating results were received for three-point-bend tests (Figure 4, bottom image) and column strength tests, which validated the FEA approach as a reliable predictor of shaft performance.

Phase two: Building the FEA model

Figure 5 : FEA response surfaces modelled for combinations of design variables and performance characteristics (flexibility, torque and column strength). (click image to enlarge)

Phase two of the project entails building the FEA model for combinations of shaft performance characteristics (Table I) and design elements (Figure 1). Matrices of combinations generate a response surface that illustrates the mathematical relationship between the parameter to be optimised and the design variables. Individual response surfaces are developed for each of the important performance characteristics of delivery shafts; examples of these are shown in Figure 5.

This type of data can be useful in the hands of the designer and greatly reduces the time taken in selecting designs that maximise or minimise a particular response. For example, the three-point-bend test is a common approach for quantifying shaft flexibility. However, as previously stated, the test procedure in terms of manufacturing and testing prototype concept units can be expensive and time consuming. The output response of a three-point-bend test is bend stiffness and this response can be seen in Figure 5, top image. The response surface shown can be used by the design engineer to define the spiral profile in terms of the design variables: pitch, slot length and slot gap. The example shown has a fixed slot length of 175 degrees. Using the graphic output generated by the FEA model, the design engineer can easily select the appropriate values for pitch and slot gap to give the desired response. In Figure 5 the slot length is represented as a fixed value; the designer could choose to fix other design variables and generate new sets of response surfaces as two dimensional or one dimensional models.

Another interesting aspect of the response surface depicted in Figure 5, top image, is its continuity. Here, the response surface shows good linearity. This is not always the case. Figure 5, middle image, contains hills and valleys that represent areas of discontinuity. Figure 5, middle image, shows the response surface for a column strength test. This response has again been expressed as a stiffness value. As in the previous example (Figure 5, top image) the slot length variable is fixed and again the design engineer can choose appropriate values for pitch and slot gap. The discontinuities or hills are the effect of slot gap alignment. The high point marked “x” is a discontinuity caused by the gaps and the slot in the spiral profile coming into alignment along the axis of the shaft. This will have two effects on the design of the part:

• the alignment of all the slot gaps will act as a spine and give a dramatic increase in tube column strength
• the number of bending planes that the tube can accommodate will be reduced to one.

As a general rule device designers wish for catheter shafts to bend in all planes. Thus, by consulting with the response surface of Figure 5, middle image, the device designer now knows to avoid designs that fall around these hills or discontinuities without ever manufacturing a single prototype.

An FEA model generates response surfaces in hours; it can take days or weeks of physical prototype manufacturing and testing to generate enough physical data to plot similar surfaces. More significantly, as a design becomes more complex it becomes more difficult for designers to intuitively predict where these discontinuities will occur. In this situation, the use of FEA becomes an essential design tool.

Individual response surfaces are then combined to produce an approximate model, in this case a model of shaft performance, which is a widely used approach to explore the design space. This approximate model can then be used to represent the response and perform design optimisation. The FEA model is interrogated using a software interface. The software enables the design engineer to adjust eight variables to achieve optimum design. The variables are of two types: specification data on shaft sizes and specification on performance requirements. When these variables are selected, they are entered into the FEA model and the database then provides an output of the optimal design pattern.

Figure 6 : A comparison of the workflow to achieve optimised design using traditional and predictive modelling approaches. Predictive modelling eliminates the costly and time consuming process of prototype iterations. (click image to enlarge)

Figure 6 illustrates the workflow pattern to achieve optimal design using an FEA design engine. This is compared with the traditional approach to design optimisation using an iterative design approach. The predictive outcomes associated with design using an FEA engine translate into significant reductions in project cost and lead times.

Phase three: Extending FEA

The initial computational simulation project addressed the performance of stainless steel delivery shafts. The project is being expanded to include ranges of common medical grade metals, metal alloys, exotic metals, extruded, braid reinforced and composite delivery shafts. The output of this extended project will enable product designers to assess the relative performance of a range of materials solutions based on their design requirements.

Design efficiency

Designing a delivery shaft to achieve specific functional parameters can be challenging because of the interdependencies between various performance characteristics. Individual performance features cannot be designed in isolation of others. The traditional approach to achieving an optimum shaft design is mainly an iterative process. For complex or challenging designs this methodology can slow development timelines and increase product development costs. An alternative design method using FEA overcomes the challenges of traditional approaches because it equips the designer with predictive data of how a design will perform in use. Employing FEA will also allow the designer to trial various design iterations yet incur little cost. An FEA supported design methodology will help the designer find the optimum design for his/her performance requirements faster and more cost-effectively than a traditional iterative design approach.

Dermot O’Reilly is a Senior Development Engineer with the Creganna Innovation Centre. Dr Alan McKiernan is the Lead Laser Applications Specialist with the Creganna Innovation Centre. Maura Leahy* is Product Manager at Creganna Medical Devices, Parkmore West, Galway, Ireland, tel. + 353 91 735 229, e-mail: maura.leahy@creganna.com, www.creganna.com.

*To whom all correspondence should be directed.